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  Subjects -> COMPUTER SCIENCE (Total: 2153 journals)
    - ANIMATION AND SIMULATION (31 journals)
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COMPUTER SCIENCE (1258 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 25)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 31)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 9)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 17)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 15)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 5)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 9)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 12)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 16)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 8)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 6)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 20)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 6)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 6)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 8)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 9)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Hybrid Journal   (Followers: 35)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Informatica Malaysia     Open Access  
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 10)
Advanced Engineering Materials     Hybrid Journal   (Followers: 29)
Advanced Science Letters     Full-text available via subscription   (Followers: 11)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 9)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 6)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 20)
Advances in Computer Engineering     Open Access   (Followers: 4)
Advances in Computer Science : an International Journal     Open Access   (Followers: 16)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 58)
Advances in Engineering Software     Hybrid Journal   (Followers: 29)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 17)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Human-Computer Interaction     Open Access   (Followers: 21)
Advances in Materials Science     Open Access   (Followers: 15)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 52)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 6)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 9)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 5)
AI EDAM     Hybrid Journal   (Followers: 2)
Air, Soil & Water Research     Open Access   (Followers: 14)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 7)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 5)
American Journal of Computational Mathematics     Open Access   (Followers: 5)
American Journal of Information Systems     Open Access   (Followers: 6)
American Journal of Sensor Technology     Open Access   (Followers: 4)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 4)
Annals of Data Science     Hybrid Journal   (Followers: 12)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12)
Annals of Pure and Applied Logic     Open Access   (Followers: 4)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annals of West University of Timisoara - Mathematics and Computer Science     Open Access   (Followers: 1)
Annual Reviews in Control     Hybrid Journal   (Followers: 8)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 1)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 12)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Applied Clinical Informatics     Hybrid Journal   (Followers: 3)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 14)
Applied Computer Systems     Open Access   (Followers: 2)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 34)
Applied Medical Informatics     Open Access   (Followers: 12)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 17)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 7)
Applied System Innovation     Open Access  
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 6)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 156)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 8)
Artifact     Open Access   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 7)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access   (Followers: 1)
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 6)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 13)
Automation in Construction     Hybrid Journal   (Followers: 8)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Balkan Journal of Electrical and Computer Engineering     Open Access  
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Big Data and Cognitive Computing     Open Access   (Followers: 4)
Biodiversity Information Science and Standards     Open Access   (Followers: 1)
Bioinformatics     Hybrid Journal   (Followers: 345)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 36)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 49)
British Journal of Educational Technology     Hybrid Journal   (Followers: 177)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Bulletin of Social Informatics Theory and Application     Open Access  
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 1)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal   (Followers: 2)
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 15)
Capturing Intelligence     Full-text available via subscription  
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
Cell Communication and Signaling     Open Access   (Followers: 2)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access   (Followers: 4)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chaos, Solitons & Fractals : X     Open Access  
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 8)
China Communications     Full-text available via subscription   (Followers: 8)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Hybrid Journal   (Followers: 11)
Circuits and Systems     Open Access   (Followers: 15)
Clean Air Journal     Full-text available via subscription   (Followers: 1)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Clinical eHealth     Open Access  
Cluster Computing     Hybrid Journal   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 3)
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Communication Methods and Measures     Hybrid Journal   (Followers: 14)
Communication Theory     Hybrid Journal   (Followers: 25)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Computational Physics     Full-text available via subscription   (Followers: 3)
Communications in Information Science and Management Engineering     Open Access   (Followers: 4)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 4)
Communications of the ACM     Full-text available via subscription   (Followers: 52)
Communications of the Association for Information Systems     Open Access   (Followers: 16)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access   (Followers: 1)
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Composite Materials Series     Full-text available via subscription   (Followers: 8)
Computación y Sistemas     Open Access  
Computation     Open Access   (Followers: 1)
Computational and Applied Mathematics     Hybrid Journal   (Followers: 4)
Computational and Mathematical Biophysics     Open Access   (Followers: 1)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational and Structural Biotechnology Journal     Open Access   (Followers: 1)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 13)
Computational Chemistry     Open Access   (Followers: 3)
Computational Cognitive Science     Open Access   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 10)
Computational Economics     Hybrid Journal   (Followers: 10)
Computational Geosciences     Hybrid Journal   (Followers: 18)
Computational Linguistics     Open Access   (Followers: 23)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 10)
Computational Mechanics     Hybrid Journal   (Followers: 5)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 8)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access   (Followers: 1)
Computational Statistics     Hybrid Journal   (Followers: 15)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 36)
Computer     Full-text available via subscription   (Followers: 106)
Computer Aided Surgery     Open Access   (Followers: 6)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)

        1 2 3 4 5 6 7 | Last

Similar Journals
Journal Cover
Computational Biology and Chemistry
Journal Prestige (SJR): 0.538
Citation Impact (citeScore): 2
Number of Followers: 13  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1476-9271
Published by Elsevier Homepage  [3185 journals]
  • Structural, Functional, and Evolutionary Analysis of Late Embryogenesis
           Abundant Proteins (LEA) in Triticum aestivum: A Detailed Molecular Level
           Biochemistry Using In silico Approach
    • Abstract: Publication date: Available online 10 June 2019Source: Computational Biology and ChemistryAuthor(s): Shreya Bhattacharya, Shreyeshi Dhar, Arundhati Banerjee, Sujay Ray LEA (Late Embryogenesis Abundant) proteins are abundant in plants and play a crucial role in abiotic stress tolerance. In our work, we primarily focused on the variations in physiochemical properties, conserved domains, secondary structure, gene ontology and evolutionary relationships among 40 LEA proteins of Triticum aestivum (common wheat). Wheat LEA protein belongs to first 6 classes out of the 13 classes present in LEApdB, the comprehensive database for LEA proteins. Proteins belonging to each LEApdB class have structures and functions distinguished from other classes. The study found three different conserved LEA domains in Triticum aestivum. One important domain was dehydrin, present in wheat proteins of classes 1, 2 and 4, though varied in sequence level, have similar biological processes. The study also found sequence level and phylogenetic similarity between dehydrin domains of class 1 and 4, but distinct from that of LEApdB class 2. This study also demonstrated functional diversity in two class 6 proteins occurred due to many destabilizing mutations in the LEA4 domain that caused alteration of ligand binding and conformational shift from 310-helix → turn within the domain. The LEA4 domains of these proteins also showed functional similarity and evolutionary relatedness with three other proteins of genus Aegilops, denoting that these proteins in Triticum aestivum were derived from its ancestor Aegilops. The study also assigned LEApdB class 4 to an unclassified LEA protein ‘WZY2-1’ based on amino acid composition, conserved domain, motif architecture and phylogenetic relatedness with class 4 proteins. Our study has revealed a detailed analysis of LEA proteins in Triticum aestivum and can serve as a pillar for further investigations and comparative analysis of wheat LEA proteins with other cereal or plant types.Graphical Graphical abstract for this article
  • Screening and insilico analysis of deleterious nsSNPs (missense) in human
           CSF3 for their effects on Protein Structure, Stability and Function
    • Abstract: Publication date: Available online 6 June 2019Source: Computational Biology and ChemistryAuthor(s): Praveen Kumar Guttula, Gopalakrishnan Chandrasekaran, Mukesh Kumar Gupta Human granulocyte colony stimulating factor (hG-CSF), known as CSF3, plays an important role in the growth, differentiation, proliferation, survival, and activation of the granulocyte cell lineage such as neutrophils and their precursors. Functional reduction in native CSF3 protein results in reduced proliferation and activation of neutrophils and leads to neutropenia. Single nucleotide polymorphisms (SNPs) in the CSF3 gene may have deleterious effects on the CSF3 protein function. This study was undertaken to find the functional SNPs in the human CSF3 gene. Results suggest that 18.9% of all the SNPs in the dbSNP database for CSF3 gene were present in the coding region. Out of 59 non-synonymous SNPs (nsSNPs), 26 nsSNPs were predicted to be non-tolerable by SIFT whereas 18 and 7 nsSNPs were predicted as probably damaging and possibly damaging, respectively by PolyPhen. Among this 31 nsSNPs, 16 nsSNPs were identified to be potentially deleterious by PANTHER server, and 4 nsSNPs were found to be neutral by PROVEAN. SNPAnalyzer predicted 7 nsSNPs to be neutral phenotype and the remaining 24 nsSNPs to be associated with diseases. Among the predicted nsSNPs, rs144408123, rs144408123, rs145136406, rs145311241, rs373191696, rs762945096, rs763688260, rs767572172, rs775326370, rs777777864, rs777983866, rs781596455, rs139072004, rs757612684, rs772911210, rs139072004, rs746634544, rs749993200, rs763426127, rs772466210 were identified as deleterious and potentially damaging. I-Mutant analysis revealed that th 20 nsSNPs were important for protein stability of CSF3. Therefore, th 20 nsSNPs may be used for the wider population-based genetic studies and also should be taken into account while engineering the recombinant CSF3 protein for clinical use.Graphical Graphical abstract for this article
  • The architecture of the GhD7 promoter reveals the roles of GhD7 in growth,
           development and the abiotic stress response in rice
    • Abstract: Publication date: Available online 5 June 2019Source: Computational Biology and ChemistryAuthor(s): Venura Herath Grain number, plant height and heading date 7 (GhD7) is considered to be one of the key yield-related genes in the production of high-yielding and climate-ready super rice varieties. GhD7 delays the plant’s flowering under long-day conditions, which ultimately results in increased yield. Recent findings indicate that GhD7 also plays a major role in the abiotic stress response; however, the fine regulatory mechanisms controlling Ghd7 expression have yet to be uncovered. This study was carried out to explore the transcription factor binding site (TFBS) architecture of the GhD7 promoter to identify the regulatory dynamics of GhD7 transcription. The promoter sequence (-2000 to +200 base pairs from the transcription start site) was retrieved from the PlantPAN 2.0 database. Ab initio promoter analysis, DNase I hypersensitive site (DHS) analysis, and methylation analysis were carried out to identify TFBSs. The TFBS diversity among rice varieties was also assessed.In addition to the previously identified 8 cis-elements, 448 novel cis-elements were identified in the GhD7 promoter that provide binding sites for 25 transcription factor families. Furthermore, a DNase I hypersensitive site and a CpG island were also identified. The identified transcription factor families include key transcription factors involved in both development and abiotic stress responses, revealing the regulatory dynamics of GhD7. Comparative analysis of multiple GhD7 promoters identified 31 single-nucleotide polymorphisms that result in TFBS variations among rice accessions. These variations are mostly found in relation to flowering and abiotic stress responsive TFBSs on the promoter. This study supports the model that GhD7 acts as a central regulator of rice growth, development, and the abiotic stress response.Graphical abstractGraphical abstract for this article
  • Genome-Wide Analysis of Magnesium Transporter Genes in Solanum
    • Abstract: Publication date: Available online 30 May 2019Source: Computational Biology and ChemistryAuthor(s): Preetom Regon, Umakanta Chowra, Jay Prakash Awasthi, Pankaj Borgohain, Sanjib Kumar Panda Magnesium (Mg) is an important micronutrient for various physiological processes in plants. In this study, putative Magnesium Transporter (MGT) genes have been identified in Solanum lycopersicum, Solanum tuberosum, Brachypodium distachyon, Fagaria vesca, Brassica juncea and were classified into 5 distinct groups based on their sequence homology. MGT genes are very diverse and possess very low sequence identity within its family. However, the Gly-Met-Asn (GMN) signature motif is present in most of the genes which are believed to be essential for Mg2+ recognition. In S. lycopersicum, different physiological root growth pattern was observed in both Mg excess and deficient conditions. Quantitative RT-PCR gene expression study shows that most of the SlMGT genes were upregulated in response to Mg deficient condition.Graphical abstractGraphical abstract for this articleClassification of MGT genes based on their sequence homology.
  • Structural insights of a cellobiose dehydrogenase enzyme from the
           basidiomycetes fungus Termitomyces clypeatus
    • Abstract: Publication date: Available online 30 May 2019Source: Computational Biology and ChemistryAuthor(s): Sanchita Banerjee, Ankit Roy, M S Madhusudhan, Hridoy R Bairagya, Amit Roy Filamentous fungi secrete various oxidative enzymes to degrade the glycosidic bonds of polysaccharides. Cellobiose dehydrogenase (CDH) (E.C. is one of the important lignocellulose degrading enzymes produced by various filamentous fungi. It contains two stereo specific ligand binding domains, cytochrome and dehydrogenase - one for heme and the other for flavin adenine dinucleotide (FAD) respectively. The enzyme is of commercial importance for its use in amperometric biosensor, biofuel production, lactose determination in food, bioremediation etc. Termitomyces clypeatus, an edible fungus belonging to the basidiomycetes group, is a good producer of CDH. In this paper we have analyzed the structural properties of this enzyme from T. clypeatus and identified a distinct carbohydrate binding module (CBM) which is not present in most fungi belonging to the basidiomycetes group. In addition, the dehydrogenase domain of T. clypeatus CDH exhibited the absence of cellulose binding residues which is in contrast to the dehydrogenase domains of CDH of other basidiomycetes. Sequence analysis of cytochrome domain showed that the important residues of this domain were conserved like in other fungal CDHs. Phylogenetic tree, constructed using basidiomycetes and ascomycetes CDH sequences, has shown that very surprisingly the CDH from T. clypeatus, which is classified as a basidiomycetes fungus, is clustered with the ascomycetes group. A homology model of this protein has been constructed using the CDH enzyme of ascomycetes fungus Myricoccum thermophilum as a template since it has been found to be the best match sequence with T. clypeatus CDH. We also have modelled the protein with its substrate, cellobiose, which has helped us to identify the substrate interacting residues (L354, P606, T629, R631, Y649, N732, H733 and N781) localized within its dehydrogenase domain. Our computational investigation revealed for the first time the presence of all three domains - cytochrome, dehydrogenase and CBM - in the CDH of T. clypeatus, a basidiomycetes fungus. In addition to discovering the unique structural attributes of this enzyme from T. clypeatus, our study also discusses the possible phylogenetic status of this fungus.Graphical abstractGraphical abstract for this article
  • Structural prediction, whole exome sequencing and molecular dynamics
           simulation confirms p.G118D somatic mutation of PIK3CA as functionally
           important in breast cancer patients
    • Abstract: Publication date: Available online 29 May 2019Source: Computational Biology and ChemistryAuthor(s): Tariq Ahmad Masoodi, Noor Ahmad Shaik, Syed Burhan, Qurratulain Hasan, Gowhar Shafi, Venkateswar Rao Talluri To understand the functional importance of PIK3CA somatic mutations, whole exome sequencing data, molecular dynamics simulation techniques in combination with computational prediction algorithms such as SIFT, PolyPhen, Provean and CADD were employed. Twenty out of eighty missense somatic mutations in PIK3CA gene were found to be pathogenic by all the four algorithms. Most recurrent mutations from these were known hotspot PIK3CA mutations with known clinical significance like p.E545 K, p.E545A, p.E545 G and p.C420R. A novel missense mutation p.G118D was found to be recurrently mutated in 5 cases. This mutation was found in one of the patients for whom exome sequencing data was available and therefore underwent molecular dynamics simulation. By molecular dynamics approach, we have shown that p.G118D mutation deviated more from the native structure which was supported by the decrease in the number of hydrogen bonds, difference in hydrogen bond distance and angle, difference in root mean square deviation between the native and the mutant structures.Graphical abstractGraphical abstract for this article
  • Alignment-Independent 3D-QSAR and Molecular Docking Studies of
           Tacrine−4-oxo-4H-Chromene Hybrids as anti-Alzheimer's Agents
    • Abstract: Publication date: Available online 28 May 2019Source: Computational Biology and ChemistryAuthor(s): Elham Manouchehrizadeh, Azar Mostoufi, Elham Tahanpesar, Masood Fereidoonnezhad A series of novel tacrine derivatives as multifunctional agents with potential inhibitory effects on both acetylcholinesterase(AChE) and butyrylcholinesterase (BuChE) enzymes for the treatment of Alzheimer's disease(AD), were applied to alignment independent 3D-QSAR methods using Pentacle software. In this studies, GRID-independent molecular descriptors (GRIND) analysis have been applied to characterize important interactions between enzymes and the studied compounds. Two H-bond acceptor groups as well as hydrophobic properties of tacrine rings for AChE and two H-bond acceptor on the carbonyl group of chromene and NH of amid group for BuChE, with positive effects on their inhibitory potency have been identified. The obtained 3D-QSAR models have been analyzed and validated. The statistical quality of the QSAR model for AChE, r2 = 0.87, q2 = 0.56 and for BuChE, r2 = 0.96, q2 = 0.70 was resulted. Using these models, novel structures have been designed and pIC50 of them were predicted. Molecular docking studies were also conducted on AChE (1ACJ) and BuChE (4BDS) and promising results in good agreement with 3D-QSAR studies were obtained.Graphical abstractGraphical abstract for this article
  • Structural and functional studies of 1-phenylcyclopentane carboxylic acid
           a potential anti-cancer drug by spectroscopic, quantum chemical and
           molecular docking methods
    • Abstract: Publication date: Available online 28 May 2019Source: Computational Biology and ChemistryAuthor(s): BR. Raajaraman, N.R. Sheela, S. Muthu The 1-phenylcyclopentane carboxylic acid (1PCPCA) molecule have been characterized by quantum chemical theory and experimental vibrational spectroscopic methods. The density functional theory (DFT) approach is followed using the method B3LYP and 6-311++G(d,p) basis set. Using potential energy distribution, all the assignments of the basic vibrational modes were calculated. Natural bond orbital (NBO) and atoms in molecules (AIM) topological studies applied to get the intermolecular interactions of the compound. 1H and 13C chemical shift of NMR were estimated on the molecule and also compared with the experimental spectra. In order to find the band gap, the time-dependent (TD-DFT) method is used to get the higher order energy levels properties and also compared with experimental data of UV-Vis spectrum. From the analysis of various spectroscopic studies, there is a good relationship between the experimental and theoretical values obtained. Quantum characters, bio-active nature and reactive areas of the molecule are revealed by Fukui function, molecular electrostatic potential (MEP) and Hirshfeld surface studies. The human enzyme steroidogenic types and their protein targets were tested with this molecule by molecular docking.Graphical abstractGraphical abstract for this article
  • THPep: a machine learning-based approach for predicting tumor homing
    • Abstract: Publication date: Available online 24 May 2019Source: Computational Biology and ChemistryAuthor(s): Watshara Shoombuatong, Nalini Schaduangrat, Reny Pratiwi, Chanin Nantasenamat In the present era, a major drawback of current anti-cancer drugs is the lack of satisfactory specificity towards tumor cells. Despite the presence of several therapies against cancer, tumor homing peptides are gaining importance as therapeutic agents. In this regard, the huge number of therapeutic peptides generated in recent years, demands the need to develop an effective and interpretable computational model for rapidly, effectively and automatically predicting tumor homing peptides. Therefore, a sequence-based approach referred herein as THPep has been developed to predict and analyze tumor homing peptides by using an interpretable random forest classifier in concomitant with amino acid composition, dipeptide composition and pseudo amino acid composition. An overall accuracy and Matthews correlation coefficient of 90.13% and 0.76, respectively, were achieved from the independent test set on an objective benchmark dataset. Upon comparison, it was found that THPep was superior to the existing method and holds high potential as a useful tool for predicting tumor homing peptides. For the convenience of experimental scientists, a web server for this proposed method is provided publicly at Graphical abstract for this article
  • Drug Promiscuity: Exploring the polypharmacology potential of 1, 3,
           6-trisubstituted 1, 4-diazepane-7-ones as an Inhibitor of the ‘god
           father’ of Immune Checkpoint
    • Abstract: Publication date: Available online 22 May 2019Source: Computational Biology and ChemistryAuthor(s): Opeyemi S. Soremekun, Fisayo A. Olotu, Clement Agoni, Mahmoud E.S. Soliman High production cost, instability, low tumor penetration are some of the shortcomings that have characterized and undermined the use of antibodies as a target for Cytotoxic T-lymphocytes associated protein 4 (CTLA-4). Design and discovery of small molecule inhibitors have therefore become a sine qua non in targeting immune proteins implicated in immune disorders. In this study, we utilized a drug repositioning approach to explore the characteristic feature of unrelated proteins to have similar binding sites and the promiscuity of drugs to repurpose an existing drug to target CTLA-4. CTLA-4 and Kallikrein-7 were found to have similar binding sites, we therefore used 1, 3, 6-trisubstituted 1, 4-diazepane-7-ones (TDSO) which is an inhibitor of Kallikrein-7 as our lead compound. High throughput screening using TDSO as a lead compound resulted in 9 hits with ZINC04515726 and ZINC08985213 having the highest binding score. We went ahead to investigate the interaction of these compounds with CTLA-4 by conducting a molecular dynamic simulation. Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) estimations revealed that TDSO had the highest binding energy value of -28.51Kcal/mol, with ZINC04515726 and ZINC08985213 having -23.76Kcal/mol and -21.03Kcal/mol respectively. The per-residue decomposition highlighted Tyr24, Ala25, Gly28, Ala30, Tyr53 and Asn72 as having significantly high electrostatic energy contributions and the main contributing residues to the binding of TDSO, ZINC04515726 and ZINC08985213 to Cytotoxic T lymphocytes CTLA-4. Summarily, from the results gathered, we proposed that TDSO can be an effective immune check point small molecule inhibitor against the suppression of T-cell activation, proliferation, and tumor cell eradication.Graphical Graphical abstract for this article
  • HIVCoR: a sequence-based tool for predicting HIV-1 CRF01_AE coreceptor
    • Abstract: Publication date: Available online 20 May 2019Source: Computational Biology and ChemistryAuthor(s): Sayamon Hongjaisee, Chanin Nantasenamat, Tanawan Samleerat, Watshara Shoombuatong Determination of HIV-1 coreceptor usage is strongly recommended before starting the coreceptor-specific inhibitors for HIV treatment. Currently, the genotypic assays are the most interesting tools due to they are more feasible than phenotypic assays. However, most of prediction models were developed and validated by data set of HIV-1 subtype B and C. The present study aims to develop a powerful and reliable model to accurately predict HIV-1 coreceptor usage for CRF01_AE subtype called HIVCoR. HIVCoR utilized random forest and support vector machine as the prediction model, together with amino acid compositions, pseudo amino acid compositions and relative synonymous codon usage frequencies as the input feature. The overall success rate of 93.79% was achieved from the external validation test on the objective benchmark dataset. Comparison results indicated that HIVCoR was superior to other bioinformatics tools and genotypic predictors. For the convenience of experimental scientists, a user-friendly webserver has been established at abstractGraphical abstract for this article
  • Virtual screening of p53 mutants reveals Y220S as an additional rescue
           drug target for PhiKan083 with higher binding characteristics
    • Abstract: Publication date: Available online 16 May 2019Source: Computational Biology and ChemistryAuthor(s): Vaijayanthi Raghavan, Maulishree Agrahari, Dhananjaya Kale Gowda Pharmacological intervention to reactivate p53 in human tumors holds great promise for cancer patients. A number of small molecules that reactivate p53 mutants that are either specific to certain mutation or more broadly on various mutants of p53 are discovered by rational design and screening methods. One of the most remarkable among small molecules for the rescue of specific mutant p53, Y220C is PhiKan083 (1-(9-ethyl-9H-carbazole-3-yl)-N-methylmethanamine) that have been demonstrated effective in advanced pre-clinical trials. Our attempt here is to identify additional targets of p53 mutants for rescue drugs and provide insight into the molecular level interactions of the drug with the mutant target. In this study PhiKan083 also known as PK083 is investigated, screened and validated on 28 different mutants of p53 using FlexX. Interaction of PhiKan08 with Y220C is found to be largely hydrophobic and a crucial single H-bond interaction with Asp228 in addition to few electrostatic interactions. Our study identified Y220S mutant as an additional target for PK083 as it shows a similar interaction pattern. Besides this, Docking and MD simulation studies, showed that Y220S binds to PK083 at higher efficiency as a result of improved steric and hydrophobic environment in the binding cavity in comparison with known mutant target, Y220C. Further, we point out that structure guided optimization of PhiKan08 can lead to an improved drug that can interact favourably with yet another mutant, Y220 N. In addition, this study revealed that Y220H and other mutants including native p53 does not provide any favourable interaction with PhiKan08 which is in accord with the experimental findings. These findings can facilitate the selection of patients for clinical studies and cancer survival analysis.Graphical abstractGraphical abstract for this article
  • Inference of Gene Regulatory Networks With Multi-Objective Cellular
           Genetic Algorithm
    • Abstract: Publication date: Available online 13 May 2019Source: Computational Biology and ChemistryAuthor(s): José García-Nieto, Antonio J. Nebro, José F. Aldana-Montes Reverse engineering of biochemical networks remains an important open challenge in computational systems biology. The goal of model inference is to, based on time-series gene expression data, obtain the sparse topological structure and parameters that quantitatively understand and reproduce the dynamics of biological systems. In this paper, we propose a multi-objective approach for the inference of S-System structures for Gene Regulatory Networks (GRNs) based on Pareto dominance and Pareto optimality theoretical concepts instead of the conventional single-objective evaluation of Mean Squared Error (MSE). Our motivation is that, using a multi-objective formulation for the GRN, it is possible to optimize the sparse topology of a given GRN as well as the kinetic order and rate constant parameters in a decoupled S-System, yet avoiding the use of additional penalty weights. A flexible and robust Multi-Objective Cellular Evolutionary Algorithm is adapted to perform the tasks of parameter learning and network topology inference for the proposed approach. The resulting software, called MONET, is evaluated on real-based academic and synthetic time-series of gene expression taken from the DREAM3 challenge and the IRMA in vivo datasets. The ability to reproduce biological behavior and robustness to noise is assessed and compared. The results obtained are competitive and indicate that the proposed approach offers advantages over previously used methods. In addition, MONET is able to provide experts with a set of trade-off solutions involving GRNs with different typologies and MSEs.Graphical abstractGraphical abstract for this article
  • Rational in silico design of aptamers for organophosphates based on the
           example of paraoxon
    • Abstract: Publication date: Available online 13 May 2019Source: Computational Biology and ChemistryAuthor(s): Daria A. Belinskaia, Pavel V. Avdonin, Richard O. Jenkins, Nikolay V. Goncharov Poisoning by organophosphates (OPs) takes one of the leading places in the total number of exotoxicoses. Detoxication of OPs at the first stage of the poison entering the body could be achieved with the help of DNA- or RNA-aptamers, which are able to bind poisons in the bloodstream. The aim of the research was to develop an approach to rational in silico design of aptamers for OPs based on the example of paraoxon. From the published sequence of an aptamer binding organophosphorus pesticides, its three-dimensional model has been constructed. The most probable binding site for paraoxon was determined by molecular docking and molecular dynamics (MD) methods. Then the nucleotides of the binding site were mutated consequently and the values of free binding energy have been calculated using MD trajectories and MM-PBSA approach. On the basis of the energy values, two sequences that bind paraoxon most efficiently have been selected. The value of free binding energy of paraoxon with peripheral anionic site of acetylcholinesterase (AChE) has been calculated as well. It has been revealed that the aptamers found bind paraoxon more effectively than AChE. The peculiarities of paraoxon interaction with the aptamers nucleotides have been analyzed. The possibility of improving in silico approach for aptamer selection is discussed.Graphical abstractGraphical abstract for this article
  • Molecular mechanism by which residues at position 481 and 546 of measles
           virus hemagglutinin protein define CD46 receptor binding using a molecular
           docking approach
    • Abstract: Publication date: Available online 11 May 2019Source: Computational Biology and ChemistryAuthor(s): Sara Sajjadi, Amruta Shirode, Sunil R. Vaidya, Sarah S. Cherian The hemagglutinin (H) protein of measles viruses (MeV) mediates binding to the cellular receptors, CD46,human signaling lymphocyte activation molecule and nectin-4. Vaccine strains primarily contain H-proteins possessing MeV-H: Y481 and can utilize CD46. Reports suggest that a single amino acid change in MeV-H at position 481 in wild type strains renders them inefficient in utilizing CD46. The in-depth molecular mechanism by which substitutions at 481 and another reported critical residue position 546 affects CD46 binding affinity however remains elusive. We used molecular docking studies of CD46 with MeV-H possessing Y481 N/D to understand the in-depth molecular mechanism involved. It was found that loss in either of the hydrogen bond (H-bond) contacts (MeV-H:481–CD46:65, MeV-H:546–CD46:63) in the central contact region prevented efficient CD46 binding. Y481 N could form the specific H-bond, while G546S H-bond could be formed only in conjunction with Y481, revealing the significance of these residues in determining CD46 receptor binding potential. Elucidating the underlying molecular mechanism of receptor usage by the MeV has implications to understanding cellular tropism, viral pathogenesis and therapy.Graphical abstractGraphical abstract for this article
  • Synthesis and Biological Evaluation of Indole-2-carbohydrazides and
           Thiazolidinyl-indole-2-carboxamides as Potent Tubulin Polymerization
    • Abstract: Publication date: Available online 11 May 2019Source: Computational Biology and ChemistryAuthor(s): Fusun Kazan, Z. Begum Yagci, Ruoli Bai, Elif Ozkirimli, Ernest Hamel, Sumru Ozkirimli A new series of N’-(substituted phenyl)-5-chloro/iodo-3-phenyl-1H-indole-2-carbohydrazide (5, 6) and N-[2-(substituted phenyl)-4-oxo-1,3-thiazolidin-3-yl]-5-iodo/chloro-3-phenyl-1H-indole-2-carboxamide (7, 8) derivatives were synthesized and evaluated for their anticancer properties. Compounds 5a and 6b, selected as prototypes by the National Cancer Institute for screening against the full panel of 60 human tumor cell lines at a minimum of five concentrations at 10-fold dilutions, demonstrated remarkable antiproliferative activity against leukemia, non-small cell lung cancer, colon cancer, central nervous system (CNS) cancer, melanoma, ovarian cancer, renal cancer, and breast cancer (MCF-7) cell lines with GI50 values < 0.4 µM. A subset of the compounds was then tested for their potential to inhibit tubulin polymerization. Compounds 6f and 6 g showed significant cytotoxicity at the nM level on MCF-7 cells and exhibited significant inhibitory activity on tubulin assembly and colchicine binding at about the same level as combretastatin A-4. Finally, docking calculations were performed to identify the binding mode of these compounds. Group 5 and 6 compounds interacted with the colchicine binding site through hydrophobic interactions similar to those of colchicine. These compounds with antiproliferative activity at high nanomolar concentration can serve as scaffolds for the design of novel microtubule targeting agents.Graphical abstractGraphical abstract for this article
  • CoMFA, CoMSIA, Topomer CoMFA, HQSAR, Molecular Docking and Molecular
           Dynamics Simulations Study of Triazine Morpholino Derivatives as mTOR
           Inhibitors for the Treatment of Breast Cancer
    • Abstract: Publication date: Available online 3 May 2019Source: Computational Biology and ChemistryAuthor(s): Dhara M. Chhatbar, Udit J. Chaube, Vivek K. Vyas, Hardik G. Bhatt mTOR has become a promising target for many types of cancer like breast, lung and renal cell carcinoma. CoMFA, CoMSIA, Topomer CoMFA and HQSAR were performed on the series of 39 triazine morpholino derivatives. CoMFA analysis showed q2 value of 0.735, r2cv value of 0.722 and r2pred value of 0.769. CoMSIA analysis (SEHD) showed q2 value of 0.761, r2cv value of 0.775 and r2pred value of 0.651. Topomer CoMFA analysis showed q2 value of 0.693, r2 (conventional correlation coefficient) value of 0.940 and r2pred value of 0.720. HQSAR analysis showedq2,r2and r2pred values of 0.694, 0.920 and 0.750, respectively. HQSAR analysis with the combination of atomic number (A), bond type (B) and atomic connections showed q2 and r2 values of 0.655 and 0.891, respectively. Contour maps from all studies provided significant insights. Molecular docking studies with molecular dynamics simulations were carried out on the highly potent compound 36. Furthermore, four acridine derivatives were designed and docking results of these designed compounds showed the same interactions as that of the standard PI-103 which proved the efficiency of 3D-QSAR and MD/MS study. In future, this study might be useful prior to synthesis for the designing of novel mTOR inhibitors.Graphical abstractGraphical abstract for this article
  • Prediction and molecular insights into fungal adhesins and adhesin like
    • Abstract: Publication date: Available online 3 May 2019Source: Computational Biology and ChemistryAuthor(s): Abhigyan Nath Adhesion is the foremost step in pathogenesis and biofilm formation and is facilitated by a special class of cell wall proteins known as adhesins. Formation of biofilms in catheters and other medical devices subsequently leads to infections. As compared to bacterial adhesins, there is relatively less work for the characterization and identification of fungal adhesins. Understanding the sequence characterization of fungal adhesins may facilitate a better understanding of its role in pathogenesis. Experimental methods for investigation and characterization of fungal adhesins are labor intensive and expensive. Therefore, there is a need for fast and efficient computational methods for the identification and characterization of fungal adhesins. The aim of the current study is twofold: (i) to develop an accurate predictor for fungal adhesins, (ii) to sieve out the prominent molecular signatures present in fungal adhesins. Of the many supervised learning algorithms implemented in the current study, voting ensembles resulted in enhanced prediction accuracy. The best voting-ensemble consisting of three support vector machines with three different kernels (PolyK, RBF, PuK) achieved an accuracy of 94.9% on leave one out cross validation and 98.0% accuracy on blind testing set. A preference/avoidance list of molecular features as well as human interpretable rules are also extracted giving insights into the general sequence features of fungal adhesins. Fungal adhesins are characterized by high Threonine and Cysteine and avoidance for Phenylalanine and Methionine. They also have avoidance for average hydrophilicity. The current analysis possibly will facilitate the understanding of the mechanism of fungal adhesin function which may further help in designing methods for restricting adhesin mediated pathogenesis.Graphical abstractGraphical abstract for this article
  • Structure based pharmacophore study to identify possible natural selective
           PARP-1 trapper as anti-cancer agent
    • Abstract: Publication date: Available online 2 May 2019Source: Computational Biology and ChemistryAuthor(s): Chandan Kumar, Lakshmi P.T.V., Annamalai Arunachalam Inhibition of poly(ADP-ribose) polymerase-1 (PARP-1) has turned out an innovative approach for cancer therapy due to its involvement in DNA repair pathways. Although several potent PARP-1 inhibitors have been identified, they exhibit high toxicity, resistivity and diverse pharmacological profile in clinical trials, which necessitate for extensive investigation and development of selective inhibitors. Therefore, the study aimed to identify selective natural PARP-1 inhibitors to reduce toxicity and resistivity with high potency. Accordingly, the combined approach of structure-based pharmacophore and molecular docking study was performed. Hence, the two hits (SN00167272 and STOCK1N-92279) were identified to have all the pharmacophoric features that showed interaction with key residues (Gly863, Ser904, Tyr896, and Tyr907) and least conserved residues (Tyr889 and Asp766). Additionally, these inhibitors represented interactions with unique selective residues (Asp756, Val762, Glu763 and Val886) and exhibited strong interaction with PARP-1 through binding free energy and molecular dynamics study. Hence, the identified hits could further considered for experimental investigations as they may reduce off-target and resistivity of currently available inhibitors and developed as potential anti-cancer agents in the future. Also, the study provides a specific structural insight which could further help to design selective and potent PARP-1 inhibitors.Graphical abstractGraphical abstract for this article
  • Genome-wide Analysis of the MADS-Box Gene: Family in Watermelon
    • Abstract: Publication date: Available online 2 May 2019Source: Computational Biology and ChemistryAuthor(s): Ping Wang, Songbo Wang, Yong Chen, Xiaomin Xu, Xuanmin Guang, Youhua Zhang MADS-box genes comprise a family of transcription factors that function in the growth and development of plants. To obtain insights into their evolution in watermelon (Citrullus lanatus), we carried out a genome-wide analysis and identified 39 MADS-box genes. These genes were classified into MIKCc (25), MIKC*(3), Mα (5), Mβ (3), and Mγ (3) clades according to their phylogenetic relationship with Arabidopsis thaliana and Cucumis sativus; moreover, these 25 genes in the MIKC clade could be classified into 13 subfamilies, and the Flowering Locus C (FLC) subfamily is absent in watermelon. Analysis of the conserved gene motifs showed similar motifs among clades. Continuing chromosomal localizations analysis indicated that MADS-box genes were distributed across 11 chromosomes in watermelon, and these genes were conditioned to be differentially expressed during plant growth and development. This research provides information that will aid further investigations into the evolution of the MADS-box gene family in plants.Graphical abstractGraphical abstract for this article
  • Identification, synthesis and evaluation of CSF1R inhibitors using
           fragment based drug design
    • Abstract: Publication date: Available online 2 May 2019Source: Computational Biology and ChemistryAuthor(s): Pavan Kumar Machiraju, Poornachandra Yedla, Satya Prakash Gubbala, Taher Bohari, Jaleel KV Abdul, Xu Shili, Rahul Patel, Ch. Venkata Ramana Reddy, Kiran Boppana, Sarma ARP Jagarlapudi, Nouri Neamati, Riyaz Syed, Ramars Amanchy Colony-stimulating factor 1 receptor is a type III receptor protein tyrosine kinase belonging to PDGFR family. CSF1R signaling is essential for differentiation, proliferation and survival of macrophages. Aberrant expression of CSF1R appears to be an attractive target in several cancer types. Higher expression of CSF1R ligands correlates to tumor progression. CSF1R inhibitors have been shown to suppress cancers. We have attempted an in silico fragment derived drug discovery approach by screening ˜25000 in-house compounds as potential CSF1R inhibitors. Using FBDD approach we have identified six diverse fragments that exhibit affinity towards hinge region of CSF1R. Some of the fragments 5-nitroindole and 7-azaindole and their derivatives were synthesized for further evaluation. The in silico and in vitro enzyme activity studies reveal moderate inhibition of CSF1R kinase activity by 5-nitroindole and good inhibition by 7-azaindole fragments. Bio and chemiinformatics studies have shown that 7-azaindole compounds have better membrane permeability and enzyme inhibition properties. Molecular docking studies show that the amino acid residues 664-666 in the hinge region of the cytosolic domain of CSF1R to be the preferred region of binding for nitroindole and azaindole derivatives. Further optimization and biological analysis would identify these fragments as potential and promising leads as CSF1R inhibitors.Graphical abstractGraphical abstract for this article
  • Phosphorylation Mapping of Laminin α1-chain: Kinases in association
           with active sites
    • Abstract: Publication date: Available online 2 May 2019Source: Computational Biology and ChemistryAuthor(s): Panagiota Angeliki Galliou, Kleio-Maria Verrou, George Koliakos Laminin­111 is a trimeric glycoprotein of the extracellular matrix (ECM) that holds a significant role in cell adhesion, migration and differentiation. Laminin­111 is the most studied laminin isoform, composed of three chains; α1, β1 and γ1. Phosphorylation is the most common eukaryotic post ­ translational modification and has regulatory effect on protein function. Using bioinformatic tools we computationally predicted all the possible phosphorylation sites on human laminin α1-chain sequence (LAMA1) according to kinases binding motifs. Thus, we predicted, for the first time, the possibly responsible kinases for fifteen of the nineteen already published experimentally observed phosphorylated residues in LAMA1. Searching the literature extensively, we recorded all the known functional sites (active sites) in LAMA1. We combined the experimentally observed and predicted phosphorylated residues as well as the active sites in LAMA1, generating an analytic phosphorylation map of human laminin α1-chain, which is useful for further analysis. Our results indicated fourteen kinases that might be important for the phosphorylation of human laminin α1-chain, out of which three kinases with reported ecto-phosphorylation activity (PKA, PKC and CKII) were suggested to have a more significant role. Six cancer associated-active sites were correlated with kinases, three out which were correlated with only the above ecto ­ kinases.Graphical abstractGraphical abstract for this article
  • Systematic Profiling and Evaluation of Structure-based Kinase–Inhibitor
           Interactome in Cervical Cancer by Integrating In Silico Analyses and In
           Vitro Assays at Molecular and Cellular Levels
    • Abstract: Publication date: Available online 1 May 2019Source: Computational Biology and ChemistryAuthor(s): Li-Xia Zhu, Qin Liu, Ya-Fang Hua, Ning Yang, Xue-Gang Zhang, Xi Ding Various protein kinases are implicated in the pathogenesis of human cervical cancer and many kinase inhibitors have been used to regulate the activity of protein kinases involved in the disease signaling networks. In the present study, a systematic kinase–inhibitor interactome is created for various small-molecule inhibitors across diverse cervical cancer-related kinases by using ontology enrichment, molecular docking, dynamics simulation and energetics analysis. The interactome profile is examined in detail with heatmap analysis and heuristic clustering to derive promising inhibitors that are highly potential to target the kinome of human cervical cancer in a multi-target manner. A number of hit and unhit inhibitors are selected and their cell-suppressing effects are tested against human cervical carcinoma HeLa, from which several inhibitor compounds with high cytotoxicity are successfully identified. A further kinase assay confirms that these inhibitors can generally target their noncognate kinases HER3 and BRaf in cervical cancer with a high or moderate activity; the activity profile are comparable with or even better than that of cognate kinases inhibitors, with IC50 values ranging between 4.8 and 340.6 nM for HER3 and between 37.2 and 638.2 nM for BRaf. This work would help to identify those unexpected kinase–inhibitor interactions in human cervical cancer and to develop new and efficient therapeutic strategy combating the disease.Graphical abstractGraphical abstract for this article
  • DFT-based QSAR modelling of selectivity and inhibitory activity of
           coumarins and sulfocoumarins against tumor-associated carbonic anhydrase
           isoform IX
    • Abstract: Publication date: Available online 30 April 2019Source: Computational Biology and ChemistryAuthor(s): Erol Eroğlu In this study, we present four multilinear regression quantitative structure-activity relationship (QSAR) models, the first of which tackles the experimental CA IX isozyme inhibitory activities of a mixed set of 59 compounds (coumarins and sulfocoumarins), while the second and the third ones are local models for coumarins (42 compounds) and sulfocoumarins (17 compounds) for the same endpoint respectively. The fourth model deals with the selective inhibitory activity of 29 compounds on CA II and CA IX isozymes which was defined as differences of inhibition constants between CA II and CA IX (ΔpKi=pKi_CAII- pKi_CAIX) in logarithmic scale.All presented models are statistically significant, robust and internally and externally validated. Most of the descriptors involved in these models are DFT-based physically-interpretable ones. Existence of the eigenvalues of chemical reactivity-related orbitals such as HOMO-1 Energy and LUMO Energy in the models allow us to speculate that the hydrolyzing reactions of the coumarins and sulfocoumarins in the active pocket of CA isozymes play a critical role in (or modulate) the binding free energy of the ligand-isozyme complexes. We believe that presented models may be used to design new virtual analogues of existing compounds with desired activity and selectivity towards CA IX or CA II.Graphical abstractGraphical abstract for this article
  • Evaluation of potential inhibitors of squalene synthase based on virtual
           screening and in vitro studies
    • Abstract: Publication date: Available online 29 April 2019Source: Computational Biology and ChemistryAuthor(s): Han Huang, Chen-Liang Chu, Lin Chen, Dong Shui Squalene synthase (SQS) is a potential target for hyperlipidemia treatment. To identify novel chemical scaffolds of SQS inhibitors, we generated 3D-QSAR pharmacophore models using HypoGen. The best quantitative pharmacophore model, Hypo 1, was selected for virtual screening using two chemical databases, Specs and Traditional Chinese Medicine database (TCM). The best-mapped hit compounds were then subjected to filtering by Lipinski’s rule of five and docking studies to refine the hits. Finally, five compounds were selected from the top-ranked hit compounds for SQS inhibitory assay in vitro. Three of these compounds could inhibit SQS in vitro, and should be further evaluated pre-clinically as a treatment for hyperlipidemia.Graphical abstractGraphical abstract for this article
  • Gene Selection of Rat Hepatocyte Proliferation using Adaptive Sparse Group
           Lasso with Weighted Gene Co-expression Network Analysis
    • Abstract: Publication date: Available online 26 April 2019Source: Computational Biology and ChemistryAuthor(s): Juntao Li, Yadi Wang, Huimin Xiao, Cunshuan Xu Grouped gene selection is the most important task for analyzing the microarray data of rat liver regeneration. Many existing gene selection methods can not outstand the interactions among the selected genes. In the process of rat liver regeneration, one of the most important events involved in many biological processes is the proliferation of rat hepatocytes, so it can be used as a measure of the effectiveness of the method. Here we proposed an adaptive sparse group lasso to select genes in groups for rat hepatocyte proliferation. The weighted gene co-expression networks analysis was used to identify modules corresponding to biological pathways, based on which a strategy of dividing genes into groups was proposed. A strategy of adaptive gene selection was also presented by assessing the gene significance and introducing the adaptive lasso penalty. Moreover, an improved blockwise descent algorithm was proposed. Experimental results demonstrated that the proposed method can improve the classification accuracy, and select less number of significant genes which act jointly in groups and have direct or indirect effects on rat hepatocyte proliferation. The effectiveness of the method was verified by the method of rat hepatocyte proliferation.Graphical abstractGraphical abstract for this article
  • Pharmacophore modeling coupled with molecular dynamic simulation approach
           to identify new leads for meprin-β metalloprotease
    • Abstract: Publication date: Available online 26 April 2019Source: Computational Biology and ChemistryAuthor(s): Ankur Chaudhuri, Nandagopal Hudait, Sibani Sen Chakraborty Human meprin beta metalloprotease, a small subgroup of the astacin family, is a potent drug target for the treatment of several disorders such as fibrosis, neurodegenerative disease in particular Alzheimer and inflammatory bowel diseases. In this study, a ligand-based pharmacophore approach has been used for the selection of potentially active compounds to understand the inhibitory activities of meprin-β by using the sulfonamide scaffold based inhibitors. Using this dataset, a pharmacophore model (Hypo1) was selected on the basis of a highest correlation coefficient (0.959), lowest total cost (105.89) and lowest root mean square deviation (1.31 Å) values. All the pharmacophore hypotheses generated from the candidate inhibitors comprised four features: two hydrogen-bond acceptor, one hydrogen-bond donor and one zinc binder feature. The best validated pharmacophore model (Hypo1) was used for virtual screening of compounds from several databases. The selective hit compounds were filtered by drug likeness property, acceptable ADMET profile, molecular docking and DFT study. Molecular dynamic simulations with the final 10 hit compounds revealed that a large number of non-covalent interactions were formed with the active site and specificity sub-pockets of the meprin beta metalloprotease. This study assists in the development of the new lead molecules as well as gives a better understanding of their interaction with meprin-β.Graphical abstractGraphical abstract for this article
  • In-Silico analysis of missense SNPs in Human HPPD gene associated with
           Tyrosinemia type III and Hawkinsinuria
    • Abstract: Publication date: Available online 25 April 2019Source: Computational Biology and ChemistryAuthor(s): Muhammad Naveed, Sana Tehreem, Muhammad Zubair Mehboob HPPD gene codes a dioxygenase enzyme involved in catalysis of different molecules such as tyrosine and phenylalanine by oxidizing them to produce energy. A single change in protein can trigger serious genetic disorders like Tyrosinemia type III and Hawkinsinuria. This study aims to identify the functional missense SNPs of HPPD gene by using multiple computational tools. All deleterious missense SNPs retrieved from Ensembl and OMIM database were evaluated through six different software. Ultimately, out of 148 missense SNPs, only 27 were confirmed as disease causing SNPs by developing consensus approach. These damaging SNPs were further examined to evaluate their impact on protein stability and energy including their evolutionary conservation. Native and mutated proteins structures were also designed and superimposed by I-TASSER and PyMol respectively. This work results in narrowing down missense SNPs which are still not confirmed experimentally and demands the confirmation by GWAS data. Thus, these missense SNPs could directly or indirectly destabilize the amino acid interactions causing functional deviations of protein.Graphical abstractGraphical abstract for this article
  • Three Dimensional Structure Prediction of Panomycocin, a Novel
           Exo-β-1,3-Glucanase Isolated from Wickerhamomyces anomalus NCYC 434 and
           the Computational Site-Directed Mutagenesis Studies to Enhance its Thermal
           Stability for Therapeutic Applications
    • Abstract: Publication date: Available online 24 April 2019Source: Computational Biology and ChemistryAuthor(s): Muhammed Tilahun Muhammed, Çağdaş Devrim Son, Fatih İzgü Panomycocin is a naturally produced potent antimycotic/antifungal protein secreted by the yeast Wickerhamomyces anomalus NCYC 434 with an exo-β-1,3-glucanase activity. In this study the three dimensional structure of panomycocin was predicted and the computational site-directed mutagenesis was performed to enhance its thermal stability in liquid formulations over the body temperature for topical therapeutic applications. Homology modeling was performed with MODELLER and I-TASSER. Among the generated models, the model with the lowest energy and DOPE score was selected for further loop modeling. The loop model was optimized and the reliability of the model was confirmed with ERRAT, Verify 3D and Ramachandran plot values. Enhancement of the thermal stability of the model was done using contemporary servers and programs such as SPDBViewer, CNA, I-Mutant2.0, Eris, AUTO-MUTE and MUpro. In the region outside the binding site of the model Leu52 Arg, Phe223Arg and Gly254Arg were found to be the best thermostabilizing mutations with 6.26 K, 6.26 K and 8.27 K increases, respectively. In the binding site Glu186Arg was found to be the best thermostabilizer mutation with a 9.58 K temperature increase. The results obtained in this study led us to design a mutant panomycocin that can be used as a novel antimycotic/antifungal drug in a liquid formulation for topical applications over the normal body temperature.Graphical abstractGraphical abstract for this article
  • Detection of nucleotide sequences capable of forming non-canonical DNA
           structures: Application of automata theory
    • Abstract: Publication date: Available online 24 April 2019Source: Computational Biology and ChemistryAuthor(s): M.V. Yurushkin, L.R. Gervich, S.S. Bachurin, M.E. Kletskii In this study, we develop a program that allows us to reveal DNA receptors, i.e. nucleotide sequences that may form more than one non-canonical structure. The data obtained may be analysed either experimentally or using DNA banks, and refers to the coding, non-coding or promotor region of the gene. These results provide a better understanding of the role that non-canonical structures play in pathological modifications of the genetic apparatus, resulting in tumour formation or inherited disease. They also reveal the effect of single nucleotide polymorphisms on gene expression, indicate so-called “risk regions” in which the substitution of a single nucleotide may lead to increased formation of non-canonical structures, and elucidate the epigenetic mechanisms of microorganism adaptation.Graphical abstractGraphical abstract for this article
  • Synthesis and molecular modelling of novel fused six-membered O-containing
           heterocycles as potential acetylcholinesterase inhibitors
    • Abstract: Publication date: Available online 9 April 2019Source: Computational Biology and ChemistryAuthor(s): Yaghoub Pourshojaei, Ardavan Abiri, Razieh Eskandari, Fatemeh Dourandish, Khalil Eskandari, Ali Asadipou An efficient, borax-catalyzed protocol for the synthesis of novel 4-aryl-substituted-4H-pyran derivatives fused to α-pyrone ring in a one-pot is described. By this achievement, some novel 4-aryl substituted 4H-pyrans fused to α-pyrone ring as potential acetylcholinesterase inhibitors (AChEIs) with good to excellent yields are obtained from a one-pot three-component reaction between various arylaldehydes, 4-hydroxy-6-methyl-2H-pyran-2-one and malononitrile. The method is a facile, inexpensive, practical and highly efficient one to obtain target compounds. The chemical structures of all compounds were characterized by FT-IR, FT-13CNMR and FT-1HNMR spectroscopy and also elemental analyses data. In addition, both molecular modelling studies and Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMETox) prediction nominated all compounds as good acetylcholinesterase inhibitors to potential treatment of Alzheimer, Parkinson and Autism diseases that among them compound 4f showed best activity against acetylcholinesterase enzyme.Graphical Graphical abstract for this articleHerein, an efficient and practical approach for the synthesis and molecular modeling of novel 4-aryl-substituted-4H-pyran derivatives fused to α-pyrone ring is reported. The molecular modeling studies nominated all compounds to the inhibition of AChE enzyme, and also ADMETox prediction confirm that they are good candidates for anti-Alzheimer, anti-Parkinson, and anti-Autism drugs.
  • Oxidatively-Mediated in Silico Epimerization of a Highly Amyloidogenic
           Segment in the Human Calcitonin Hormone (hCT15-19)
    • Abstract: Publication date: Available online 8 April 2019Source: Computational Biology and ChemistryAuthor(s): Ahmad Kamal M. Hamid, Joanna C. Salvatore, Ke Wang, Prashantha Murahari, Andrea Guljas, Anita Rágyanszki, Michael Owen, Balázs Jójárt, Milán Szőri, Imre G. Csizmadia, Béla Viskolcz, Béla Fiser In order to study the effects of peptide exposure to oxidative attack, we chose a model reaction in which the hydroxyl radical discretely abstracts a hydrogen atom from the α-carbon of each residue of a highly amyloidogenic region in the human calcitonin hormone, hCT15-19. Based on a combined Molecular Mechanics / Quantum Mechanics approach, the extended and folded L- and D-configuration and radical intermediate hCT15-19 peptides were optimized to obtain their compactness, secondary structure and relative thermodynamic data. The results suggest that the epimerization of residues is generally an exergonic process that can explain the cumulative nature of molecular aging. Moreover, the configurational inversion induced conformational changes can cause protein dysfunction. The epimerization of the central residue to the D-configuration induced a hairpin structure in hCT15-19, concomitant with a possible oligomerization of human calcitonin into Aβ(1-42)-like amyloid fibrils present in patients suffering from Alzheimer’s disease.Graphical abstractGraphical abstract for this article
  • Computational binding study of cardiac troponin I antibody towards cardiac
           versus skeletal troponin I
    • Abstract: Publication date: June 2019Source: Computational Biology and Chemistry, Volume 80Author(s): Jad Sabek, Paula Martínez-Pérez, Jaime García-Rupérez A computational study of the interaction of cardiac troponin I (cTnI) with its specific antibody and of that antibody with skeletal troponin I (sTnI), the principal interferon of cTnI, is carried out. Computational and simulation tools such as FTSite, FTMap, FTDock and pyDock are used to determine the binding sites of these molecules and to study their interactions and molecular docking performance, allowing us to obtain relevant information related with the antigen-antibody interaction for each of the targets. In the context of the development of immunosensing platforms, this type of computational analysis allows performing a preliminary in-silico affinity study of the available bioreceptors for a better selection when moving to the experimental stage, with the subsequent saving in cost and time.Graphical abstractGraphical abstract for this article
  • Identification and expression analysis of StGRAS gene family in potato
           (Solanum tuberosum L.)
    • Abstract: Publication date: Available online 5 April 2019Source: Computational Biology and ChemistryAuthor(s): Shulin Wang, Ning Zhang, Xi Zhu, Jiangwei Yang, Shigui Li, Yuzhang Che, Weigang Liu, Huaijun Si The GRAS gene family is a class of plant-specific transcription factors which play pivotal roles in the regulation of plant growth and development. At present, the GRAS gene family has been completely identified in Arabidopsis thaliana, however, there are no systematic research reports in potato. In the present study, we obtained an overview of the GRAS gene family including gene structure, gene expression, chromosome mapping and phylogenetic analysis, and 52 StGRASs were identified in the potato by bioinformatics analysis, which could be divided into eight subfamilies based on phylogeny. More than 90% of genes do not contain introns and the StGRAS family major function is protein binding according to gene ontology analysis (GO).The tissue specific expression analysis showed that StGRAS3, StGRAS35 and StGRAS50 gene had the higher expression in roots, stems and leaves compared with other StGRAS, StGRAS9 and StGRAS28 genes were responded to plant hormones IAA, BA and GA3 treatment. The result could provide a basis for further studying the function of GRAS genes and GRAS-mediated signal transduction pathways in potato.Graphical abstractGraphical abstract for this articleThe expression profiles of StGRAS genes visualized as heatmaps with respect to different tissues. The data was collected from potato gene database.
  • A selectivity study of benzenesulfonamide derivatives on human carbonic
           anhydrase II/IX by 3D-QSAR, Molecular Docking and Molecular Dynamics
    • Abstract: Publication date: Available online 4 April 2019Source: Computational Biology and ChemistryAuthor(s): Guanghui Tang, Yuxuan Wang, Haiqiong Guo, Qingxiu He, Yuping Zhang, Yong Hu, Yuanqiang Wang, Zhihua Lin Nowadays, different approaches have been pursued with the intent to develop sulfonamide-like carbonic anhydrase inhibitors that possess better selectivity profiles toward the different human isoforms of the enzyme. Here, we used conventional 3D-QSAR methods, including comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and Topomer CoMFA, to construct three-dimensional quantitative structure-activity relationship (3D-QSAR) models for benzenesulfonamide derivatives as human carbonic anhydrase (hCA) II/IX inhibitors. The theoretical models had good reliability (R2>0.75) and predictability (Q2>0.55), and the contour maps could graphically present the contributions of the force fields for activity and identify the structural divergence between human carbonic anhydrase II inhibitors and human carbonic anhydrase IX inhibitors. Consequently, we explored the selectivity of inhibitor for human carbonic anhydrase II and IX through molecular docking, and the difference of activity coincides with the potential binding mode well. According to the results of the predicted values and the molecule docking, we found that the inhibitors published in the literature had stronger inhibition on the hCA IX; based on the theoretical models, we designed seven new compounds with good potential activity and reasonably good ADMET profile, which could selectively inhibit hCA IX. Molecular Dynamics Simulation showed that newly-designed compound D7 had good selectivity on hCA IX. The findings from 3D-QSAR and docking studies maybe helpful in the rational drug design of isoform-selective inhibitors.
  • Phylogenomics of an uncultivated, aerobic and thermophilic,
           photoheterotrophic member of Chlorobia sheds light into the evolution of
           the phylum Chlorobi
    • Abstract: Publication date: Available online 3 April 2019Source: Computational Biology and ChemistryAuthor(s): Chayan Roy, Utpal Bakshi, Moidu Jameela Rameez, Subhrangshu Mandal, Prabir Kumar Haldar, Prosenjit Pyne, Wriddhiman Ghosh All cultivated members of the phylum Chlorobi are classified under the two classes Chlorobia and Ignavibacteria. The recently-reported, uncultivated genome-species of Chlorobi have not suggested any alteration in the dichotomy of the two classes, but have hypothesized the existence of a distinct, aerobic and photoheterotrophic, order/family level lineage within Chlorobia, which otherwise was considered to be a monophyletic group of anaerobic sulfur-photolithoautotrophs. Here we report the discovery of a novel population genome bin (named Chlorobi-445) from the combined metagenomes of three spatially-contiguous but visually-distinct microbial mats growing along the 65-41 °C hydrothermal gradient of a boron-rich microbialite spring located in the Puga geothermal area of Eastern Ladakh, India. 1.3, 8.2 and 3.8% metagenomic reads from the mat communities located at 65 °C, 52 °C and 41 °C sample-sites respectively, were found to map-back to the 2,809,852 bp genome of Chlorobi-445. Phylogenomically, and therefore in terms of potential metabolic attributes, Chlorobi-445 showed close relationship with Ca. Thermochlorobacter aerophilum. Gene content suggested Chlorobi-445 to be an aerobic photoorganoheterotroph. Although this new lineage encodes all the proteins necessary for the biosynthesis of bacteriochlorophylls and the photosynthetic reaction centre, it is potentially devoid of genes concerned with lithotrophic sulfur oxidation and carbon-fixation. Individual Chlorobi phylogenies based on the sequence similarities of 16S rRNA genes, 22 ribosomal proteins, and 56 conserved marker-proteins that are encoded from single-copy genes, unanimously suggested that the class Chlorobia encompasses two major branches/clades. Whereas the Clade-I is a homogeneous cluster of culturable, anaerobic sulfur-/iron-oxidizing photolithoautotrophs, Clade-II harbors (i) Chloroherpeton species, and (ii) uncultivated aerobic photoheterotrophs such as Chlorobi-445, Chlorobium sp. GBChlB & Ca. T. aerophilum, in its two sub-clades. Distribution of bioenergetic attributes over the different branches of Chlorobi, together with the aerobic chemoorganoheterotrophic nature of the deepest-branching genome-species NICIL-2, indicated that the early Chlorobi were aerobic chemoorganoheterotrophs, while anaerobicity, phototrophy, lithotrophy, and autotrophy were all potentially added in the course of evolution.Graphical abstractGraphical abstract for this article
  • Identifying Mutated Driver Pathways in Cancer by Integrating Multi-Omics
    • Abstract: Publication date: Available online 2 April 2019Source: Computational Biology and ChemistryAuthor(s): Jingli Wu, Qirong Cai, Jinyan Wang, Yuanxiu Liao Since the driver pathway in cancer plays a crucial role in the formation and progression of cancer, it is very imperative to identify driver pathways, which will offer important information for precision medicine or personalized medicine. In this paper, an improved maximum weight submatrix problem model is proposed by integrating such three kinds of omics data as somatic mutations, copy number variations, and gene expressions. The model tries to adjust coverage and mutual exclusivity with the average weight of genes in a pathway, and simultaneously considers the correlation among genes, so that the pathway having high coverage but moderate mutual exclusivity can be identified. By introducing a kind of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm PGA-MWS is presented to solve the model. Experimental comparisons among algorithms GA, MOGA, iMCMC and PGA-MWS were performed on biological and simulated data sets. The experimental results show that, compared with the other three algorithms, the PGA-MWS one based on the improved model can identify the gene sets with high coverage but moderate mutual exclusivity and scales well. Many of the identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in literatures. The experimental results indicate that the proposed approach may become a useful complementary tool for detecting cancer pathways.
  • Interspersed 5’cis-regulatory elements ascertain the spatio-temporal
           transcription of cytoskeletal profilin gene family in Arabidopsis
    • Abstract: Publication date: Available online 2 April 2019Source: Computational Biology and ChemistryAuthor(s): Arnav K. Upadhyay, Sakshi Arora, Dhananjay K. Pandey, Bhupendra Chaudhary Spatio-temporal expression patterns of cytoskeleton-associated profilin (PRF) family proteins in response to varied environmental stimuli are tightly regulated. Functional analyses of PRFs have revealed their crucial roles in varied developmental and stress related traits, but very little is implicit pertaining to cis-acting regulatory elements that regulate such intricate expression patterns. Here, we identified cis-elements with their varying distribution frequencies by scanning 1.5kbp upstream sequences of 5’regulatory regions of PRFs of dicot and monocot plant species. Predicted cis-elements in the regulatory sub-regions of Arabidopsis PRFs (AtPRFs) were predominantly associated with development-responsive motifs (DREs), light responsive elements (LREs), hormonal responsive elements (HREs), core motifs and stress-responsive elements (SREs). Interestingly, DREs, LREs and core promoter motifs, were extensively distributed up to the distal end of 5’regulatory regions on contrary to HREs present closer to the translational start site in Arabidopsis. The evolutionary footprints of predicted orthologous cis-elements were conserved, and preferably located in the proximal regions of 5’regulatory regions of evolutionarily diverged plant species. We also explored comprehensive tissue-specific global gene expression levels of PRFs under diverse hormonal and abiotic stress regimes. In response, the PRFs exhibited large transcriptional biases in a time- and organ-dependent manner. Further, the methodical elucidation of spatial expression analysis of predicted cis-elements binding transcription factors and relevant PRFs showed notable correlation. Results indicate that binding transcription factors’ expression data is largely informative for envisaging their precise roles in the spatial regulation of target PRFs. These results highlight the importance of PRFs during plant development; and establish a relationship between their spatial expression patterns and presence of respective regulatory motifs in their promoter sequences. This information could be employed in future studies and field-utilization of cell wall structural genes.Graphical Graphical abstract for this articleThe distribution frequencies of cis-elements in 1.5kbp upstream sequences of 5’regulatory regions of Arabidopsis PRFs were examined. Several cis-elements responsive to various external stimuli were predicted and their evolutionary footprints were identified. Expression analysis of AtPRFs revealed their transcriptional biases in a time- and organ-dependent manner. Methodical elucidation of spatial expression analysis of predicted cis-elements binding transcription factors and relevant PRFs showed significant correlation highlighting the importance of PRFs during plant development.
  • De ovo glycan structural identification from mass spectra using tree
           merging strategy
    • Abstract: Publication date: Available online 30 March 2019Source: Computational Biology and ChemistryAuthor(s): Fusong Ju, Jingwei Zhang, Dongbo Bu, Yan Li, Jinyu Zhou, Hui Wang, Yaojun Wang, Chuncui Huang, Shiwei Sun MotivationGlycans are large molecules with specific tree structures. Glycans play important roles in a great variety of biological processes. These roles are primarily determined by the fine details of their structures, making glycan structural identification highly desirable. Mass spectrometry (MS) has become the major technology for elucidation of glycan structures. Most de Novo approaches to glycan structural identification from mass spectra fall into three categories: enumerating followed by filtering approaches, heuristic and dynamic programming-based approaches. The former suffers from its low efficiency while the latter two suffer from the possibility of missing the actual glycan structures. Thus, how to reliably and efficiently identify glycan structures from mass spectra still remains challenging.ResultsIn this study we propose an efficient and reliable approach to glycan structure identification using tree merging strategy. Briefly, for each MS peak, our approach first calculated monosaccharide composition of its corresponding fragment ion, and then built a constraint that forces these monosaccharides to be directly connected in the underlying glycan tree structure. According to these connecting constraints, we next merged constituting monosaccharides of the glycan into a complete structure step by step. During this process, the intermediate structures were represented as subtrees, which were merged iteratively until a complete tree structure was generated. Finally the generated complete structures were ranked according to their compatibility to the input mass spectra. Unlike the traditional enumerating followed by filtering strategy, our approach performed deisomorphism to remove isomorphic subtrees, and ruled out invalid structures that violates the connection constraints at each tree merging step, thus significantly increasing efficiency. In addition, all complete structures satisfying the connection constraints were enumerated without any missing structure. Over a test set of 10 N-glycan standards, our approach accomplished structural identification in minutes and gave the manually-validated structure first three highest score. We further successfully applied our approach to profiling and subsequent structure assignment of glycans released from glycoprotein mAb, which was in perfect agreement with previous studies and CE analysis.AvailabilitySource code of gNovo is freely available through, and a web server is available through, information:Supplementary data are available online.
  • In silico analysis of different signal peptides for the secretory
           production of recombinant human keratinocyte growth factor in Escherichia
    • Abstract: Publication date: Available online 29 March 2019Source: Computational Biology and ChemistryAuthor(s): Mansoureh Shahbazi Dastjerdeh, Mahya Marashian, Mohammadtaghi Borjian Boroujeni, Hamzeh Rahimi BackgroundThe recombinant human truncated Keratinocyte growth factor (Palifermin) is the only FDA approved medicine for the treatment of oral mucositis. The Keratinocyte growth factor is a fairly unstable protein due to its high aggregation propensity and therefore its expression as a secretory protein may results in the production of a protein with more stability, higher solubility, better folding, enhanced biological activity, N-terminal authenticity and simplified downstream processing.ObjectiveThe aim of this study was in silico evaluation of 31 different secretory signal peptides to determine the best theoretical candidates for the secretory production of recombinant truncated human KGF in E. coli.MethodsThirty different prokaryotic signal peptides experimentally shown to be capable of recombinant protein secretion in E.coli, along with the native KGF signal peptide were selected for further investigations. The signal peptide sequences were retrieved from UniProt database. The ability of SPs to act as a secretory leader peptide for rhKGF and the location of cleavage sites were predicted by SignalP 4.1. Physicochemical properties of the signal peptides, which may influence protein secretion, were analyzed by ProtParam and PROSOII. Furthermore, the mRNA secondary structure and Gibbs free energy profile of the selected SPs were analyzed in the fusion state with the rhKGF using Visual Gene Developer package.Results and ConclusionComputational analysis of the physicochemical properties affecting protein secretion identified Sec-B dependent OmpC, Bla, and StaI and SRP dependent TolB signal peptides as the best theoretical candidates for the secretory production of recombinant truncated human KGF in E.coli.Graphical abstractGraphical abstract for this article
  • Synthesis, cytotoxicity, apoptosis and molecular docking studies of novel
           phenylbutyrate derivatives as potential anticancer agents
    • Abstract: Publication date: Available online 28 March 2019Source: Computational Biology and ChemistryAuthor(s): Azar Mostoufi, Raheleh Baghgoli, Masood Fereidoonnezhad Phenylbutyrate (PB), a small aromatic fatty acid, has been known as an interesting compound with the ability of anti-proliferation and cell growth inhibition in cancer cells. In the present study, a series of PB derivatives were synthesized by Passerini multicomponent reaction and their cytotoxic activities against various human cancer cell lines including A549 (non-small cell lung cancer), MDA-MB-231 (breast cancer), and SW1116 (colon cancer) were evaluated. The results revealed that B9, displayed significantly higher in vitro cytotoxicity with IC50 of 6.65, 8.44 and 24.71 μM, against A549, MDA-MB-231 and, SW1116, respectively, in comparison to PB. The effects of these compounds on the proliferation of MCF-10A as non-tumoral breast cell line, showed good selectivity of the compounds between tumorigenic and non-tumorigenic cell lines. Moreover, B9 has indicated apoptosis-inducing activities to MDA-MB-231 cancer cell line in a dose-dependent manner. The molecular docking studies of the synthesized compounds on pyruvate dehydrogenase kinase 2 (PDK2; PDB ID: 2BU8) and histone deacetylase complex (HDAC; PDB ID: 1C3R), as the main targets of PB were applied to predict the binding sites and binding orientation of the compounds to these targets.Graphical abstractGraphical abstract for this article
  • Theoretical study on the DNA interaction properties of copper(II)
    • Abstract: Publication date: Available online 26 March 2019Source: Computational Biology and ChemistryAuthor(s): Shuang Li, Tifang Miao, Xianliang Fu, Fang Ma, Hui Gao, Guoping Zhang Theoretical studies on DNA-cleavage and DNA-binding properties of a series of Cu(II) complexes [Cu(bimda)(diimine)] 1–5 have been carried out by density functional theory (DFT). The optimized structures of Cu(II) complexes were docked into parallel, antiparallel and mixed G-quadruplexes, with which the binding energies of complexes 1–5 were obtained. The cytotoxicities of these complexes can be predicted preliminarily by the binding energies. To explore the energy changes of Cu(II) complexes in duplex DNA, the optimized structures of these complexes were docked into the duplex DNA, and the obtained docking models were further optimized using QM/MM method. The DNA-cleavage abilities of complexes 1–5 can be predicted accurately and explained reasonably by the computed intra-molecular reorganization energies of these complexes. This work reported here has implications for the understanding of the interaction Cu(II) complexes with the DNA, which might be helpful for the future directing the design of novel anticancer Cu(II) complexes.Graphical abstractGraphical abstract for this articleTheoretical studies on DNA-cleavage and DNA-binding properties of Cu(II) complexes 1–5 have been carried out using the density functional theory and docking methods. The DNA-cleavage abilities of these complexes can be predicted by the computed intra-molecular reorganization energies of these complexes, and corresponding DNA-cleavage mechanisms were explored.
  • Automatic Hierarchy Classification in Venation Networks using Directional
           Morphological Filtering for Hierarchical Structure Traits Extraction
    • Abstract: Publication date: Available online 26 March 2019Source: Computational Biology and ChemistryAuthor(s): Yangjing Gan, Yi Rong, Fei Huang, Lun Hu, Xiaohan Yu, Pengfei Duan, Shengwu Xiong, Haiping Liu, Jing Peng, Xiaohui Yuan The extraction of vein traits from venation networks is of great significance to the development of a variety of research fields, such as evolutionary biology. However, traditional studies normally target to the extraction of reticulate structure traits (ReSTs), which is not sufficient enough to distinguish the difference between vein orders. For hierarchical structure traits (HiSTs), only a few tools have made attempts with human assistance, and obviously are not practical for large-scale traits extraction. Thus, there is a necessity to develop the method of automated vein hierarchy classification, raising a new challenge yet to be addressed. We propose a novel vein hierarchy classification method based on directional morphological filtering to automatically classify vein orders. Different from traditional methods, our method classify vein orders from highly dense venation networks for the extraction of traits with ecological significance. To the best of our knowledge, this is the first attempt to automatically classify vein hierarchy. To evaluate the performance of our method, we prepare a soybean transmission image dataset (STID) composed of 1200 soybean leaf images and the vein orders of these leaves are manually coarsely annotated by experts as ground truth. We apply our method to classify vein orders of each leaf in the dataset. Compared with ground truth, the proposed method achieves great performance, while the average deviation on major vein is less than 5 pixels and the average completeness on second-order veins reaches 54.28%.
  • Combination of Pharmacophore Modeling and 3D-QSAR analysis of Potential
           Glyoxalase-I Inhibitors as Anticancer Agents
    • Abstract: Publication date: Available online 25 March 2019Source: Computational Biology and ChemistryAuthor(s): Mahmoud A. Al-Sha'er, Qosay A. Al-Balas, Mohammad A. Hassan, Ghazi A. Al Jabal, Ammar M. Almaaytah Glyoxalase system is an ubiquitous system in human cells which has been examined thoroughly for its role in different diseases. It comprises two enzymes; Glyoxalase I (Glo-I) and Glyoxalase II (Glo-II) which perform detoxifying endogenous harmful metabolites, mainly methylglyoxal (MG) into non-toxic bystanders. In silico computer Aided Drug Design approaches were used and ninety two diverse pharmacophore models were generated from eighteen Glyoxalase I crystallographic complexes. Subsequent QSAR modeling followed by ROC evaluation identified a single pharmacophore model which was able to predict the expected Glyoxalase I inhibition. Screening of the National Cancer Institute (NCI) database using the optimal pharmacophore Hypo(3VW9) identified several promising hits. Thirty eight hits were successfully predicted then ordered and evaluated in vitro. Seven hits out of the thirty eight tested compounds showed more than 50% inhibition with low micromolar IC50.Graphical Graphical abstract for this articleHydrogen bond map and Hydrophobic bond map of hit 27 (IC50 = 3.65 µM) docked in the binding pocket of Glyoxalase I (pdb: 3VW9, resolution =1.47 Å)
  • Modelling the role of dual specificity phosphatases in Herceptin resistant
           breast cancer cell lines
    • Abstract: Publication date: Available online 25 March 2019Source: Computational Biology and ChemistryAuthor(s): Petronela Buiga, Ari Elson, Lydia Tabernero, Jean-Marc Schwartz BackgroundBreast cancer remains the most lethal type of cancer for women. A significant proportion of breast cancer cases are characterised by overexpression of the human epidermal growth factor receptor 2 protein (HER2). These cancers are commonly treated by Herceptin (Trastuzumab), but resistance to drug treatment frequently develops in tumour cells. Dual-specificity phosphatases (DUSPs) are thought to play a role in the mechanism of resistance, since some of them were reported to be overexpressed in tumours resistant to Herceptin.ResultsWe used a systems biology approach to investigate how DUSP overexpression could favour cell proliferation and to predict how this mechanism could be reversed by targeted inhibition of selected DUSPs. We measured the expression of 20 DUSP genes in two breast cancer cell lines following long-term (6 months) exposure to Herceptin, after confirming that these cells had become resistant to the drug. We constructed several Boolean models including specific substrates of each DUSP, and showed that our models correctly account for resistance when overexpressed DUSPs were kept activated. We then simulated inhibition of both individual and combinations of DUSPs, and determined conditions under which the resistance could be reversed.ConclusionsThese results show how a combination of experimental analysis and modelling help to understand cell survival mechanisms in breast cancer tumours, and crucially enable us to generate testable predictions potentially leading to new treatments of resistant tumours.
  • Predicting Drug-Target Interaction Network Using Deep Learning Model
    • Abstract: Publication date: Available online 25 March 2019Source: Computational Biology and ChemistryAuthor(s): Jiaying You, Robert D. McLeod, Pingzhao Hu BackgroundTraditional methods for drug discovery are time-consuming and expensive, so efforts are being made to repurpose existing drugs. To find new ways for drug repurposing, many computational approaches have been proposed to predict drug-target interactions (DTIs). However, due to the high-dimensional nature of the data sets extracted from drugs and targets, traditional machine learning approaches, such as logistic regression analysis, cannot analyze these data sets efficiently. To overcome this issue, we propose LASSO (Least absolute shrinkage and selection operator)-based regularized linear classification models and a LASSO-DNN (Deep Neural Network) model based on LASSO feature selection to predict DTIs. These methods are demonstrated for repurposing drugs for breast cancer treatment.MethodsWe collected drug descriptors, protein sequence data from Drugbank and protein domain information from NCBI. Validated DTIs were downloaded from Drugbank. A new similarity-based approach was developed to build the negative DTIs. We proposed multiple LASSO models to integrate different combinations of feature sets to explore the prediction power and predict DTIs. Furthermore, building on the features extracted from the LASSO models with the best performance, we also introduced a LASSO-DNN model to predict DTIs. The performance of our newly proposed DNN model (LASSO-DNN) was compared with the LASSO, standard logistic (SLG) regression, support vector machine (SVM), and standard DNN models.ResultsExperimental results showed that the LASSO-DNN over performed the SLG, LASSO, SVM and standard DNN models. In particular, the LASSO models with protein tripeptide composition (TC) features and domain features were superior to those that contained other protein information, which may imply that TC and domain information could be better representations of proteins. Furthermore, we showed that the top ranked DTIs predicted using the LASSO-DNN model can potentially be used for repurposing existing drugs for breast cancer based on risk gene information.ConclusionsIn summary, we demonstrated that the efficient representations of drug and target features are key for building learning models for predicting DTIs. The disease-associated risk genes identified from large-scale genomic studies are the potential drug targets, which can be used for drug repurposing.Graphical Graphical abstract for this article
  • Discovery of perturbation gene targets via free text metadata mining in
           Gene Expression Omnibus
    • Abstract: Publication date: Available online 24 March 2019Source: Computational Biology and ChemistryAuthor(s): Djordje Djordjevic, Joshua Y.S. Tang, Yun Xin Chen, Shu Lun Shannon Kwan, Raymond W.K. Ling, Gordon Qian, Chelsea Y.Y. Woo, Samuel J. Ellis, Joshua W.K. Ho There exists over 2.5 million publicly available gene expression samples across 101,000 data series in NCBI's Gene Expression Omnibus (GEO) database. Due to the lack of the use of standardised ontology terms in GEO's free text metadata to annotate the experimental type and sample type, this database remains difficult to harness computationally without significant manual intervention.In this work, we present an interactive R/Shiny tool called GEOracle that utilises text mining and machine learning techniques to automatically identify perturbation experiments, group treatment and control samples and perform differential expression. We present applications of GEOracle to discover conserved signalling pathway target genes and identify an organ specific gene regulatory network.GEOracle is effective in discovering perturbation gene targets in GEO by harnessing its free text metadata. Its effectiveness and applicability has been demonstrated by cross validation and two real-life case studies. It opens up new avenues to unlock the gene regulatory information embedded inside large biological databases such as GEO. GEOracle is available at
  • SCOUT:A new algorithm for the inference of pseudo-time trajectory using
           single-cell data
    • Abstract: Publication date: Available online 24 March 2019Source: Computational Biology and ChemistryAuthor(s): Jiangyong Wei, Tianshou Zhou, Xinan Zhang, Tianhai Tian Single cell technology is a powerful tool to reveal intercellular heterogeneity and discover cellular developmental processes. When analyzing the complexity of cellular dynamics and variability, it is important to construct a pseudo-time trajectory using single-cell expression data to reflect the process of cellular development. Although a number of computational and statistical methods have been developed recently for single-cell analysis, more effective and efficient methods are still strongly needed. In this work we propose a new method named SCOUT for the inference of single-cell pseudo-time ordering with bifurcation trajectories. We first propose to use the fixed-radius near neighbors algorithms based on cell densities to find landmarks to represent the cell states, and employ the minimum spanning tree (MST) to determine the developmental branches. We then propose to use the projection of Apollonian circle or a weighted distance to determine the pseudo-time trajectories of single cells. The proposed algorithm is applied to one synthetic and two realistic single-cell datasets (including single-branching and multi-branching trajectories) and the cellular developmental dynamics is recovered successfully. Compared with other popular methods, numerical results show that our proposed method is able to generate more robust and accurate pseudo-time trajectories. The code of the method is implemented in Python and available at
  • A class imbalance-aware Relief algorithm for the classification of tumors
           using microarray gene expression data
    • Abstract: Publication date: Available online 24 March 2019Source: Computational Biology and ChemistryAuthor(s): Yuanyu He, Junhai Zhou, Yaping Lin, Tuanfei Zhu DNA microarray data has been widely used in cancer research due to the significant advantage helped to successfully distinguish between tumor classes. However, typical gene expression data usually presents a high-dimensional imbalanced characteristic, which poses severe challenge for traditional machine learning methods to construct a robust classifier performing well on both the minority and majority classes. As one of the most successful feature weighting techniques, Relief is considered to particularly suit to handle high-dimensional problems. Unfortunately, almost all Relief-based methods have not taken the class imbalance distribution into account. This study identifies that existing Relief-based algorithms may underestimate the features with the discernibility ability of minority classes, and ignore the distribution characteristic of minority class samples. As a result, an additional bias towards being classified into the majority classes can be introduced. To this end, a new method, named imRelief, is proposed for efficiently handling high-dimensional imbalanced gene expression data. imRelief can correct the bias towards to the majority classes, and consider the scattered distributional characteristic of minority class samples in the process of estimating feature weights. This way, imRelief has the ability to reward the features which perform well at separating the minority classes from other classes. Experiments on four microarray gene expression data sets demonstrate the effectiveness of imRelief in both feature weighting and feature subset selection applications.
  • Identification of novel Plasmodium falciparum PI4KB inhibitors as
           potential anti-malarial drugs: homology modeling, molecular docking and
           molecular dynamics simulations
    • Abstract: Publication date: Available online 23 March 2019Source: Computational Biology and ChemistryAuthor(s): Mahmoud A.A. Ibrahim, Alaa H.M. Abdelrahman, Alaa M.A. Hassan The current study was set to discover selective Plasmodium falciparum phosphatidylinositol-4-OH kinase type III beta (pfPI4KB) inhibitors as potential antimalarial agents using combined structure-based and ligand-based drug discovery approach. A comparative model of pfPI4KB was first constructed and validated using molecular docking techniques. Performance of Autodock4.2 and Vina4 software in predicting the inhibitor-PI4KB binding mode and energy was assessed based on two Test Sets: Test Set I contained five ligands with resolved crystal structures with PI4KB, while Test Set II considered eleven compounds with known IC50 value towards PI4KB. The outperformance of Autodock as compared to Vina was reported, giving a correlation coefficient (R2) value of 0.87 and 0.90 for Test Set I and Test Set II, respectively. Pharmacophore-based screening was then conducted to identify drug-like molecules from ZINC database with physicochemical similarity to two potent pfPI4KB inhibitors –namely cpa and cpb. For each query inhibitor, the best 1000 hits in terms of TanimotoCombo scores were selected and subjected to molecular docking and molecular dynamics (MD) calculations. Binding energy was then estimated using molecular mechanics–generalized Born surface area (MM-GBSA) approach over 50 ns MD simulations of the inhibitor-pfPI4KB complexes. According to the calculated MM-GBSA binding energies, ZINC78988474 and ZINC20564116 were identified as potent pfPI4KB inhibitors with binding energies better than those of cpa and cpb, with ΔGbinding ≥ -34.56 kcal/mol. The inhibitor-pfPI4KB interaction and stability were examined over 50 ns MD simulation; as well the selectivity of the identified inhibitors towards pfPI4KB over PI4KB was reported.Graphical abstractGraphical abstract for this article
  • DFT and QTAIM based investigation on the structure and antioxidant
           behavior of lichen substances Atranorin, Evernic acid and Diffractaic acid
    • Abstract: Publication date: Available online 23 March 2019Source: Computational Biology and ChemistryAuthor(s): T.K. Shameera Ahamed, Vijisha K. Rajan, K. Sabira, K. Muraleedharan In this study, the structural and antioxidant behavior of the three lichen-derived natural compounds such as atranorin (AT), evernic acid (EV) and diffractaic acid (DF) has been investigated in the gas and water phase using both B3LYP and M06-2X functional level of density functional theory (DFT) with two different basis sets 6-31+G (d, p) and 6-311++G (d, p). The intramolecular H–bonds (IHB) strength, aromaticity and noncovalent interactions (NCI) have been computed with the help of the quantum theory of atoms in molecules (QTAIM). This calculation gives major structural characteristics that indirectly influence the antioxidant behavior of the investigated compounds. The spin density (SD) delocalization of the unpaired electron is found to be the main stabilizing factor of neutral and cationic radical species. The main mechanisms, recommended in the literature, for the antioxidant action of polyphenols as radical scavengers such as hydrogen atom transfer (HAT), single electron transfer followed by proton transfer (SET-PT), and sequential proton loss electron transfer (SPLET), were examined. The result shows that the HAT and SPLET mechanism are the most conceivable one for the antioxidant action of this class of compounds in gas and water phase respectively. Preference of SPLET over HAT in water phase is due to the significantly lower value of proton affinity (PA) compared to the bond dissociation enthalpy (BDE) value. This study reveals that O2-H3, O9-H26 and O4-H45 respectively are the most favored site of AT, EV and DF for homolytic as well as heterolytic O-H bond breaking.Graphical abstractGraphical abstract for this article
  • G-rich VEGF Aptamer as a Potential Inhibitor of Chitin Trafficking Signal
           in Emerging Opportunistic Yeast Infection
    • Abstract: Publication date: Available online 18 March 2019Source: Computational Biology and ChemistryAuthor(s): Mohammad Vahed, Gholamreza Ahmadian, Niyoosha Ameri, Majid Vahed The alarm is rang for friendly fire; Saccharomyces cerevisiae (S. cerevisiae) newfound as a fungal pathogen with an individual feature. S. cerevisiae has food safety and is not capable of producing infection but, when the host defenses are weakened, there is room for opportunistic S. cerevisiae strains to cause a health issues. Fungal diseases are challenging to treat because, unlike bacteria, the fungal are eukaryotes. Antibiotics only target prokaryotic cells, whereas compounds that kill fungi also harm the mammalian host. Small differences between mammalian and fungal cells regarding genes and proteins sequence and function make finding a drug target more challenging. Recently, Chitin synthase has been considered as a promising target for antifungal drug development as it is absent in mammals. In S. cerevisiae, CHS3, a class IV chitin synthase, produces 90% of the chitin and essential for cell growth. CHS3 from the trans-Golgi network to the plasma membrane requires assembly of the exomer complex (including proteins cargo such as CHS5, CHS6, Bach1, and Arf1). In this work, we performed SELEX (Systematic Evolution of Ligands by EXponential enrichment) as high throughput virtual screening of the RCSB data bank to find an aptamer as potential inhibit of the class IV chitin synthase of S. cerevisiae. Among all the candidates, G-rich VEGF (GVEGF) aptamer (PDB code: 2M53) containing locked sugar parts was observed as potential inhibitor of the assembly of CHS5-CHS6 exomer complex a subsequently block the chitin biosynthesis pathway as an effective anti-fungal. It was suggested from the simulation that an assembly of exomer core should begin CHS5-CHS6, not from CHS5-bach1. It is notable that secondary structures of CHS6 and Bach1 was observed very similar, but they have only 25% identity at the amino acid sequence that exhibited different features in exomer assembly.Graphical abstractGraphical abstract for this article
  • DFT studies on global parameters, antioxidant mechanism and molecular
           docking of amlodipine besylate
    • Abstract: Publication date: Available online 14 March 2019Source: Computational Biology and ChemistryAuthor(s): K.P. Safna Hussan, M. Shahin Thayyil, Vijisha K. Rajan, K. MuraleedharanABSTRACTAmlodipine besylate (AMB) is a synthetic dihydropyridine calcium channel blocker with antihypertensive and anti-anginal effects. Quantum computational investigations on AMB were done using DFT/B3LYP/6-311++G (d, p) level of theory, to study the molecular structural properties, nonlinear properties and antioxidant properties of AMB. The electrophilic and nucleophilic sites along with complete NBO analysis helps to locate the intermolecular electronic interactions and their stabilization energies. Complete NBO analysis was additionally done to locate the intermolecular electronic interactions and their stabilization energies. Charge distributions of Mulliken population, NBO and MEP are correlated. Also, the antioxidant properties of AMB were assessed to check whether these antioxidant effects contribute to the effects of antioxidant therapy. Further, the molecular docking studies of these compounds demonstrated a good selectivity profile with Monoamine oxidase B with better binding affinity and confirms AMB is a potent antioxidant.Graphical abstractGraphical abstract for this article
  • Design, synthesis and in silico study of pyridine based 1,3,4-oxadiazole
           embedded hydrazinecarbothioamide derivatives as potent anti-tubercular
    • Abstract: Publication date: Available online 13 March 2019Source: Computational Biology and ChemistryAuthor(s): Ajay N. Ambhore, Sonali S. Kamble, Shuddhodan N. Kadam, Rahul D. Kamble, Madhav J. Hebade, Shrikant V. Hese, Milind V. Gaikwad, Rohan J. Meshram, Rajesh N. Gacche, Bhaskar S. Dawane Development of novel, safe and effective drug candidates combating the emerging drug resistance has remained a major focus in the mainstream of anti-tuberculosis research. Here, we inspired to design and synthesize series of new pyridin-4-yl-1,3,4-oxadiazol-2-yl-thio-ethylidene-hydrazinecarbothioamide derivatives as potential anti-tubercular agents. The anti-tubercular bioactive assay demonstrated that the synthesized compounds exhibit potent anti-tubercular activity (MIC = 3.9 to 7.81 µg/mL) in comparison with reference drugs Rifampicin and Isoniazid.We employed pharmacophore probing approach for the identification of CYP51 as a possible drug target for the synthesized compounds. To understand the preferable binding mode, the synthesized molecules were docked onto the active site of Sterol 14 α-demethylases (CYP51) target. From the binding free energy of the docking results it was revealed that the compounds were effective CYP51 inhibitors and acts as antitubercular agent.Graphical abstractGraphical abstract for this article
  • Assessment of Structurally and Functionally High-risk nsSNPs Impacts on
           Human Bone Morphogenetic Protein Receptor Type IA (BMPR1A) by
           Computational Approach
    • Abstract: Publication date: Available online 12 March 2019Source: Computational Biology and ChemistryAuthor(s): Jahirul Islam, Rimon Parves, Shafi Mahmud, Fahmida Alam Tithi, Abu Reza BMPR1A (BMP type 1 receptor) is a transmembrane cell-surface receptor also known as ALK3 (activin-like kinases-3) encodes for a type I serine/threonine kinase receptor and a member of the transforming growth-factor β–receptor (TGF-β) super family. The BMPR1A has a significant interaction with BMP-2 for protein activity and also has a low affinity with growth and differentiation factor 5 (GDF5); positively regulates chondrocyte differentiation. The genetic variations can alter the structure and function of the BMPR1A gene that causes several diseases such as juvenile polyposis syndrome or hereditary cancer-predisposing syndrome. The current study was carried out to identify potential deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in BMPR1A by implementing different computational algorithms such as SIFT, PolyPhen2, SNAP2, PROVEAN, PhD-SNP, SNPs&GO, nsSNPAnalyzer, and P-Mut. From 205 nsSNPs in BMPR1A, 7 nsSNPs (C76Y, C124R, C124Y, C376Y, R443C, R480W, and W487R) were predicted as deleterious in 8 prediction algorithms. The Consurf analysis showed that selected 7 nsSNPs were present in the highly conserved regions. Molecular dynamics simulation analysis also performed to explore conformational changes in the variant structure with respect to its native structure. According to the MDS result, all variants flexibility and rigidity were unbalanced, which may alter the structural and functional behavior of the native protein. Although, three nsSNPs i.e., C124R, C376Y, and R443C have already been reported in patients associated with JPS, but their structural and functional molecular studies remain uncharacterized. Therefore, the findings of this study can provide a better understanding of uncharacterized nsSNPS and to find their association with disease susceptibility and also facilitate to the researchers for designing or developing the target dependent drugs.Graphical Graphical abstract for this article
  • On the characterization of novel biologically active steroids: Selection
           of lipophilicity models of newly synthesized steroidal derivatives by
           classical and non-parametric ranking approaches
    • Abstract: Publication date: Available online 9 March 2019Source: Computational Biology and ChemistryAuthor(s): Milica Ž. Karadžić, Strahinja Z. Kovačević, Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Anamarija I. Mandić In this paper, the guidelines for the interpretation of the results of quantitative structure-retention relationship (QSRR) modeling, comparison and assessment of the established models, as well as the selection of the best and most consistent QSRR model were presented. Various linear and non-linear chemometric regression techniques were used to build QSRR models for chromatographic lipophilicity prediction of a series of triazole, tetrazole, toluenesulfonylhydrazide, nitrile, dinitrile and dione steroid derivatives. Linear regression (LR) and multiple linear regression (MLR) were used as linear techniques, while artificial neural networks (ANNs) were applied as non-linear modeling techniques. Generated models were statistically evaluated applying different approaches for model comparison and ranking. Two non-parametric methods (generalized pair correlation method – GPCM and sum of ranking differences – SRD) were used for model ranking and assessment of the best model for chromatographic lipophilicity prediction using experimentally obtained logk values and row average as a reference ranking. Both, GPCM and SRD, provided highly similar model choice regardless on a different background. These results are in agreement with the classical approach.Graphical abstractGraphical abstract for this article
  • Cross-Kingdom Gene Regulation via miRNAs of Hypericum Perforatum (St.
           John’s wort) Flower Dietetically Absorbed: An In Silico Approach to
           Define Potential Biomarkers for Prostate Cancer
    • Abstract: Publication date: Available online 28 February 2019Source: Computational Biology and ChemistryAuthor(s): Sercan Ergün Prostate cancer (PCa) is the most frequent type of cancer in men. Hypericum perforatum (H. Perforatum) extract (HPE) administration provides remarkable decrease of PCa development. H. perforatum contains 7 conserved miRNAs (Hyp-miR-156a, Hyp-miR-156b, Hyp-miR-166, Hyp-miR-390, Hyp-miR-394, Hyp-miR-396 and Hyp-miR-414) with different targets. In this study, we aimed to investigate cross-kingdom gene regulation via miRNAs of H. perforatum flower dietetically absorbed in manner of an in silico approach to define potential biomarkers for PCa. psRNATarget database was used to find human genes targeted by 7 pre-defined H. perforatum miRNAs. We defined the mostly affected gene families from these miRNAs as ZNF, TMEM, SLC and FAM gene families. GeneMANIA database was used to define the most affected genes (TMEM41B and SLC4A7) from these 7 miRNAs. cBioPortal database was used to define alteration frequencies of TMEM41B and SLC4A7 on different types of PCa and to measure the mutual interaction potency and significance of co-occurence in PCa. This analysis showed that neuroendocrine prostate cancer (NEPC) had the highest total mutation frequency (22 %) of TMEM41B and SLC4A7 genes. Also, TMEM41B and SLC4A7 genes had an average 2.1 % pathway change potential among all different types of PCa. Moreover, TMEM41B and SLC4A7 gene pair was found significantly co-occurrent in PCa (p 
  • Molecular evolution of the plant Eceriferum1 genes involved in aliphatic
           hydrocarbon production
    • Abstract: Publication date: Available online 26 February 2019Source: Computational Biology and ChemistryAuthor(s): Hongliang Wang, Xinzhi Ni, Karen Harris-Shultz The Arabidopsis Eceriferum1 (CER1) protein is a decarbonylase that converts fatty acid metabolites into alkanes. Alkanes are components of waxes in the plant cuticle, a waterproof barrier serving to protect land plants from both biotic and abiotic stimuli. CER1 enzymes can be used to produce alternative and sustainable hydrocarbons in eukaryotic systems. In this report we identified 193 CER1 and 128 CER3 sequences from 56 land plants respectively. CER1 and CER3 proteins have high amino acid similarity and both are involved in alkane synthesis in Arabidopsis. The common homologues of CER1 and CER3 genes were identified in three species of chlorophytes, which may be one of the earliest plant taxa that possess CER1 and CER3 genes. To facilitate potential applications, the 3-dimensional structure and conserved motifs of CER1 proteins were also characterized. CER1 and CER3 proteins are structurally similar, but CER1 proteins have more conserved histidine-containing motifs common to fatty acid hydroxylases and stearoyl-CoA desaturases. There was no significant loss or gain of protein motifs after ancient and recent duplications, suggesting that varied properties of CER1 proteins may be associated with less-conserved regions. Among 56 land plants, the codon-based assessments of selection modes reveled that neither entire proteins nor individual amino acids of CER1 proteins were significantly subjected to positive selection, indicating that CER1 proteins are highly conserved throughout evolution.Graphical abstractGraphical abstract for this article
  • Combined QSAR/QSPR and Molecular Docking Study on Fluoroquinolones to
           Reduce Biological Enrichment
    • Abstract: Publication date: Available online 25 February 2019Source: Computational Biology and ChemistryAuthor(s): Xiaohui Zhao, Yuanyuan Zhao, Zhixing Ren, Yu Li With the aim of reducing the adverse effects of fluoroquinolones in the environment, a complete design and screening system for the low biological enrichment and high photodegradabilities of 29 fluoroquinolones was established through a three-dimensional quantitative structure–activity relationship (3D-QSAR) model and molecular docking. The interaction mechanisms of the fluoroquinolones with Gram-negative bacteria (DNA gyrase in Escherichia coli) and Gram-positive bacteria (Topoisomerase IV in Staphylococcus aureus) were also evaluated. Consequently, the 3D-QSAR model showed that the 3- and 18-positions of the fluoroquinolones strongly affected their biological enrichment, and that the introduction of electropositive or hydrophobic groups at these positions reduced the logarithm of the octanol-water partition coefficient. Using nadifloxacin as a template, 23 derivatives with lower biological enrichment than nadifloxacin (decreased by 30.12%–94.18%) were designed. Meanwhile, the photodegradabilities of 15 derivatives were increased compared with nadifloxacin. Finally, the further screening by molecular docking of nadifloxacin and the above 15 derivatives with DNA gyrase and Topoisomerase IV showed that 13 of the derivatives had lower biological enrichment (decreased by 0.30%–16.76%) than nadifloxacin in the bacteria.Graphical abstractGraphical abstract for this article
  • Identification of Potential AMPK Activator by Pharmacophore Modeling,
           Molecular Docking and QSAR Study
    • Abstract: Publication date: Available online 23 February 2019Source: Computational Biology and ChemistryAuthor(s): Yingying Li, Jiale Peng, Penghua Li, Haibo Du, Yaping Li, Xingyong Liu, Li Zhang, Liang-Liang Wang, Zhili Zuo AMP-activated protein kinase (AMPK) plays a major role in maintaining cellular energy homeostasis by sensing and responding to AMP/ADP concentrations relative to ATP. AMPK has attracted widespread attention as a potential therapeutic target for metabolic diseases such as cancer and cardiovascular diseases. The structure-based 3D pharmacophore model was developed based on the training set. The best pharmacophore model Hypo5 was proposed and validated using a decoy set, an external test set. Hypo5, with the correlation coefficient value of 0.936, cost difference value of 112.08 and low RMS value of 1.63, includes a ionizable positive, a hydrogen bond donor, a hydrogen bond acceptor and two hydrophobic features, which showed a high goodness of fit and enrichment factor. Thus it was used as a 3D query to find potential activator from the SPECS Database. Then the ADMET descriptors were used to filter all of 158 screening molecules. The 41 filtering compounds were subsequently subjected to molecular docking and Quantitative structure–activity relationship (QSAR) analysis. Finally, the compound H2 was picked out from those filtering compounds based on the receptor-ligand interaction analysis and the prediction of the QSAR models. And then it was submitted for molecular dynamics (MD) simulations to explore the stability of complex. The result indicates that the candidate could be considered a potential AMPK activator.Graphical Graphical abstract for this article
  • Restricted-Derestricted Dynamic Bayesian Network inference of
           Transcriptional Regulatory Relationships among Genes in Cancer
    • Abstract: Publication date: Available online 21 February 2019Source: Computational Biology and ChemistryAuthor(s): Emmanuel S. Adabor, George K. Acquaah-Mensah Understanding transcriptional regulatory relationships among genes is important for gaining etiological insights into diseases such as cancer. To this end, high-throughput biological data have been generated through advancements in a variety of technologies. These rely on computational approaches to discover underlying structures in such data. Among these computational approaches, Bayesian networks (BNs) stand out because their probabilistic nature enables them to manage randomness in the dynamics of gene regulation and experimental data. Feedback loops inherent in networks of regulatory relationships are more tractable when enhancements to BNs are applied to them. Here, we propose Restricted-Derestricted dynamic BNs with a novel search technique, Restricted-Derestricted Greedy Method, for such tasks. This approach relies on the Restricted-Derestricted Greedy search technique to infer transcriptional regulatory networks in two phases: restricted inference and derestricted inference. An application of this approach to real data sets reveals it performs favourably well compared to other existing well performing dynamic BN approaches in terms of recovering true relationships among genes. In addition, it provides a balance between searching for optimal networks and keeping biologically relevant regulatory interactions among variables.Graphical abstractGraphical abstract for this article
  • Discovery of potent IRAK-4 inhibitors as potential anti-inflammatory and
           anticancer agents using structure-based exploration of IRAK-4
           pharmacophoric space coupled with QSAR analyses
    • Abstract: Publication date: Available online 20 February 2019Source: Computational Biology and ChemistryAuthor(s): Mohammad A. Khanfar, Saja Alqtaishat Interleukin-1 Receptor-Associated Kinase 4 (IRAK-4) has an important role in immunity, inflammation, and malignancy. The significant role of IRAK-4 makes it an interesting target for the discovery and development of potent small molecule inhibitors. In the current study, multiple linear regression -based QSAR analyses coupled with structure-based pharmacophoric exploration was applied to reveal the structural and physiochemical properties required for IRAK-4 inhibition. Manually built pharmacophoric models were initially validated with receiver operating characteristic curve, and best-ranked models were subsequently integrated in QSAR analysis along with other physiochemical descriptors. The pharmacophore model, selected using the statistically optimum QSAR equation, was implied as a 3D-search filter to mine the National Cancer Institute database for novel IRAK-4 inhibitors. Whereas the associated QSAR model prioritized the bioactivities of captured hits for in vitro evaluation. Experimental validation identified several potent IRAK-4 inhibitors of novel structural scaffolds. The most potent captured hit exhibited an IC50 value of 157 nM.Graphical abstractGraphical abstract for this article
  • Time-Dependent AI-Modeling of the Anticancer Efficacy of Synthesized
           Gallic Acid Analogues
    • Abstract: Publication date: Available online 16 February 2019Source: Computational Biology and ChemistryAuthor(s): Lubna Sherin, Ayesha Sohail, Shahida Shujaat Background/AimMain objective of this study is mapping of the anticancer efficacy of synthesized gallic acid analogues using modeling and artificial intelligence (AI) over a large range of concentrations and exposure times to explore the underline mechanisms of drug action and draw careful inferences regarding drug response heterogeneity.MethodsTwo series of gallic acid derivatives i.e. esters and amides have been synthesized and characterized by FTIR, NMR and mass spectrometry. The compounds have been tested in vitro for their anticancer activity against wild type human ovarian cancer cell line A2780, prostate cancer cell line PC3 and normal human fibroblast cells 3T3. To completely characterize optimal anticancer activity, a comprehensive model using piecewise recursive Hill model is used to quantitatively assess the in vitro anticancer effect of the tested compounds as a function of concentration and exposure time for periods ranging from 24 to 72 hrs. A robust artificial intelligence approach i.e. the “Support Vector Machine (SVM) Learning Algorithm” is adopted to utilize the data obtained at different temporal values, to identify compounds that trail forecasting algorithm.ResultsAll the synthesized analogues were found biocompatible. Significantly low EC50 values indicated that tested compounds have potent anticancer activity against A2780 cell line in comparison to PC3 cells where only few compounds generated same impact at almost 200 times high dose. On the basis of EC50 values, compounds 7 h, 7 m, 9c, 9b, 7c, 7b and 7 g were identified as the most active anticancer agent against A2780. Three major patterns of drug response heterogeneity were observed for different compounds in the form of multiple Hill graphs and shallow slopes. The anticancer efficiency of the compounds was verified using Machine learning SVM regression learner algorithm. For compounds 7a, 7b, 7e, 7 g, 7o, 7 r, 9b, 9e-9 g higher accuracy was found in predicted and experimentally obtained end point potency in terms of % viability.ConclusionsPharmacodynamics modeling of anticancer potential of the synthesized compounds revealed that drug efficacy and response heterogeneity could be modulated by changing the exposure time to optimize therapeutic impact. Combining experimental results with AI based drug action forecasting, compounds 7b, 7 g, and 9b may be tested further as potent anticancer agent for in vivo studies. This approach may serve a useful tool for extrapolation of in vitro results for generating lead compounds in in vivo and preclinical studies.
  • Identification and Structural Characterization of Deleterious
           Non-Synonymous Single Nucleotide Polymorphisms in the Human SKP2 Gene
    • Abstract: Publication date: Available online 15 February 2019Source: Computational Biology and ChemistryAuthor(s): S.M. Zahid Hosen, Raju Dash, Md. Junaid, Sarmistha Mitra, Nurul Absar In SCF (Skp, Cullin, F-box) ubiquitin-protein ligase complexes, S-phase kinase 2 (SKP2) is one of the major players of F-box family, that is responsible for the degradation of several important cell regulators and tumor suppressor proteins. Despite of having significant evidence for the role of SKP2 on tumorgenesis, there is a lack of available data regarding the effect of non-synonymous polymorphisms. In this communication, the structural and functional consequences of non-synonymous single nucleotide polymorphisms (nsSNPs) of SKP2 have been reported by employing various computational approaches and molecular dynamics simulation. Initially, several computational tools like SIFT, PolyPhen-2, PredictSNP, I-Mutant 2.0 and ConSurf have been implicated in this study to explore the damaging SNPs. In total of 172 nsSNPs, 5 nsSNPs were identified as deleterious and 3 of them were predicted to be decreased the stability of protein. Guided from ConSurf analysis, P101 L (rs761253702) and Y346C (rs755010517) were categorized as the highly conserved and functional disrupting mutations. Therefore, these mutations were subjected to three dimensional model building and molecular dynamics simulation study for the detailed structural consequences upon the mutations. The study revealed that P101 L and Y346C mutations increased the flexibility and changed the structural dynamics. As both these mutations are located in the most functional regions of SKP2 protein, these computational insights might be helpful to consider these nsSNPs for wet-lab confirmatory analysis as well as in rationalizing future population based studies and future structure based drug design against SKP2.Graphical Graphical abstract for this article
  • Comparison between Echinococcus granulosus sensu stricto (G1) and E.
           canadensis (G6) mitochondrial genes (cox1 and nad1) and their related
           protein models using experimental and bioinformatics analysis
    • Abstract: Publication date: Available online 5 February 2019Source: Computational Biology and ChemistryAuthor(s): Seyed Mahmoud Sadjjadi, Mohammad Ebrahimipour, Fatemeh Sadat Sadjjadi BackgroundCystic echinococcosis (CE) as a zoonotic parasitic disease, remains a health challenge in many parts of the world. There are different species of Echinococcus granulosus sensu lato with different pathogenicity and host preferences.Different procedures have been applied for characterization of Echinococcus taxa in which two mitochondrial genes, cox1 and nad1 have been used more common. They have been able to differentiate E. granulosus sensu stricto and E. canadensis species in different hosts. The affinity of E. granulosus sensu stricto and E. canadensis species for localizing different organs seems to be different. To what such affinity and related pathogenicity could be related, is not known, so far. Bioinformatics analysis may be helpful to interpret such difference by investigating the genes and their related protein models between different species infecting human and animals. The current work was designed to study the differences between E. granulosus s.s. and E. canadensis species mitochondrial genes (cox1 and nad1) and related protein models of CE cysts by experimental and bioinformatics analysis.Materials and methodsDifferent human and animal CE cysts were collected and their DNA was extracted and sequenced based on their cox1 and nad1 genes. In order to determine the E. granulosus s.s. and E. canadensis species of the samples, BLAST analysis was performed on sequenced genes. Three sequences were selected for analysis and were deposited in GenBank. Moreover, the sequence number of KT988116.1 which belonged to E. canadensis from our already deposited in GenBank was also selected. Alignment and phylogenetic analysis were performed on the sequences using BioEdit and MEGA7 software. The raw sequences of translated proteins belonged to the mentioned genes were obtained from Protein database in NCBI. The secondary structure was determined by PSIPRED Protein Sequence Analysis Workbench. The tertiary models of COX1 and NAD1 proteins in both genotypes were constructed using Modeler 9.12 software and their physicochemical features were computed using ProtParam tool in ExPASY server.ResultsBLAST analysis on sequenced genes showed that the samples belonged to E. granulosus s.s. and E. canadensis species. These sequences were deposited in GenBank with accession numbers: JN579173.1, KF437811.1, and KY924632.1.The results showed that proteins of COX1 of E. granulosus s.s., COX1of E. canadensis, NAD1of E. granulosus s.s. and NAD1of E. canadensis species, consisted of 135, 122, 120 and 124 amino acids, respectively. The aligned sequences of translated proteins belonged to COX1 and NAD1 enzymes in E. granulosus s.s. and E. canadensis species were different; such that alignment COX1 sequence between E. granulosus s.s. and E. canadensis species showed that amino acids were different in 6 positions. This difference for NAD1 sequences were different in 19 positions. The secondary structure determined by PSIPRED showed differences in coil, strand and helix chains in COX1 and NAD1 proteins in E. granulosus s.s. and E. canadensis species. Comparison between three-dimensional structures (3D) of COX1 protein model in E. granulosus s.s. and E. canadensis species demonstrated an additional helix with two conserved iron binding sites in the COX1 protein of E. granulosus s.s. species.ConclusionE. granulosus s.s. and E. canadensis species differences are reflected in two important proteins: COX1 and NAD1. These differences are demonstrable in the 3D structure of proteins of both strains. So, the present study is adding to our understanding of the difference in molecular sequences between the E. granulosus s.s. (G1) and E. canadensis (G6) which may be used for interpreting the difference between the pathogenicity and localization affinity in these two important helminthic zoonosis.Graphical abstractGraphical abstract for this article
  • Gp41 Inhibitory Activity Prediction of Theaflavin Derivatives Using
           Ligand/Structure-Based Virtual Screening Approaches
    • Abstract: Publication date: Available online 5 February 2019Source: Computational Biology and ChemistryAuthor(s): Tahereh Mostashari-Rad, Lotfollah Saghaei, Afshin Fassihi Gp41 and its conserved hydrophobic groove on the NHR region is one of the attractive targets in the design of HIV-1 entry inhibitory agents. This hydrophobic pocket is very critical for the progression of HIV and host cell fusion. In this study different ligand-based (structure similarity search) and structure-based (molecular docking and molecular dynamic simulation) methods were performed in a virtual screening procedure to select the best compounds with the most probable HIV-1 gp41 inhibitory activities. In silico pharmacokinetics and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties filtration also was considered to choose the compounds with best drug-like properties. The results of molecular docking and molecular dynamic simulations of the final selected compounds showed suitable stabilities of their complexes with gp41. The final selected hits could have better pharmacokinetics properties than the the template compound, theaflavin digallate (TF3), a naturally-originated potent gp41 inhibitor.Graphical abstractGraphical abstract for this article
  • Synthesis, monoamine oxidase inhibitory activity and computational study
           of novel isoxazole derivatives as potential antiparkinson agents
    • Abstract: Publication date: Available online 29 January 2019Source: Computational Biology and ChemistryAuthor(s): Neetu Agrawal, Pradeep Mishra Monoamine oxidase (MAO) enzymes are one of the most promising targets for the treatment of neurological disorders. A series of phenylisoxazole carbohydrazides was designed, synthesized and screened for both MAO-A and MAO-B inhibition using Amplex Red assays. None of the compounds inhibited the MAO-A activity while most of them significantly inhibited MAO-B in the micromolar to nanomolar range. Among them, the compound N’-(4-methylbenzylidene)-5-phenylisoxazole-3-carbohydrazide (6c) exhibited the most potent inhibitory activity towards MAO-B. Enzyme kinetic studies revealed the reversible and competitive nature of compound 6c towards MAO-B inhibition. The results of the enzyme inhibition assay were in agreement with molecular docking study, in which compound 6c displayed a strong binding affinity for MAO-B with a docking score of -10.98 Kcal/mol. In order to explore the neuroprotective effect of compound 6c, MPTP-induced mouse model for Parkinson's disease was used, and motor behavioural assessment of experimental animals was carried out. The compound 6c was able to significantly prevent the MPTP-induced neurotoxicity as revealed by improvement in gait behaviour in footprint test and increase in grip strength score in horizontal wire test. Thus, phenylisoxazole carbohydrazides can be promising leads in the development of potent, selective and reversible MAO-B inhibitors for the treatment of Parkinson's disease.Graphical abstractGraphical abstract for this article
  • Synthesis, computational quantum chemical study, in silico ADMET and
           molecular docking analysis, in vitro biological evaluation of a novel
           sulfur heterocyclic thiophene derivative containing 1,2,3-triazole and
           pyridine moieties as a potential human topoisomerase IIα inhibiting
           anticancer agent
    • Abstract: Publication date: Available online 28 January 2019Source: Computational Biology and ChemistryAuthor(s): S. Murugavel, C. Ravikumar, G. Jaabil, Ponnuswamy AlagusundaramABSTRACTComputational quantum chemical study and biological evaluation of a synthesized novel sulfur heterocyclic thiophene derivative containing 1,2,3-triazole and pyridine moieties namely BTPT [2-(1-benzyl-5-methyl-1H-1,2,3-triazol-4-yl)-6-methoxy-4-(thiophen-2-yl) pyridine] was presented in this study. The crystal structure was determined by SCXRD method. For the title compound BTPT, spectroscopic characterization like 1H NMR, 13C NMR, FTIR, UV-Vis were carried out theoretically by computational DFT method and compared with experimental data. Druglikeness parameters of BTPT were found through in silico pharmacological ADMET properties estimation. The molecular docking investigation was performed with human topoisomerase IIα (PDB ID:1ZXM) targeting ATP binding site. in vitro cytotoxicity activity of BTPT/doxorubicin were examined by MTT assay procedure against three human cancer cell lines A549, PC-3, MDAMB-231 with IC50 values of 0.68/0.70, 1.03/0.77 and 0.88/0.98 µM, respectively. Our title compound BTPT reveals notable cytotoxicity against breast cancer cell (MDAMB-231), moderate activity with human lung cancer cell (A-549) and less inhibition with human prostate cancer cell (PC-3) compared to familiar cancer medicine doxorubicin. From the results, BTPT could be observed as a potential candidate for novel anticancer drug development process.Graphical abstractGraphical abstract for this article
  • Identification of Coenzyme-Binding Proteins with Machine Learning
    • Abstract: Publication date: Available online 28 January 2019Source: Computational Biology and ChemistryAuthor(s): Yong Liu, Zhiwei Kong, Tao Ran, Alfredo Sahagun-Ruiz, Zhixiong He, Chuanshe Zhou, Zhiliang Tan The coenzyme-binding proteins play a vital role in the cellular metabolism processes, such as fatty acid biosynthesis, enzyme and gene regulation, lipid synthesis, particular vesicular traffic, and β-oxidation donation of acyl-CoA esters. Based on the theory of Star Graph Topological Indices (SGTIs) of protein primary sequences, we proposed a method to develop a first classification model for predicting protein with coenzyme-binding properties. In order to simulate the properties of coenzyme-binding proteins, we created a dataset containing 2,897 proteins, among 456 proteins functioned as coenzyme-binding activity. The SGTIs of peptide sequence were calculated with S2SNet application. We used the SGTIs as inputs to several classification techniques with a machine learning software - Weka. A Random Forest classifier based on 3 features of the embedded and non-embedded graphs was identified as the best predictive model for coenzyme-binding proteins. This model developed was with the true positive (TP) rate of 91.7%, false positive (FP) rate of 7.6%, and Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.971. The prediction of new coenzyme-binding activity proteins using this model could be useful for further drug development or enzyme metabolism researches.Graphical abstractGraphical abstract for this article
  • Identification and Characterization of Differentially Expressed Genes in
           Type 2 Diabetes using in silico Approach
    • Abstract: Publication date: Available online 24 January 2019Source: Computational Biology and ChemistryAuthor(s): Manoj Kumar Gupta, Ramakrishna VaddeABSTRACTDiabetes mellitus is clinically characterized by hyperglycemia. Though many studies have been done to understand the mechanism of Type 2 Diabetes (T2D), however, the complete network of diabetes and its associated disorders through polygenic involvement is still under debate. The present study designed to re-analyze publicly available T2D related microarray raw datasets present in GEO database and T2D genes information present in GWAS catalog for screening out differentially expressed genes (DEGs) and identify key hub genes associated with T2D. T2D related microarray data downloaded from Gene Expression Omnibus (GEO) database and re-analysis performed with in house R packages scripts for background correction, normalization and identification of DEGs in T2D. Also retrieved T2D related DEGs information from GWAS catalog. Both DEGs lists were grouped after removal of overlapping genes. These screened DEGs were utilized further for identification and characterization of key hub genes in T2D and its associated diseases using STRING, WebGestalt and Panther databases. Computational analysis reveal that out of 99 identified key hub gene candidates from 348 DEGs, only four genes (CCL2, ELMO1, VEGFA and TCF7L2) along with FOS playing key role in causing T2D and its associated disorders, like nephropathy, neuropathy, rheumatoid arthritis and cancer via p53 or Wnt signaling pathways. MIR-29, and MAZ_Q6 are identified potential target microRNA and TF along with probable drugs alprostadil, collagenase and dinoprostone for the key hub gene candidates. The results suggest that identified key DEGs may play promising roles in prevention of diabetes.
  • Identification of Glucosyl-3-Phosphoglycertae Phosphatase as a Novel Drug
           Target against Resistant Strain of Mycobacterium tuberculosis (XDR1219) by
           using Comparative Metabolic Pathway Approach
    • Abstract: Publication date: Available online 24 January 2019Source: Computational Biology and ChemistryAuthor(s): Reaz Uddin, Noor-ul-Ain Zahra, Syed Sikander Azam Tuberculosis (TB) is a major global health challenge. It has been afflicting human for thousands of years and is still severely affecting a huge population. The etiological agent of the disease is Mycobacterium tuberculosis (MTB) that survives in the human host in latent, dormant, and non-replicative state by evading the immune system. It is one of the leading causes of infection related death worldwide. The situation is exacerbated by the massive increase in the resistant strains such as multi-drug resistant TB (MDR-TB) and extensive drug-resistant TB (XDR-TB). The resistance is as severe that it resulted in failure of the current chemotherapy regimens (i.e. anti-tubercular drugs). It is therefore imperative to discover the new anti-tuberculosis drug targets and their potential inhibitors. Current study has made the use of in silico approaches to perform the comparative metabolic pathway analysis of the MTBXDR1219 with the host i.e. H. sapiens. We identified several metabolic pathways which are unique to pathogen only. By performing subtractive genomic analysis 05 proteins as potential drug target are retrieved. This study suggested that the identified proteins are essential for the bacterial survival and non-homolog to the host proteins. Furthermore, we selected glucosyl-3-phosoglycerate phosphatase (GpgP, EC out of the 05 proteins for molecular docking analysis and virtual screening. The protein is involved in the biosynthesis of methylglucose lipopolysaccharides (MGLPs) which regulate the biosynthesis of mycolic acid. Mycolic acid is the building block of the unique cell wall of the MTB which is responsible for the resistance and pathogenicity. A relatively larger library consisting of 10,431 compounds was screened using AutoDock Vina to predict the binding modes and to rank the potential inhibitors. No potent inhibitor against MTB GpgP has been reported yet, therefore ranking of compounds is performed by making a comparison with the substrate i.e. glucosyl-3-phosphoglycerate. The obtained results provide the understanding of underlying mechanism of interactions of ligands with protein. Follow up study will include the study of the Protein-Protein Interactions (PPIs), and to propose the potential inhibitors against them.Graphical abstractGraphical abstract for this article
  • In silico identification and evaluation of new Trypanosoma cruzi
           Trypanothione Reductase (TcTR) inhibitors obtained from Natural Products
           Database of the Bahia Semi-Arid region (NatProDB)
    • Abstract: Publication date: Available online 22 January 2019Source: Computational Biology and ChemistryAuthor(s): Vinícius Guimarães da Paixão, Samuel Silva da Rocha Pita Trypanosoma cruzi Trypanothione Reductase (TcTR) is one of the therapeutic targets studied in the development of new drugs against Chagas' disease. Due to its biodiversity, Brazil has several compounds of natural origin that were not yet properly explored in drug discovery. Therefore, we employed the Virtual Screening against TcTR aiming to discover new inhibitors from the Natural Products Database of the Bahia Semi-Arid region (NatProDB). This database has a wide chemical diversity favoring the discovery of new chemical entities. Subsequently, we analyzed the best docking conformations using self-organizing maps (AuPosSOM) aiming to verify their interaction sites at TcTR. Finally, the Pred-hERG, the Aggregator Advisor, the FAF-DRUGS and the pkCSM results allowed us to evaluate, respectively, the cardiotoxicity, aggregation capacity, presence of false positives (PAINS) and its toxicity. Thus, we selected three molecules that could be tested in in vitro assays in the hope that the computational results reported here would favor the development of new anti-chagasic drugs.Graphical abstractGraphical abstract for this article
  • Development and Design of Novel Cardiovascular Therapeutics Based on Rho
           Kinase Inhibition - In Silico Approach
    • Abstract: Publication date: Available online 21 January 2019Source: Computational Biology and ChemistryAuthor(s): Snezana Ćirić Zdravković, Milan Pavlović, Svetlana Apostlović, Goran Koraćević, Sonja Šalinger Martinović, Dragana Stanojević, Dušan Sokolović, Aleksandar M. Veselinović Rho kinases, one of the best-known members of the serine/threonine (Ser/Thr) protein kinase family, can be used as target enzymes for the treatment of many diseases such as cancer or multiple sclerosis, and especially for the treatment of cardiovascular diseases. This study presents QSAR modeling for a series of 41 chemical compounds as Rho kinase inhibitors based on the Monte Carlo method. QSAR models were developed for three random splits into the training and test set. Molecular descriptors used for QSAR modeling were based on the SMILES notation and local invariants of the molecular graph. The statistical quality of the developed model, including robustness and predictability, was tested with different statistical approaches and satisfying results were obtained. The best calculated QSAR model had the following statistical parameters: r2 = 0.8825 and q2 = 0.8626 for the training set and r2 = 0.9377 and q2 = 0.9124 for the test set. Novel statistical metric entitled as the index of ideality of correlation was used for the final model assessment, and the obtained results were 0.6631 for the training and 0.9683 for the test set. Molecular fragments responsible for the increases and decreases of the studied activity were defined and they were further used for the computer-aided design of new compounds as potential Rho kinase inhibitors. The final assessment of the developed QSAR model and designed inhibitors was achieved with the application of molecular docking. An excellent correlation between the results from QSAR and molecular docking studies was obtained.Graphical abstractGraphical abstract for this article
  • Novel 2,4-disubstituted Quinazolines as Cytotoxic Agents and JAK2
    • Abstract: Publication date: Available online 21 January 2019Source: Computational Biology and ChemistryAuthor(s): Siva Jyothi Buggana, ManiChandrika Paturi, Harathi Perka, Deepak Reddy Gade, VVS Rajendra PrasadABSTRACTRecent studies reported the involvement of JAK2/STAT3 pathway in various solid tumours including breast, ovarian, prostate and lung cancers. Clinical literature also reported the lowered burden in breast and ovarian cancers by targeting JAK2 pathway. In this study, a series of novel 2,4-substituted quinazolines (2a-2 j and 3a-3 j) were synthesized and were evaluated for their cytotoxicity against human breast cancer (MDA-MB-231) and ovarian cancer (SK-O-V3) cell lines using MTT assay. Moderate to good in vitro cytotoxic potentials of the newly synthesized molecules were reported against selected human cancer cell lines. Among the tested molecules, compound 3b showed the good cytotoxic activity against MD-AMB-231 (10.1 ± 0.51 μM). In vitro JAK2 inhibition studies elucidated the mechanistic profile of the derivatives with moderate percentage of inhibition. Compound 3b and 3d were reported with 35.4% and 34.2% inhibition of JAK2 protein. SAR studies suggest that the larger hydrophobic aromatic nucleus with hydrophilic linkage could probably increase the cytotoxic and JAK2 potentials and hydroxyl or nitro substitution could add more benefit. Molecular dynamics simulation studies with JAK2-3b, and JAK2-3d complexes elucidated the conformational changes. With the reported bioactivities of these derivatives, further studies on the derivatization could elucidate the broader cytotoxic potentials.Graphical Graphical abstract for this article
  • Editors’ note concerning a development of database analysis by Smith
           and Stein (2009)
    • Abstract: Publication date: Available online 3 August 2009Source: Computational Biology and ChemistryAuthor(s): M.J.C. Crabbe, A.K. Konopka
  • Protein structure prediction from inaccurate and sparse NMR data using an
           enhanced genetic algorithm
    • Abstract: Publication date: Available online 19 January 2019Source: Computational Biology and ChemistryAuthor(s): Md. Lisul Islam, Swakkhar Shatabda, Mahmood A. Rashid, M.G.M. Khan, M Sohel Rahman Nuclear Magnetic Resonance Spectroscopy (most commonly known as NMR Spectroscopy) is used to generate approximate and partial distances between pairs of atoms of the native structure of a protein. To predict protein structure from these partial distances by solving the Euclidean distance geometry problem from the partial distances obtained from NMR Spectroscopy, we can predict three-dimensional (3D) structure of a protein. In this paper, a new genetic algorithm is proposed to efficiently address the Euclidean distance geometry problem towards building 3D structure of a given protein applying NMR's sparse data. Our genetic algorithm uses (i) a greedy mutation and crossover operator to intensify the search; (ii) a twin removal technique for diversification in the population; (iii) a random restart method to recover from stagnation; and (iv) a compaction factor to reduce the search space. Reducing the search space drastically, our approach improves the quality of the search. We tested our algorithms on a set of standard benchmarks. Experimentally, we show that our enhanced genetic algorithms significantly outperforms the traditional genetic algorithms and a previously proposed state-of-the-art method. Our method is capable of producing structures that are very close to the native structures and hence, the experimental biologists could adopt it to determine more accurate protein structures from NMR data.
  • Conformational and docking studies of acyl homoserine lactones as a robust
           method to investigate bioactive conformations
    • Abstract: Publication date: Available online 19 January 2019Source: Computational Biology and ChemistryAuthor(s): Laurent Soulère, Yves Queneau A method aiming at investigating possible bioactive conformations of acyl homoserine lactone (AHL) quorum sensing (QS) modulators is established. The method relies on the exhaustive conformational analysis of AHLs by varying torsion angles around the amide group then on the selection of the closest conformation to those known from co-crystallized XRD data of AHL-receptor complexes. These latter are then docked as rigid ligand within the receptor binding site, leading to conformations, interactions with binding site residues which are highly consistent as compared with the data arising from XRD studies.The method is first validated using AHLs for which XRD data of their complexes with their cognate receptor are available, then extended to examples for which the binding mode is still unknown.Three compounds were used to validate the method: hexanoyl homoserine lactone (HHL) as an example of autoinducer, 3-oxo-butanoyl homoserine lactone (OBHL), as a representative model of 3-oxo-AHLs, and 4-(4-chlorophenoxy)butanoyl homoserine lactone (CPOBHL) as an example of a QS inhibitor. The conformational analysis of these three compounds to their cognate protein (TraR, SdiA, LasR and CviR) provides the data which enable the next rigid docking step. Further rigid docking of the closest conformations compared to the known bioactive ones within the binding sites allows to recover the expected binding mode with high precision (atomic RMSD 
  • An alignment-free measure based on physicochemical properties of amino
           acids for protein sequence comparison
    • Abstract: Publication date: Available online 18 January 2019Source: Computational Biology and ChemistryAuthor(s): Yunxiu Zhao, Xiaolong Xue, Xiaoli Xie Sequence comparison is an important topic in bioinformatics. With the exponential increase of biological sequences, the traditional protein sequence comparison methods — the alignment methods become limited, so the alignment-free methods are widely proposed in the past two decades. In this paper, we considered not only the six typical physicochemical properties of amino acids, but also their frequency and positional distribution. A 51-dimensional vector was obtained to describe the protein sequence. We got a pairwise distance matrix by computing the standardized Euclidean distance, and discriminant analysis and phylogenetic analysis can be made. The results on the Influenza A virus and ND5 datasets indicate that our method is accurate and efficient for classifying proteins and inferring the phylogeny of species.Graphical abstractGraphical abstract for this article
  • Integrating pharmacophore mapping, virtual screening, density functional
           theory, molecular simulation towards the discovery of novel apolipoprotein
           (apoE ε4) inhibitors
    • Abstract: Publication date: Available online 11 January 2019Source: Computational Biology and ChemistryAuthor(s): Surabhi Johari, Ashwani Sharma, Subrata Sinha, Aparoop Das AimAn integrated protocol of virtual screening involving molecular docking, pharmacophore probing, and simulations was established to identify small novel molecules targeting crucial residues involved in the variant apoE ε4 to mimic its behavior as apoE2 thereby eliminating the amyloid plaque accumulation and facilitating its clearance.Materials and MethodsAn excellent ligand-based and structure-based approach was made to identify common pharmacophoric features involving structure-based docking with respect to apoE ε4 leading to the development of apoE ε4 inhibitors possessing new scaffolds. An effort was made to design multiple-substituted triazine derivatives series bearing a novel scaffold. A structure-based pharmacophore mapping was developed to explore the binding sites of apoE ε4 which was taken into consideration. Subsequently, virtual screening, ADMET, DFT searches were at work to narrow down the proposed hits to be forwarded as a potential drug likes candidates. Further, the binding patterns of the best-proposed hits were studied and were forwarded for molecular dynamic simulations of 10 ns for its structural optimization.ResultsSelectivity profile for the most promising candidates was studied, revealing significantly C13 and C15 to be the most potent compounds. The proposed hits can be forwarded for further study against apoE ε4 involved in neurological disorder Alzheimer’s.
  • Rational design of hyper-glycosylated human luteinizing hormone analogs (A
           Bioinformatics Approach)
    • Abstract: Publication date: Available online 3 January 2019Source: Computational Biology and ChemistryAuthor(s): M. Shafaghi, A. Shabani, Z. Minuchehr Glycoengineering is a recently used approach to extend serum half-life of valuable protein therapeutics. One aspect of glycoengineering is to introduce new N-glycosylation site (Asn-X-Thr/Ser, where X ≠ Pro) into desirable positions in the peptide backbone, resulting in the generation of hyper-glycosylated protein. In this study, human luteinizing hormone (LH) was considered for identification of the suitable positions for the addition of new N-linked glycosylation sites. A rational in silico approach was applied for prediction of structural and functional alterations caused by changes in amino acid sequence. As the first step, we explored the amino acid sequence of LH to find out desirable positions for introducing Asn or/and Thr to create new N-glycosylation sites. This exploration led to the identification of 38 potential N-glycan sites, and then the four acceptable ones were selected for further analysis. Three-dimensional (3D) structures of the selected analogs were generated and examined by the model evaluation methods. Finally, two analogs with one additional glycosylation site were suggested as the qualified analogs for hyper-glycosylation of the LH, which can be considered for further experimental investigations. Our computational strategy can reduce laborious and time-consuming experimental analyses of the analogs.Graphical abstractGraphical abstract for this article
  • Complete genome sequence of Bacillus megaterium JX285 isolated from
           Camellia oleifera rhizosphere
    • Abstract: Publication date: Available online 31 December 2018Source: Computational Biology and ChemistryAuthor(s): Fang-liang Huang, Yang Zhang, Lin-ping Zhang, Shu Wang, Ye Feng, Nian-hang Rong Bacillus megaterium strain JX285, isolated from rhizosphere red soil sample, can solubilize inorganic phosphorus, which increases the amount of available phosphorus and promotes plant growth. To investigate the mechanisms underlying phosphate solubilization, we sequenced the entire genome of B. megaterium strain JX285 (CGMCC 1.1621), which comprises a circular chromosome of 5,066,463 bp and seven plasmids of 167,030, 128,297, 60,905, 134,795, 9,598, 37,455, and 6,332 bp, respectively. The whole genome sequence includes 5,948 protein-coding genes, 124 tRNAs, and 29 rRNAs, and has been deposited at Genbank/EMBL/DDBJ with accession numbers CP018874–CP018881. We detected genes associated with organic acid production, which may be vital for phosphate conversion. Furthermore, phosphatase-encoding genes were also detected. The information embedded in the genome will assist in studying the mechanisms of phosphate solubilization. In conclusion, analysis of the JX285 genome will further our knowledge regarding this strain and may contribute to its biotechnological application.Graphical abstractGraphical abstract for this articleBacillus megaterium strain JX285, isolated from rhizosphere red soil sample, can solubilize inorganic phosphorus, which increases the amount of available phosphorus and promotes plant growth. We studied the genome of this strain and compared it with other bacteria.
  • Physicochemical property based computational scheme for classifying DNA
           sequence elements of Saccharomyces cerevisiae
    • Abstract: Publication date: Available online 26 December 2018Source: Computational Biology and ChemistryAuthor(s): Atul Kumar Jaiswal, Annangarachari Krishnamachari GenerationE of huge “omics” data necessitates the development and application of computational methods to annotate the data in terms of biological features. In the context of DNA sequence, it is important to unravel the hidden physicochemical signatures. For this purpose, we have considered various sequence elements such as promoter, ACS, LTRs, telomere, and retrotransposon of the model organism Saccharomyces cerevisiae. Contributions due to di-nucleotides play a major role in studying the DNA conformation profile. The physicochemical parameters used are hydrogen bonding energy, stacking energy and solvation energy per base pair. Our computational study shows that all sequence elements in this study have distinctive physicochemical signatures and the same can be exploited for prediction experiments. The order that we see in a DNA sequence is dictated by biological regions and hence, there exists role of dependency in the sequence makeup, keeping this in mind we are proposing two computational schemes (a) using a windowing block size procedure and (b) using di-nucleotide transitions. We obtained better discriminating profile when we analyzed the sequence data in windowing manner. In the second novel approach, we introduced the di-nucleotide transition probability matrix (DTPM) to study the hidden layer of information embedded in the sequences. DTPM has been used as weights for scanning and predictions. This proposed computational scheme incorporates the memory property which is more realistic to study the physicochemical properties embedded in DNA sequences. Our analysis shows that the DTPM scheme performs better than the existing method in this applied region. Characterization of these elements will be a key to genome editing applications and advanced machine learning approaches may also require such distinctive profiles as useful input features.
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