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  Subjects -> COMPUTER SCIENCE (Total: 2110 journals)
    - ANIMATION AND SIMULATION (31 journals)
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COMPUTER SCIENCE (1241 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: 26)
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: 4)
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: 11)
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: 21)
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: 10)
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: 37)
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: 30)
Advanced Science Letters     Full-text available via subscription   (Followers: 12)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 9)
Advances in Artificial Intelligence     Open Access   (Followers: 16)
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: 17)
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: 24)
Advances in Human-Computer Interaction     Open Access   (Followers: 21)
Advances in Materials Science     Open Access   (Followers: 17)
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: 53)
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: 10)
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: 7)
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: 8)
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: 33)
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: 165)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6)
arq: Architectural Research Quarterly     Hybrid Journal   (Followers: 7)
Artifact     Open Access   (Followers: 3)
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 Computer Science and Information Technology     Open Access   (Followers: 2)
Asian Journal of Control     Hybrid Journal  
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: 7)
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: 5)
Biodiversity Information Science and Standards     Open Access   (Followers: 1)
Bioinformatics     Hybrid Journal   (Followers: 361)
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: 38)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 50)
British Journal of Educational Technology     Hybrid Journal   (Followers: 187)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 13)
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: 5)
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: 9)
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: 15)
Communication Methods and Measures     Hybrid Journal   (Followers: 15)
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: 57)
Communications of the Association for Information Systems     Open Access   (Followers: 16)
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: 9)
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: 2)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 13)
Computational Chemistry     Open Access   (Followers: 3)
Computational Cognitive Science     Open Access   (Followers: 4)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 11)
Computational Economics     Hybrid Journal   (Followers: 10)
Computational Geosciences     Hybrid Journal   (Followers: 17)
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: 37)
Computer     Full-text available via subscription   (Followers: 114)
Computer Aided Surgery     Open Access   (Followers: 6)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 16)

        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]
  • Kinetic flux vector splitting scheme for solving non-reactive
           multi-component flows
    • Abstract: Publication date: December 2019Source: Computational Biology and Chemistry, Volume 83Author(s): Muhammad Saqib, Attia Rabbani, Ubaid Ahmed Nisar, Waqas Ashraf, Shamsul Qamar This paper is about multi-component flow. There is no doubt that multi-component flow has a wide range of applications, specially in aerospace it plays a vital role during reentry of space ship into earth's atmosphere thats why it cannot be neglected for a proper vehicle design. In this paper one- and two-dimensional homogenous multi-component flow models are numerically investigated by using a high resolution splitting scheme and this scheme is known as Kinetic Flux Vector Splitting scheme. This scheme preserves positivity conditions and resolves shocks, rarefaction and contact discontinuity. The scheme is based on splitting of flux functions. Moreover Runge-Kutta time stepping technique with MUSCL-type initial reconstruction is used to guarantee higher order accurate solution. This work is first done by Qamar and Warnecke (2004) for the homogeneous multi-component flow equations using central scheme, here we investigate the same work using kinetic flux vector splitting scheme (KFVS) and compared the results with central scheme to verify the efficiency of studied scheme.
  • The initial stage of structural transformation of Aβ42 peptides from the
           human and mole rat in the presence of Fe2+ and Fe3+: Related to
           Alzheimer's disease
    • Abstract: Publication date: Available online 20 September 2019Source: Computational Biology and ChemistryAuthor(s): Mohammad Vahed, Aaron Sweeney, Hiroshi Sirasawa, Majid VahedABSTRACTThe early stage of secondary structural conversion of amyloid beta (Aβ) to misfolded aggregations is a key feature of Alzheimer's disease (AD). Under normal physiological conditions, Aβ peptides can protect neurons from the toxicity of highly concentrated metals. However, they become toxic under certain conditions. Under conditions of excess iron, amyloid precursor proteins (APP) become overexpressed. This subsequently increases Aβ production. Experimental studies suggest that Aβ fibrillation (main-pathway) and amorphous (off-pathway) aggregate formations are two competitive pathways driven by factors such as metal binding, pH and temperature. In this study, we performed molecular dynamic (MD) simulations to examine the initial stage of conformational transformations of human Aβ (hAβ) and rat Aβ (rAβ) peptides in the presence of Fe2+ and Fe3+ ions. Our results demonstrated that Fe2+ and Fe3+ play key roles in Aβs folding and aggregation. Fe3+ had a greater effect than Fe2+on Aβs’ folding during intermolecular interactions and subsequently, had a greater effect in decreasing structural diversity. Fe2+ was observed to be more likely than Fe3+ to interact with nitrogen atoms from the residues of imidazole rings of His. rAβ peptides are more energetically favorable than hAβ for intermolecular interactions and amorphous aggregations. We concluded that most hAβ structures were energetically unfavorable. However, hAβs with intermolecular β-sheet formations in the C-terminal were energetically favorable. It is notable that Fe2+ can change the surface charge of hAβ. Furthermore, Fe3+ can promote C-terminal folding by binding to Glu22 and Ala42, and by forming stable β-sheet formations on the C-terminal. Fe3+ can also pause the main-pathway by inducing random aggregations.Graphical abstractGraphical abstract for this article
  • Evaluation of drug candidature: In silico ADMET, binding interactions with
           CDK7 and normal cell line studies of potentially anti-breast cancer
    • Abstract: Publication date: Available online 14 September 2019Source: Computational Biology and ChemistryAuthor(s): Prakash Bansode, R. Anantacharya, Maruti Dhanavade, Subodh Kamble, Sagar Barale, Kailas Sonawane, Nayak D. Satyanarayan, Gajanan Rashinkar We have recently explored novel class of potentially anti-breast cancer active enamidines in which four molecules 4a-c and 4 h showed higher anticancer activity compared to standard drug doxorubicin. As a part of extension of this work, we have further evaluated in silico cheminformatic studies on bioactivity prediction of synthesized series of enamidines using mole information. The normal cell line study of four lead compounds 4a-c and 4 h against African green monkey kidney vero strain further revealed that the compounds complemented good selectivity in inhibition of cancer cells. The in silico bioactivity and molecular docking studies also revealed that the compounds have significant interactions with the drug targets. The results reveal that enamidine moieties are vital for anti-breast cancer activity as they possess excellent drug-like characteristics, being potentially good inhibitors of cyclin dependent kinases7 (CDK7).Graphical abstractGraphical abstract for this article
  • Integration of core hopping, quantum-mechanics, molecular mechanics
           coupled binding-energy estimations and dynamic simulations for
           fragment-based novel therapeutic scaffolds against Helicobacter pylori
    • Abstract: Publication date: Available online 13 September 2019Source: Computational Biology and ChemistryAuthor(s): Chiranjeevi Pasala, Sudheer Kumar Katari, Ravina Madhulitha Nalamolu, R. Bitla Aparna, Umamaheswari Amineni The cascade of complications by Helicobacter pylori including extra-gastric and peptic ulcers to gastric cancer imposes a salient cause of cancer death globally. Adverse drug reactions and burgeoned genetically diverse resistant strains create a big barrier in the treatment, thereby demanding novel proof-of-concept ligands and breakthrough medicines. Hence, as a follow-up of the previous proteomics study against 53 H. pylori strains, KdsB was identified as a vital conserved-target enzyme. Herein, the rational therapeutic-design strategies exploiting for such a hidden cryptic inhibitor were utilized in lead-optimization campaigns through shape screening, the powerful scaffold-hopping, rigid-receptor, quantum-polarized ligand and induced-fit docking techniques coupled with estimating molecular-mechanics energies (ΔGbind) through generalized-Born and surface-area-continuum solvation. Variable-dielectric-Surface-Generalized Born, a novel energy model and physics-based corrections for bond-interactions and ADME/Tox predictions led to yield improved eight therapeutic chemical entities with positive synthesizability scores (0-1). Long-range molecular dynamics (300 ns) simulations revealed stability of leads. Significant computational findings with better competitive binding-strengths than experimental ligands could pave the best choice for selecting better leads as it warrants and filter false-positives based on the consensus of scaffolds interactions and suggesting that designed novel class of KdsB-antagonist molecules may dysfunction the target and stimulate new insights for developing effectual medical interventions.Graphical abstractGraphical abstract for this article
  • VWF, CXCL8 and IL6 might be potential druggable genes for acute coronary
           syndrome (ACS)
    • Abstract: Publication date: Available online 13 September 2019Source: Computational Biology and ChemistryAuthor(s): Gu Jinxia, Hong Zhu, Dayong Zhu, Ming Li, Mochao Xiao, Dongxia Yan, Shaohui Shen ObjectiveAcute coronary syndrome (ACS) is currently a leading cause of morbidity and mortality worldwide. This study aimed to screen critical genes and miRNAs involved in ACS.Materials and MethodsMicroarray data (access number GSE19339) was downloaded from Gene Expression Omnibus (GEO) database. After data preprocessing, we screened the differentially expressed genes (DEGs) using limma package and subsequently performed enrichment analysis using DAVID tool. The protein-protein interaction (PPI) network and transcription factor (TF)-miRNA-target gene regulatory network were visualized using Cytoscape software. Finally, the drug-gene interactions were predicted using DGIdb database.ResultsA total of 425 DEGs were identified in ACS samples compared with healthy control samples. Functional enrichment analysis showed that DEGs were mainly involved in angiogenesis, inflammatory response and PI3K-Akt signaling pathway. IL6 and VEGFA were key nodes in PPI network. In addition, hsa-miR-29, hsa-miR-1, NFIC, NFKB1 and RELA were identified as key factors in TF-miRNA-target gene network. Finally, the prediction results revealed that VWF, CXCL8 and IL6 had higher degree than other genes.ConclusionIL6 and VEGFA might play major roles in ACS progression. Two miRNAs (hsa-miR-29 and hsa-miR-1) and three TFs (NFIC, NFKB1 and RELA) were critical genes involved in pathological process of ACS. VWF, CXCL8 and IL6 might be potential druggable genes for ACS therapy.Graphical abstractGraphical abstract for this article
  • Target prediction of candidate miRNAs from Oryza sativa for
           silencing the RYMV genome
    • Abstract: Publication date: Available online 13 September 2019Source: Computational Biology and ChemistryAuthor(s): Basit Jabbar, Muhammad Shahzad Iqbal, Anicet A. Batcho, Idrees A. Nasir, Bushra Rashid, Tayyab Husnain, Robert J. Henry In order to maintain a consistent supply of rice globally, control of pathogens affecting crop production is a matter of due concern. Rice yellow mottle virus(RYMV) is known to cause a variety of symptoms which can result in reduced yield. Four ORFs can be identified in the genome of RYMV encoding for P1 (ORF1), Polyprotein (processed to produce VPg, protease, helicase, RdRp4) (ORF2), putative RdRp (ORF3) and capsid/coat protein (ORF4). This research was aimed at identifying genome encoded miRNAs of O. sativa that are targeted to the genome of Rice Yellow Mottle Virus (RYMV). A consensus of four miRNA target prediction algorithms (RNA22, miRanda, TargetFinder and psRNATarget) was computed, followed by calculation of free energies of miRNA-mRNA duplex formation. A phylogenetic tree was constructed to portray the evolutionary relationships between RYMV strains isolated to date. From the consensus of algorithms used, a total of seven O. sativa miRNAs were predicted and conservation of target site was finally evaluated. Predicted miRNAs can be further evaluated by experiments involving the testing of the success of in vitro gene silencing of RYMV genome; this can pave the way for development of RYMV resistant rice varieties in the future.Graphical abstractGraphical abstract for this article
  • Simulating the Monty Hall problem in a DNA sequencing machine
    • Abstract: Publication date: Available online 11 September 2019Source: Computational Biology and ChemistryAuthor(s): Noam Mamet, Gil Harari, Adva Zamir, Ido Bachelet The Monty Hall problem is a decision problem with an answer that is surprisingly counter-intuitive yet provably correct. Here we simulate and prove this decision in a high-throughput DNA sequencing machine, using a simple encoding. All possible scenarios are represented by DNA oligonucleotides, and gameplay decisions are implemented by sequencing these oligonucleotides from specific positions, with a single run simulating more than 12,000,000 independent games. This work highlights high-throughput DNA sequencing as a new tool that could extend existing capabilities and enable new encoding schemes for problems in DNA computing.Graphical abstractGraphical abstract for this article
  • Molecular evolution of the internal transcribed spacers in red oaks
           (Quercus sect. Lobatae)
    • Abstract: Publication date: Available online 11 September 2019Source: Computational Biology and ChemistryAuthor(s): M. Lucía Vázquez Previous studies of the Internal Transcribed Spacers of the nuclear ribosomal DNA (ITS) in sections Quercus (white oaks), Protobalanus (intermediate or golden cup oaks), Cerris (Cerris oaks), and Ilex (Ilex oaks) suggest that ITS regions undergo full concerted evolution in oaks; however, ITS evolution patterns in red oaks (section Lobatae) are unknown due to scant representation in published work. To determine whether full concerted evolution occurs in red oaks, the purpose of this study was to examine ITS sequences from 40 red oak species. The results show incomplete concerted evolution and the presence of three ITS ribotypes of lengths 505, 609, 601 bp, hereafter referred to as ITS-S (small), I ITS-M (medium), and ITS-L (large), respectively. Thirty species had only one ribotype (ITS-M), nine species had two ribotypes (different combinations of ITS-L, ITS-M, and ITS-S), and only one species had all three ribotypes. Furthermore, examination of these three ribotypes showed that only ITS-M is putatively functional and ITS-L and ITS-S are pseudogenes. Bayesian analysis strongly supported (100%) two pseudogenes clades but provided weak support for the monophyly of a putative functional clade (ITS-M); moreover, within the “functional” clade, species relationships were uncertain and, in most cases, sequences from the same species failed to group together. The results of the current study suggest that ITS may not be appropriate for phylogeny reconstruction of red oaks due to low levels of interspecific variation and incomplete concerted evolution.
  • Exploring the effect of aplidin on low molecular weight protein tyrosine
           phosphatase by molecular docking and molecular dynamic simulation study
    • Abstract: Publication date: Available online 7 September 2019Source: Computational Biology and ChemistryAuthor(s): Ying-Zhan Sun, Wu Jing-Wei, Lu Xin-Hua, Ying Ma, Run-Ling Wang The low molecular weight protein tyrosine phosphatase (LMW-PTP) could regulate many signaling pathways, and it had drawn attention as a potential target for cancer. As previous report has indicated that the aplidin could inhibit the LMW-PTP, and thus, the relevant cancer caused by the abnormal regulation of the LMW-PTP could be remission. However, the molecular mechanism of inhibition of the LMW-PTP by the aplidin had not been fully understood. In this study, various computational approaches, namely molecular docking, MDs and post-dynamic analyses were utilized to explore the effect of the aplidin on the LMW-PTP. The results suggested that the intramolecular interactions of the residues in the two sides of the active site (Ser43-Ala55 and Pro121-Asn134) and the P-loop region (Leu13-Ser19) in the LMW-PTP was disturbed owing to the aplidin, meanwhile, the π-π interaction between Tyr131 and Tyr132 might be broken. The Asn15 might be the key residue to break the residues interactions. In a word, this study may provide more information for understanding the effect of inhibition of the aplidin on the LMW-PTP.Graphical Graphical abstract for this articleThe changes of the interactions of the key residues (Asn15) between theLMW-PTP and LMW-PTP/aplidin systems.
  • Biomarkers for ischemic stroke subtypes: A protein-protein interaction
    • Abstract: Publication date: Available online 7 September 2019Source: Computational Biology and ChemistryAuthor(s): Loo Keat Wei, Leong Shi Quan According to the Trial of Org 10172 in Acute Stroke Treatment, ischemic stroke is classified into five subtypes. However, the predictive biomarkers of ischemic stroke subtypes are still largely unknown. The utmost objective of this study is to map, construct and analyze protein-protein interaction (PPI) networks for all subtypes of ischemic stroke, and to suggest the predominant biological pathways for each subtypes. Through 6285 protein data retrieved from PolySearch2 and STRING database, the first PPI networks for all subtypes of ischemic stroke was constructed. Notably, F2 and PLG were identified as the critical proteins for large artery atherosclerosis (LAA), lacunar, cardioembolic, stroke of other determined etiology (SOE) and stroke of undetermined etiology (SUE). Gene ontology and DAVID analysis revealed that GO:0030193 regulation of blood coagulation and GO:0051917 regulation of fibrinolysis were the important functional clusters for all the subtypes. In addition, inflammatory pathway was the key etiology for LAA and lacunar, while FOS and JAK2/STAT3 signaling pathways might contribute to cardioembolic stroke. Due to many risk factors associated with SOE and SUE, the precise etiology for these two subtypes remained to be concluded.
  • Anticancer SAR Establishment and Novel Accruing Signal Transduction Model
           of Drug Action Using Biscoumarin Scaffold
    • Abstract: Publication date: Available online 6 September 2019Source: Computational Biology and ChemistryAuthor(s): Mayank, Ashutosh Singh, Navneet Kaur, Neha Garg, Narinder Singh In this paper, we have established methylenebis(4-hydroxy-2H-chromen-2-one) as a promising anticancer scaffold with kinesin spindle protein (KSP) inhibitory activity under malignant condition. A series of biscoumarin derivatives (MN1 to MN30) with different substituent were synthesized, and their anticancer activity was explored. Six biscoumarin derivatives that were found activewere further selected to formulate organic nanoparticles (ONPs). Anticancer activity of both the forms (viz conventional and ONPs) was compared. MN30 was found most potent whereby MN10 showed good anticancer activity in both,i.e., conventional and ONP form; the structural activity relationship (SAR) study has been established. Computational investigation revealed biscoumarin scaffold as a suitable pharmacophore to bind against KSP protein. Molecular dynamics simulation studies revealed protein-ligand stability and dynamic behaviourof biscoumarin-KSP complex. Finally, accruing signal transduction model was formulated to explain the observed MTT trend of conventional and ONP form. The model seems useful towards solving population specific varied results of chemotherapeutic agents. According to the model, MN10 and MN30 derivatives have good pharmacodynamics inertia and therefore,both the molecules were able to provide dose-dependent cytotoxic results.Graphical abstractGraphical abstract for this articleSynthesized biscoumarin derivatives as anticancer lead were explored, showing promising anticancer potential by apoptosis induction. Kinesin spindle protein as its target receptor was finally established.
  • Integrative approaches to reconstruct regulatory networks from multi-omics
           data: A review of state-of-the-art methods
    • Abstract: Publication date: December 2019Source: Computational Biology and Chemistry, Volume 83Author(s): Nisar Wani, Khalid Raza Data generation using high throughput technologies has led to the accumulation of diverse types of molecular data. These data have different types (discrete, real, string, etc.) and occur in various formats and sizes. Datasets including gene expression, miRNA expression, protein–DNA binding data (ChIP-Seq/ChIP-ChIP), mutation data (copy number variation, single nucleotide polymorphisms), annotations, interactions, and association data are some of the commonly used biological datasets to study various cellular mechanisms of living organisms. Each of them provides a unique, complementary and partly independent view of the genome and hence embed essential information about the regulatory mechanisms of genes and their products. Therefore, integrating these data and inferring regulatory interactions from them offer a system level of biological insight in predicting gene functions and their phenotypic outcomes. To study genome functionality through regulatory networks, different methods have been proposed for collective mining of information from an integrated dataset. We survey here integration methods that reconstruct regulatory networks using state-of-the-art techniques to handle multi-omics (i.e., genomic, transcriptomic, proteomic) and other biological datasets.Graphical abstractGraphical abstract for this article
  • Natural compounds as potential Hsp90 inhibitors for breast
           cancer-Pharmacophore guided molecular modelling studies
    • Abstract: Publication date: December 2019Source: Computational Biology and Chemistry, Volume 83Author(s): Shailima Rampogu, Shraddha Parate, Saravanan Parameswaran, Chanin Park, Ayoung Baek, Minky Son, Yohan Park, Seok Ju Park, Keun Woo Lee Breast cancer is one of the major impediments affecting women globally. The ATP-dependant heat shock protein 90 (Hsp90) forms the central component of molecular chaperone machinery that predominantly governs the folding of newly synthesized peptides and their conformational maturation. It regulates the stability and function of numerous client proteins that are frequently upregulated and/or mutated in cancer cells, therefore, making Hsp90 inhibition a promising therapeutic strategy for the development of new efficacious drugs to treat breast cancer. In the present in silico investigation, a structure-based pharmacophore model was generated with hydrogen bond donor, hydrogen bond acceptor and hydrophobic features complementary to crucial residues Ala55, Lys58, Asp93, Ile96, Met98 and Thr184 directed at inhibiting the ATP-binding activity of Hsp90. Subsequently, the phytochemical dataset of 3210 natural compounds was screened to retrieve the prospective inhibitors after rigorous validation of the model pharmacophore. The retrieved 135 phytocompounds were further filtered by drug-likeness parameters including Lipinski’s rule of five and ADMET properties, then investigated via molecular docking-based scoring. Molecular interactions were assessed using Genetic Optimisation for Ligand Docking program for 95 drug-like natural compounds against Hsp90 along with two clinical drugs as reference compounds – Geldanamycin and Radicicol. Docking studies revealed three phytochemicals are better than the investigated clinical drugs. The reference and hit compounds with dock scores of 48.27 (Geldanamycin), 40.90 (Radicicol), 73.04 (Hit1), 72.92 (Hit2) and 68.12 (Hit3) were further validated for their binding stability through molecular dynamics simulations. We propose that the non-macrocyclic scaffolds of three identified phytochemicals might aid in the development of novel therapeutic candidates against Hsp90-driven cancers.Graphical abstractGraphical abstract for this article
  • Predictive Biomarkers of Colorectal Cancer
    • Abstract: Publication date: Available online 3 September 2019Source: Computational Biology and ChemistryAuthor(s): Di Ding, Siyu Han, Hui Zhang, Ye He, Ying Li Colorectal cancer is one of the top leading causes of cancer mortality worldwide, especially in China. However, most of the current treatments are invasive and can only be applied to very few cancers. The earlier a malignant tumor is diagnosed, the higher the patient's survival rate. In this study, we proposed a computational framework to identify highly-reliable and easierly-detectable biomarkers capable of secreting into blood, urine and saliva by integrating transcriptomics and proteomics data at the system biology level. First, a large number of transcriptome data were processed to identify candidate biomarkers for colorectal cancer. Second, three classified models are constructed to predict biomarkers for colorectal cancer capable of secreting into blood, urine and saliva, which are effective disease diagnosis media to facilitate clinical screening. Then biological functions and molecular mechanisms of the candidate biomarkers of colorectal cancer are inferred utilizing multi-source biological knowledge and literature mining. Furthermore, the classification power of different combinations of candidate biomarkers is verified by machine learning models. In addition, the targeted drugs of the predicted biomarkers are further analyzed to provide assistance for clinical treatment of colorectal cancer. In this paper, our proposed computational model not only provides the effective candidate biomarkers ESM1, CTHRC1, AZGP1 for colorectal cancer capable of secreting into blood, urine and saliva, but also helps to understand the molecular mechanism of colorectal cancer. This computational framework can span the huge gap between transcriptome and proteomics, which can easily be applied to the biomarker research for other types of tumor.Graphical abstractGraphical abstract for this article
  • Construction and analysis of a diabetic nephropathy related
           protein-protein interaction network reveals nine critical and functionally
           associated genes
    • Abstract: Publication date: Available online 30 August 2019Source: Computational Biology and ChemistryAuthor(s): Wenhao Jiang, Zheng Zhang, Yan Sun, Yajuan Zhang, Luyu Zhang, Handeng Liu, Rui Peng Diabetic nephropathy (DN) is one of the common diabetic complications, but the mechanisms are still largely unknown. In this study, we constructed a DN related protein-protein interaction network (DNPPIN) on the basis of RNA-seq analysis of renal cortices of DN and normal mice, and the STRING database. We analyzed DNPPIN in detail revealing nine critical proteins which are central in DNPPIN, and contained in one network module which is functionally enriched in ribosome, nucleic acid binding and metabolic process. Overall, this study identified nine critical and functionally associated protein-coding genes concerning DN. These genes could be a starting point of future research towards the goal of elucidating the mechanisms of DN pathogenesis and progression.Graphical abstractGraphical abstract for this article
  • Multi-targeted potential of Pittosporum senacia Putt.: HPLC-ESI-MSn
           analysis, in silico docking, DNA protection, antimicrobial, enzyme
           inhibition, anti-cancer and apoptotic activity
    • Abstract: Publication date: Available online 29 August 2019Source: Computational Biology and ChemistryAuthor(s): Mohamad Fawzi Mahomoodally, Carene Picot-Allain, M. Hosenally, Asli Ugurlu, Adriano Mollica, Azzurra Stefanucci, E.J. Llorent-Martínez, Mehmet Cengiz Baloglu, Gokhan Zengin Pittosporum senacia (PS) Putt. (Pittosporaceae), indigenous to the Mascarene Islands, is a common ingredient in traditional medicines. However, there is currently a dearth of studies to validate some of these traditional claims. Given the broad traditional uses of PS against several diseases, we aimed to provide a comprehensive insight into the biological and chemical profile of P. senacia. The antioxidant, enzyme inhibitory activity, anticancer, and phytochemical composition of the methanolic extract of P. senacia leaf extracts were studied. The possible interaction and binding mode of the most abundant phytochemicals were studied via in silico docking experiments on tyrosinase and α-glucosidase. The mechanism behind the cytotoxic property of P. senacia extract for MDA-MB-231 was also examined using different methods including 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) cell viability test checking apoptosis-associated genes, and wound healing assays. Twenty-six compounds were identified, of which caffeoylquinic acid derivatives, ferulic acid derivative, cinnamoylquinic acid derivative and two other polyphenols (oleuropeine and isoramnetin glucoside) being abundant, have been tested using in silico studies, against α-glucosidase and tyrosinase. The extract (IC50 = 118.8 μg/ml) exhibited time and dose dependent anti-proliferative effect on human breast cancer cell line, MDA-MB-231. According to the expression profile of apoptosis inhibitors and apoptosis promoters genes, expression of Bax and Bak genes were significantly increased compared to Bcl-2 and Birc5 genes. Based on wound healing analysis, cell migration was inhibited after the application of the plant extract. The present findings suggested that PS might be a good candidate as sources of bioactive compounds for designing functional applications.Graphical abstractGraphical abstract for this article
  • Modeling, Stability and the Activity Assessment of Glutathione Reductase
           from Streptococcus Thermophilus; Insights from the In-silico Simulation
    • Abstract: Publication date: Available online 28 August 2019Source: Computational Biology and ChemistryAuthor(s): Nazanin Gholampour-Faroji, Razieh Farazmand, Jafar Hemmat, Aliakbar Haddad-Mashadrizeh Antioxidant enzymes (AEs) are the main parts of the natural barriers of the body which deactivate the oxidant factors. To discover and understand their structures and functions will deserve a deeper investigation. Accordingly, as an AE of probiotic strains, glutathione reductase of Streptococcus thermophilus (GRst), is characterized and modeled by in-silico methods. The investigation indicated the physicochemical properties of the enzyme and estimated its half-life of being more than 10 hours. The analysis revealed that the enzyme is composed of 86 strands, 123 helices, and 241 random coils. Homology modeling of the GRst led to the construction of the enzyme’s 3D model that 62% of which is analogous to the glutathione reductase of Escherichia Coli (GRec), and which is qualitatively high in terms of Molpdf, ERRAT, Verify-3D and Ramachandran scores Moreover, the structural stability of the model was substantiated within 10 and 20 ns at 400 and 300 K, respectively. Interestingly, these data showed that the enzyme is more stable than GRec at 400 K. In other words, the active cavity of the constructed model is characteristic of 38 amino acid residues within 4 Å around the NADPH and GSSG as corresponding ligands of GRst. Noteworthy herein is the fact that, CYS40 and CYS45 are specified as the active site residues of this enzyme. Furthermore, the interaction assays of the model support its antioxidant capability which is even more than that of GRec.In general, these data provide a new model of AEs being inclusive of high antioxidant capacity and thermostability.Graphical abstractGraphical abstract for this article
  • Compositional features and codon usage pattern of TP63 gene
    • Abstract: Publication date: Available online 28 August 2019Source: Computational Biology and ChemistryAuthor(s): Supriyo Chakraborty, Parvin A Barbhuiya, Gulshana A Mazumder, Bornali Deb, Arif Uddin The tumor protein p63encoded by the gene TP63 acts as a homologue of p53 protein. TP63 gene is the transformation factor with two initiation sites for transcriptional process and is related with stress, signal transduction and cell cycle control. The biasness in the preference of a few codons more frequently over other synonymous codons is the codon usage bias (CUB). Natural selection and mutational pressure are the two prime evolutionary forces acting on CUB. Here, the bioinformatic based analysis was performed to investigate the base distribution and CUB of TP63transcript variants (isoforms) as no work was performed earlier. Analysis of compositional features revealed variation in base content across TP63 gene isoforms and the GC content was more than 50%, indicating GC richness of its isoforms. The mean effective number of codons (ENC), a measure of CUB, was 51.83, i.e. overall CUB of TP63 gene was low. Among 13 isoforms of TP63 gene, nature selected against the CTA codon in 8 isoforms and favored five over-represented (RSCU > 1.6) codons namely CTG, CAG, ATC, AAC and GCC during evolution. Correlation between overall nucleotide composition and its 3rd codon position revealed that both mutational pressure and natural selection moulded its CUB. Further, the correlation between ENC and aromaticity depicted that variation of CUB was related to the degree of aromaticity of p63 protein.Graphical abstractGraphical abstract for this article
  • In vitro Antitumor Activity, ADME-Tox and 3D-QSAR of Synthesized and
           Selected Natural Styryl Lactones
    • Abstract: Publication date: Available online 23 August 2019Source: Computational Biology and ChemistryAuthor(s): Vladimir R. Vukic, Davor M. Loncar, Dajana V. Vukic, Lidija R. Jevric, Goran Benedekovic, Jovana Francuz, Vesna Kojic, Milica Z. Karadzic Banjac, Velimir Popsavin Prostate cancer is a common cause of death in men and a novel treating methods should be developed. In order to find a new drug for prostate cancer, a series of novel conformationally constrained analogues of (+)-goniofufurone and 7-epi-(+)-goniofufurone, as well as the newly synthesized styryl lactones containing the cinnamic acid ester groups were evaluated for in vitro cytotoxicity against prostate cancer cell (PC-3). Furthermore, prediction of physicochemical characteristics and drugability as well as in silico ADME-Tox tests of investigated compounds were performed. The 3D-QSAR model was established using the comparative molecular field analysis method. According to obtained results, the tricyclic compounds 9 and 10 had the highest potency with IC50 < 20 µM. Evaluation of structural features through 3D-QSAR model identified steric field feature on the cinnamic acid ester groups at C-7 as a crucial for the cytotoxic activity. This research suggests that most of the analysed compounds have desirable properties for drug candidates and high potential in drug development, which recommend them for further research in treatment of prostate cancer. Furthermore, obtained 3D-QSAR model is able to successfully identify styryl lactones that have significant cytotoxic activity and provide information for screening and design of novel inhibitors against PC-3 cell line that could be used as drugs in treatment of the prostate cancer.Graphical abstractGraphical abstract for this article
  • Biochemical and computational insights of adenosine deaminase inhibition
           by Epigallocatechin gallate
    • Abstract: Publication date: Available online 17 August 2019Source: Computational Biology and ChemistryAuthor(s): Arun K.G, Sharanya C.S, Abhithaj J, Sadasivan C Epigallocatechin gallate, a flavonoid from Camellia sinensis possess various pharmacological activities such as anticancer, antimicrobial and antioxidant etc. Adenosine deaminase, (ADA), is a key enzyme involved in the purine metabolism, the inhibitors of which is being considered as highly promising candidate for the development of anti-proliferative and anti-inflammatory drugs. In this work we studied adenosine deaminase inhibitory activity of epigallocatechin gallate by using biophysical and computational methods. The enzyme inhibition study result indicated that epigallocatechin gallate possess strong inhibitory activity on ADA. ITC study revealed the energetics of binding. Also the binding is confirmed by using fluorescence spectroscopy. The structural details of binding are obtained from molecular docking and MD simulation studies.Graphical abstractGraphical abstract for this article
  • Homology modeling and 3D–QSAR study of benzhydrylpiperazine δ
           opioid receptor agonists
    • Abstract: Publication date: Available online 16 August 2019Source: Computational Biology and ChemistryAuthor(s): Chenling Pan, Hao Meng, Shuqun Zhang, Zhili Zuo, Yuehai Shen, Liangliang Wang, Kwen-Jen Chang The binding affinity of a series of benzhydrylpiperazine δ opioid receptor agonists were pooled and evaluated by using 3D-QSAR and homology modeling/molecular docking methods. Ligand-based CoMFA and CoMSIA 3D-QSAR analyses with 46 compounds were performed on benzhydrylpiperazine analogues by taking the most active compound BW373U86 as the template. The models were generated successfully with q2 value of 0.508 and r2 value of 0.964 for CoMFA, and q2 value of 0.530 and r2 value of 0.927 for CoMSIA. The predictive capabilities of the two models were validated on the test set with R2pred value of 0.720 and 0.814, respectively. The CoMSIA model appeared to work better in this case. A homology model of active form of δ opioid receptor was established by Swiss-Model using a reported crystal structure of active μ opioid receptor as a template, and was further optimized using nanosecond scale molecular dynamics simulation. The most active compound BW373U86 was docked to the active site of δ opioid receptor and the lowest energy binding pose was then used to identify binding residues such as s Gln105, Lys108, Leu125, Asp128, Tyr129, Leu200, Met132, Met199, Lys214, Trp274, Ile277, Ile304 and Tyr308. The docking and 3D-QSAR results showed that hydrogen bond and hydrophobic interactions played major roles in ligand-receptor interactions. Our results highlight that an approach combining structure-based homology modeling/molecular docking and ligand-based 3D-QSAR methods could be useful in designing of new opioid receptor agonists.Graphical Graphical abstract for this article
  • An integrated in silico screening strategy for identifying promising
           disruptors of p53-MDM2 interaction
    • Abstract: Publication date: Available online 16 August 2019Source: Computational Biology and ChemistryAuthor(s): Hajar Sirous, Giulia Chemi, Giuseppe Campiani, Simone Brogi The p53 protein, also called guardian of the genome, plays a critical role in the cell cycle regulation and apoptosis. This protein is frequently inactivated in several types of human cancer by abnormally high levels of its negative regulator, mouse double minute 2 (MDM2). As a result, restoration of p53 function by inhibiting p53-MDM2 protein–protein interaction has been pursued as a compelling strategy for cancer therapy. To date, a limited number of small-molecules have been reported as effective p53−MDM2 inhibitors. X-ray structures of MDM2 in complex with some ligands are available in Protein Data Bank and herein, these data have been exploited to efficiently identify new p53-MDM2 interaction antagonists through a hierarchical virtual screening strategy. For this purpose, the first step was aimed at compiling a focused library of 686,630 structurally suitable compounds, from PubChem database, similar to two known effective inhibitors, Nutlin-3a and DP222669. These compounds were subjected to the subsequent structure-based approaches (quantum polarized ligand docking and molecular dynamics simulation) to select potential compounds with highest binding affinity for MDM2 protein. Additionally, ligand binding energy, ADMET properties and PAINS analysis were also considered as filtering criteria for selecting the most promising drug-like molecules. On the basis of these analyses, three top-ranked hit molecules, CID_118439641, CID_60452010 and CID_3106907, were found to have acceptable pharmacokinetics properties along with superior in silico inhibitory ability towards the p53-MDM2 interaction compared to known inhibitors. Molecular docking and molecular dynamics results well confirmed the interactions of the final selected compounds with critical residues within p53 binding site on the MDM2 hydrophobic clefts with satisfactory thermodynamics stability. Consequently, the new final scaffolds identified by the presented computational approach could offer a set of guidelines for designing promising anti-cancer agents targeting p53-MDM2 interaction.Graphical abstractGraphical abstract for this article
  • Revelation of Enzyme Activity of Mutant Pyrazinamidases from Mycobacterium
           Tuberculosis upon Binding with Various Metals using Quantum Mechanical
    • Abstract: Publication date: Available online 16 August 2019Source: Computational Biology and ChemistryAuthor(s): Nouman Rasool, Waqar Husssain, Yaser Daanial Khan Pyrazinamide (PZA) is one of the most potent bacteriostatic drug against tuberculosis, a deadliest disease with high mortality and morbidity rate. PZA metabolizes into its active form pyrazinoic acid (POA) with the help of a metalloenzyme, pyrazinamidase (PZase). Mutagenicity and metal substitution in PZase weakens the binding of PZA with PZase and increases the drug resistance in Mycobacterium tuberculosis. The present study aims at the quantum mechanistic analysis of mutant-metal substituted PZase complexes by studying the mechanics of metals and PZA binding at MCS and catalytic site, respectively. A total of 66 complexes are scrutinised in this study to elucidate the effect of mutations on the enzymatic function of PZase. Among the 10 mutations considered in this study, 7 different mutations i.e. Asp49 → Asn, His51 → Arg, Gly78 → Cys, Asp12 → Gly, Asp12 → Ala, Thr135 → Pro and Asp136 → Gly cause a detrimental effect on the activity of PZase. In addition to this, the substitution of iron with cobalt enhances the enzymatic activity of both wild type and mutant PZase while zinc, magnesium and copper reduce it. Based on these results, it is concluded that upon substitution of iron with zinc, magnesium and copper, PZase cannot function properly. Due to mutations, the reactivity of the drug also reduces as its binding with PZase weakens and this phenomenon enhances the resistance of Mycobacterium tuberculosis against drug.Graphical abstractGraphical abstract for this article
  • Landscape of ROD9 Island: Functional Annotations and Biological Network of
           Hypothetical Proteins in Salmonella enterica
    • Abstract: Publication date: Available online 16 August 2019Source: Computational Biology and ChemistryAuthor(s): Nikita Soni, Sunil Kumar Swain, Ravi Kant, Aditya Singh, Rahul Ravichandran, Suresh K Verma, Pritam Kumar Panda, Mrutyunjay Suar Salmonella, an Enterobacteria is a therapeutically important pathogen for the host. The advancement of genome sequencing of S. enterica serovar Enteritidis have identified a distinct ROD9 pathogenic island, imparting virulence. The occurrence of 17 ROD9 hypothetical proteins, necessitates subsequent bioinformatics approach for structural and functional aspects of protein-protein relations or networks in different pathogenic phenotypes express. A collective analysis using predictive bioinformatics tools that includes NCBI-BLASTp and BLAST2GO annotated the motif patterns and functional significance. The VFDB identified 10 virulence proteins at both genomic and metagenomic level. Phylogenetic analysis revealed a divergent and convergent relationship between 17 ROD9 and 41 SP-1 proteins. Here, combining a comprehensive approach from sequence based, motif recognitions, domain identification, virulence ability to structural modelling provides a precise function to ROD9 proteins biological network, for which no experimental information is available.Graphical abstractGraphical abstract for this article
  • Influence of functional moiety in lupane-type triterpenoids in BACE1
    • Abstract: Publication date: Available online 14 August 2019Source: Computational Biology and ChemistryAuthor(s): Aditi Wagle, Su Hui Seong, María Julia Castro, María Belén Faraoni, Ana Paula Murray, Hyun Ah Jung, Jae Sue Choi Lupane-type triterpenoids have shown a potential effect against neurodegenerative disorders. Alzheimer’s disease, one of the common neurodegenerative disease, is evident by the accumulation of amyloid-beta (Aβ) plaque in the extracellular regions of the brain. β-site APP cleaving enzyme 1 (BACE1) is a key enzyme for the Aβ formation via the cleavage of amyloid precursor protein (APP). Therefore, to find the potent BACE1 inhibitors and furthermore to explore the role of the functional group responsible for the strong BACE1 inhibitory activity, we synthesized a series of triterpenoids with lupane skeleton starting from the natural compounds calenduladiol and lupeol. Compound 1 revealed a potent competitive BACE1 inhibitory activity (IC50 = 16.77 ± 1.16 µM; Ki = 19.38). Furthermore, the molecular docking simulation revealed the importance of Tyr198 residue along with the other hydrophobic interaction for the strong affinity of 1‒BACE1 complex. To sum up, our results demonstrated the importance of carbonyl moiety at 3 and 16 position of lupane-type triterpenoid over the hydroxyl group at the same position.Graphical abstractGraphical abstract for this article
  • In silico Identification of Natural Products with Anticancer Activity
           Using a Chemo-structural Database of Brazilian Biodiversity
    • Abstract: Publication date: Available online 14 August 2019Source: Computational Biology and ChemistryAuthor(s): João Marcos Galúcio, Elton Figueira Monteiro, Deivid Almeida de Jesus, Clauber Henrique Costa, Raissa Caroline Siqueira, Gabriela Bianchi dos Santos, Jerônimo Lameira, Kauê Santana da Costa Cancer is one of the leading causes of death worldwide, and the number of patients has only increased each year, despite the considerable efforts and investments in scientific research. Since natural products (NPs) may serve as suitable sources for drug development, the cytotoxicity against cancer cells of 2,221 compounds from the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBEDB) was predicted using CDRUG algorithm. Molecular modeling, chemoinformatics, and chemometric tools were then used to analyze the structural and physicochemical properties of these compounds. We compared the positive NPs with FDA-approved anticancer drugs and predicted the molecular targets involved in the anticancer activity. In the present study, 46 families comprising potential anticancer compounds and at least 19 molecular targets involved in oncogenesis. To the best of our knowledge, this is the first large-scale study conducted to evaluate the potentiality of NPs sourced from Brazilian biodiversity as anticancer agents, using in silico approaches. Our results provided interesting insights about the mechanism of action of these compounds, and also suggested that their structural diversity may aid structure-based optimization strategies for developing novel drugs for cancer therapy.Graphical Graphical abstract for this article
  • TTAgP 1.0: A computational tool for the specific prediction of tumor T
           cell antigens
    • Abstract: Publication date: Available online 12 August 2019Source: Computational Biology and ChemistryAuthor(s): Jorge Félix Beltrán Lissabet, Lisandra Herrera Belén, Jorge G. Farias Nowadays, cancer is considered a global pandemic and millions of people die every year because this disease remains a challenge for the world scientific community. Even with the efforts made to combat it, there is a growing need to discover and design new drugs and vaccines. Among these alternatives, antitumor peptides are a promising therapeutic solution to reduce the incidence of deaths caused by cancer. In the present study, we developed TTAgP, an accurate bioinformatic tool that uses the random forest algorithm for antitumor peptide predictions, which are presented in the context of MHC class I. The predictive model of TTAgP was trained and validated based on several features of 922 peptides. During the model validation we achieved sensitivity = 0.89, specificity = 0.92, accuracy = 0.90 and the Matthews correlation coefficient = 0.79 performance measures, which are indicative of a robust model. TTAgP is a fast, accurate and intuitive software focused on the prediction of tumor T cell antigens.Graphical Graphical abstract for this article
  • Identification of miRNA, their targets and miPEPs in peanut (Arachis
    • Abstract: Publication date: Available online 10 August 2019Source: Computational Biology and ChemistryAuthor(s): Mousam Kumar Ram, Koel Mukherjee, Dev Mani Pandey MicroRNAs (miRNAs) are one of the major cytoplasmic tools employed by the eukaryotes for post-transcriptional gene regulation. These ˜21 nt small non-coding RNA molecules are highly conserved among species which forms a base for identification of new miRNAs. In this study, we used previously known mature miRNAs to search their homologs in Arachis hypogaea ESTs. A total of 50 non-protein coding sequences showing homology with no more than 3 mismatches were folded back to hairpin stem-loop structures using mfold. These predicted structures were passed through strict filtration criteria to obtain 18 miRNAs, all of which were other than those reported in miRBase. Out of 18 miRNAs, 7 were found to be new. These miRNAs belonged to miR156, miR166, miR167, miR319, miR398, miR399, miR482 and miR1507 family. These miRNAs were found to target a total of 118 genes in Arabidopsis. These targets included disease resistant proteins, auxin responsive proteins, squamosa promoter binding like proteins, co-transporter protein, transposable element genes, NAD(P) binding protein and topoisomerase II. KEGG pathway analysis showed potential involvement of these miRNAs in regulating different pathways. Apart from miRNA and their targets, miPEPs for 14 miRNAs were also identified.Graphical abstractGraphical abstract for this article
  • Exploring the influence of conserved lysine69 on the catalytic activity of
           the helicobacter pylori shikimate dehydrogenase: A combined QM/MM and MD
    • Abstract: Publication date: Available online 9 August 2019Source: Computational Biology and ChemistryAuthor(s): Jun Li, Ge’an Wu, Qiang Fu, Heng’an Ge, Shu Liu, Xiaolong Li, Biao Cheng Shikimate dehydrogenase (SDH) catalyzes the reversible, NADPH-dependent reduction of 3-dehydroshikimate to shikimate, involved in the shikimate pathway. This pathway has emerged as an important target for the development of antimicrobial agent. Structural and functional analyses suggest that the conserved Lys69 plays an important role in the catalytic activity of Helicobacter pylori (H. pylori) SDH. However, the detailed mechanism how mutation of Lys69 affects the catalytic activity of H. pylori SDH remains unclear. Here, two-layered ONIOM-based quantum mechanics/molecular mechanics (QM/MM) calculation and molecular dynamics (MD) simulations were performed to explore the role of Lys69 in the H. pylori SDH. Our results showed that in addition to act as a catalytic base, the conserved Lys69 plays an additional, important role in the maintenance of the substrate shikimate in the active site, facilitating the catalytic reaction between the cofactor NADP+ and shikimate. Mutation of Lys69 triggers the movement of shikimate away from the active site of SDH, thereby disrupting the catalytic activity. This result can advance our understanding the catalytic mechanism of SDH family, which may benefit of the rational design of SDH inhibitors.
  • Structural insights into the binding mechanism of Plasmodium falciparum
           exported Hsp40-Hsp70 chaperone pair
    • Abstract: Publication date: Available online 8 August 2019Source: Computational Biology and ChemistryAuthor(s): Ankita Behl, Prakash Chandra Mishra Expression of heat shock proteins in Plasmodium falciparum (Pf) increases during febrile episodes to play key roles in several necessary cellular processes. ‘PFA0660w-PfHsp70-x’, an exported chaperone pair is known to co-localize to specialized intracellular structures termed J-dots, and has been implicated in trafficking of the major virulence factor, PfEMP1 (Plasmodium falciparum erythrocyte membrane protein 1) across the host cell. This article highlights for the first time detailed structural analysis of PFA0660w-PfHsp70-x chaperone pair to better understand their binding mechanism. Here, we have modeled reliable molecular structures for the complete conserved region of PFA0660w and PfHsp70-x. These structures were evaluated by different structure verification tools followed by molecular dynamics (MD) simulations. The model of PFA0660w was subjected to docking with PfHsp70-x using Haddock to reveal a number of residues crucial for their bipartite interaction, and also performed MD simulations on the complex. The peptide binding clefts of PFA0660w and its other Plasmodium species homologs were found to be bigger than their counterparts in higher eukaryotes like yeast, humans and C. parvum. Based on our results, we propose a model for PFA0660w-PfHsp70-x interaction and a mechanism of substrate binding, and compare it with its dimeric human counterparts. Owing to these striking structural differences between the host and parasite chaperones, such information on the essential Hsp40 and its partner Hsp70 may form the basis for rational drug design against fatal malaria.Graphical abstractGraphical abstract for this article
  • Systematic evaluation of the mechanisms of zoledronic acid based on
           network pharmacology
    • Abstract: Publication date: Available online 1 August 2019Source: Computational Biology and ChemistryAuthor(s): Xue-Zhen Liang, Rui Li, Bo Xu, Di Luo, Guang-Bo Liu, Jiang Peng, Gang Li Zoledronic acid (ZA) is an FDA-approved drug and a third-generation bisphosphonate (BPs). A systematic evaluation of the mechanisms of ZA has not previously been performed. In this study, validated targets of ZA were screened using PubChem, Herbal Ingredients’ Targets Database (HIT), Binding Database (BindingDB), and ChemBank, and potential targets of ZA were identified based on structural characteristics of ligands and proteins. The candidate targets were then assessed using GeneMANIA, Gene Ontology (GO), and pathway analysis, and molecule-target-GO-pathway networks were visualized using Cytoscape. Nine validated targets and 26 potential targets were obtained. The networks generated via this analysis showed that the candidate targets were associated with cell proliferation and metabolism as well as other biological processes (BP) and pathways. In general, ZA appeared to play crucial roles in multiple functions, including metabolism, regulation of vascular smooth muscle cell proliferation, and chemical carcinogenesis; a great deal of additional research must be performed. Moreover, the current study showed that it is feasible to analyze the mechanisms of ZA via target prediction, which facilitates systematic pharmacological evaluation.
  • In silico and in vitro identification of candidate SIRT1 activators from
           Indonesian medicinal plants compounds database
    • Abstract: Publication date: Available online 18 July 2019Source: Computational Biology and ChemistryAuthor(s): Azminah Azminah, Linda Erlina, Maksum Radji, Abdul Mun’im, Rezi Riadhi Syahdi, Arry YanuarABSTRACTSirtuin 1 (SIRT1) is a class III family of protein histone deacetylases involved in NAD+-dependent deacetylation reactions. It has been suggested that SIRT1 activators may have a protective role against type 2 diabetes, the aging process, and inflammation. This study aimed to explore and identify medicinal plant compounds from Indonesian Herbal Database (HerbalDB) that might potentially become a candidate for SIRT1 activators through a combination of in silico and in vitro methods. Two pharmacophore models were developed using co-crystalized ligands that allosterically bind with SIRT1 similar to the putative ligands used by SIRT1 activators. Then, these were used for the virtual screening of HerbalDB. The identified compounds were subjected to molecular docking and 50 ns molecular dynamics simulation. Molecular dynamics simulation was analyzed using MM-GB(PB)SA methods. The compounds identified by these methods were tested in an in vitro study using a SIRT-GloTM luminescence assay. Virtual screening using structure-based pharmacophores predicted that mulberrin as the best candidate SIRT1 activator. Virtual screening using ligand-based pharmacophores predicted that gartanin, quinidine, and quinine to be the best candidates as SIRT1 activators. The molecular docking studies showed the important residues involved were Ile223 and Ile227 at the allosteric region. The MM-GB(PB)SA calculations confirmed that mulberrin, gartanin, quinidine, quinine showed activity at allosteric region and their EC50 in vitro values are 2.10; 1.79; 1.71; 1.14 µM, respectively. Based on in silico and in vitro study results, mulberin, gartanin, quinidine, and quinine had good activity as SIRT1 activators.Graphical abstractGraphical abstract for this article
  • New SDC function prediction based on protein-protein interaction using
           bioinformatics tools
    • Abstract: Publication date: Available online 16 July 2019Source: Computational Biology and ChemistryAuthor(s): Flávia S. Zandonadi, Elisa Castañeda, Johanna Korvala The precise roles for SDC have been complex to specify. Assigning and reanalyzing protein and peptide identification to novel protein functions is one of the most important challenges in postgenomic era. Here, we provide SDC molecular description to support, contextualize and reanalyze the corresponding protein-protein interaction (PPI). From SDC-1 data mining, we discuss the potential of bioinformatics tools to predict new biological rules of SDC. Using these methods, we have assembled new possibilities for SDC biology from PPI data, once, the understanding of biology complexity cannot be capture from one simple question.Graphical abstractGraphical abstract for this article
  • Disorder Atlas: web-based software for the proteome-based interpretation
           of intrinsic disorder predictions
    • Abstract: Publication date: Available online 13 July 2019Source: Computational Biology and ChemistryAuthor(s): Michael Vincent, Santiago Schnell Intrinsically disordered proteins lack a stable three-dimensional structure under physiological conditions. While this property has gained considerable interest within the past two decades, disorder poses substantial challenges to experimental characterization efforts. In effect, numerous computational tools have been developed to predict disorder from primary sequences, however, interpreting the output of these algorithms remains a challenge. To begin to bridge this gap, we present Disorder Atlas, web-based software that facilitates the interpretation of intrinsic disorder predictions using proteome-based descriptive statistics. This service is also equipped to facilitate large-scale systematic exploratory searches for proteins encompassing disorder features of interest, and further allows users to browse the prevalence of multiple disorder features at the proteome level. As a result, Disorder Atlas provides a user-friendly tool that places algorithm-generated disorder predictions in the context of the proteome, thereby providing an instrument to compare the results of a query protein against predictions made for an entire population. Disorder Atlas currently supports ten eukaryotic proteomes and is freely available for non-commercial users at abnstractGraphical abstract for this article
  • Dynamics of Human Defensin 5 (HD5) Self-assembly in Solution: Molecular
    • Abstract: Publication date: Available online 13 July 2019Source: Computational Biology and ChemistryAuthor(s): Phoom Chairattana, Jitti Niramitranon, Prapasiri Pongprayoon Human α -defensin 5 (HD5) is a 32-residue cysteine-rich host-defense peptide that exhibits broad-spectrum antimicrobial activity and plays an essential role in innate immunity in the human gut and other organ systems. Although its antimicrobial mechanism of action remains unclear, the high salt concentration seems to attenuate the antimicrobial function of HD5 via an unknown mechanism. In this work, we employ Molecular Dynamics (MD) simulations to analyse the oligomerization behaviour of HD5 when exposed to salt concentration. We demonstrate that the presence of salt, such as sodium chloride (NaCl), promotes HD5 to form higher-order oligomers (up to heptamers) in our simulations. In addition, we also analyse the electrostatic interactions between the two Glu residues (E14 and E21) and their neighbouring residues. Our data confirm that the E14 residue is essential for the structural integrity, whereas the E21 residue contributes to the dimerization of HD5, suggesting that these Glu residues are important for the antimicrobial function of this peptide.Graphical abstractGraphical abstract for this article
  • A comparative multivariate analysis of nitrilase enzymes: an ensemble
           based computational approach
    • Abstract: Publication date: Available online 12 July 2019Source: Computational Biology and ChemistryAuthor(s): Priya Kumari, Raju Poddar Nitrilases, member of nitrilase superfamily catalyze the hydrolysis of different nitriles to corresponding amides and acids. In this article, we demonstrate two-fold computational comparative analysis on coding gene sequences, amino acid sequences, three-dimensional structure and function. A large ensemble-based dataset was utilized from bacteria, fungi, plants and animals. A relative multivariate analysis on codon and amino acids usage, motif analysis and Bayesian phylogenetic analysis were performed in detail, to comprehend relative synonymous codon usage, translational selection, comparative conserved catalytic motif and evolutionary relationship among different nitrilases. To explore structure-function relationship of nitrilases, SCOP analysis, molecular dynamics simulation, structural fluctuation, principal component analysis (PCA), dynamic cross correlation (DCCM), root mean squared inner product (RMSIP), free energy surface (FES) and molecular docking analysis were performed sumptuously. The comparative results reveal different new aspects about nitrilase. It also supports the theory of bacterial nitrilases are in ecological relationships with fungi and plant in plant pathogen interaction in large extent.Graphical abstractGraphical abstract for this articleDiversity of Nitrilase enzymes among all species
  • A comparison of classifiers for predicting the class color of fluorescent
    • Abstract: Publication date: Available online 9 July 2019Source: Computational Biology and ChemistryAuthor(s): Roger Sá da Silva, Luis Fernando Marins, Daniela Volcan Almeida, Karina dos Santos Machado, Adriano V. Werhli Fluorescent proteins have been applied in a wide variety of fields ranging from basic science to industrial applications. Apart from the naturally occurring fluorescent proteins, there is a growing interest in genetically modified variants that emit light in a specific wavelength. Genetically modifying a protein is not an easy task, specially because the exchange of one residue by other has to achieve the desired property while maintaining protein stability. To help in the choice of residue exchange, computational methods are applied to predict function and stability of proteins. In this work we have prepared a dataset composed by 109 fluorescent proteins and tested four classical supervised classification algorithms: Artificial Neural Networks (ANNs), Decision Trees (DTs), Support Vector Machines (SVMs) and Random Forests (RFs). This is the first time that algorithms are compared in this task. Results of comparing the algorithm's performance shows that DT, SVM adn RF were significantly better than ANNs, and RF was the best method in all the scenarios. However, the interpretability of DTs is highly relevant and can provide important clues about the mechanisms involved in protein color emission. The results are promising and indicate that the use of in silico methods can greatly reduce the time and cost of the in vitro experiments.Graphical abstractGraphical abstract for this article
  • DM-RPIs: predicting ncRNA-protein interactions using stacked ensembling
    • Abstract: Publication date: Available online 6 July 2019Source: Computational Biology and ChemistryAuthor(s): Shuping Cheng, Lu Zhang, Jianjun Tan, Weikang Gong, Chunhua Li, Xiaoyi Zhang ncRNA-protein interactions (ncRPIs) play an important role in a number of cellular processes, such as post-transcriptional modification, transcriptional regulation, disease progression and development. Since experimental methods are expensive and time-consuming to identify the ncRPIs, we proposed a computational method, Deep Mining ncRNA-Protein Interactions (DM-RPIs), for identifying the ncRPIs. In order to descending dimension and excavating hidden information from k-mer frequency of RNA and protein sequences, using the Deep Stacking Auto-encoders Networks (DSANs) model refined the raw data. Three common machine learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Convolution Neural Network (CNN), were separately trained as individual predictors and then the three individual predictors were integrated together using stacked ensembling strategy. Based on the RPI2241 dataset, DM-RPI obtains an accuracy of 0.851, precision of 0.852, sensitivity of 0.873, specificity of 0.826, and MCC of 0.701, which is promising and pioneering for the prediction of ncRPIs.Graphical abstractGraphical abstract for this article
  • Meta-analysis of Gene Expression for Development and Validation of a
           Diagnostic Biomarker Panel for Oral Squamous Cell Carcinoma
    • Abstract: Publication date: Available online 2 July 2019Source: Computational Biology and ChemistryAuthor(s): Vladimir Makarov, Alex Gorlin We use a newly developed feature extraction and classification method to analyze previously published gene expression data sets in Oral Squamous Cell Carcinoma and in healthy oral mucosa in order to find a gene set sufficient for diagnoses. The feature selection technology is based on the relative dichotomy power concept published by us earlier. The resulting biomarker panel has 100% sensitivity and 95% specificity, is enriched in genes associated with oncogenesis and invasive tumor growth, and, unlike marker panels devised in earlier studies, shows concordance with previously published marker genes.Graphical abstractGraphical abstract for this articleHighlights •Present a diagnostic biomarker panel for Oral Squamous Cell Carcinoma.•Use meta-analysis of gene expression data and sort features with a dichotomy power metric.•Results are reproducible - concordant with earlier studies.•Explain irreproducibility of molecular biomarkers for this cancer seen in earlier studies.
  • Maltase-glucoamylase inhibition potency and cytotoxicity of
           pyrimidine-fused compounds: An in silico and in vitro approach
    • Abstract: Publication date: Available online 19 June 2019Source: Computational Biology and ChemistryAuthor(s): Mohammad Hossein Mehraban, Mahboubeh Mansourian, Sajjad Ahrari, Ali HajiEbrahimi, Salman Odooli, Majid Motovali-Bashi, Reza Yousefi, Younes Ghasemi The prevalence of diabetes mellitus has been incremented in the current century and the need for novel therapeutic compounds to treat this disease has been significantly increased. One of the most promising approaches is to inhibit intestinal alpha glucosidases. Based on our previous studies, four pyrimidine-fused heterocycles (PFH) were selected as they revealed satisfactory inhibitory action against mammalian α-glucosidase. The interaction of these compounds with both active domains of human maltase-glucoamylase (MGAM) and their effect on human Caco-2 cell line were investigated. The docking assessments suggested that binding properties of these ligands were almost similar to that of acarbose by establishing hydrogen bonds especially with Tyr1251 and Arg526 in both C-terminal and N-terminal MGAM, respectively. Also, these compounds indicated a stronger affinity for C-terminal of MGAM. L2 and L4 made tightly complexes with both terminals of MGAM which in turn revealed the importance of introducing pyrimidine scaffold and its hinge compartment. The results of molecular dynamics simulation analyses confirmed the docking data and showed deep penetration of L2 and L4 into the active site of MGAM. Based on cell cytotoxicity assessments, no significant cell death induction was observed. Hence, these functional MGAM inhibitors might be considered as new potential therapeutic compounds in treatment of diabetes and its complications.Graphical abstractGraphical abstract for this article
  • Insights into the flexibility of the T3 loop and GTPase activating protein
           (GAP) domain of dimeric α and β tubulins from a molecular dynamics
    • Abstract: Publication date: Available online 18 June 2019Source: Computational Biology and ChemistryAuthor(s): Selvaa Kumar C, Nikhil Gadewal, Rajankumar Choudhary, Debjani Dasgupta Tubulin protein is the fundamental unit of microtubules, and comprises of α and β subunits arranged in an alternate manner forming protofilaments. These longitudinal protofilaments are made up of intra- (α-β) and inter-dimer (β-α) interactions. Literature review confirms that GTP hydrolysis results in considerable structural rearrangement within GTP binding site of β-α dimer interface after the release of γ phosphate. In addition to this, the intra-dimer interface exhibits structural rigidity which needs further investigation. In this study, we explored the reasons for the flexibility and the rigidity of the β-α dimer and the α-β dimer respectively through molecular simulation and Anisotropic Normal Mode based analysis. As per the sequence alignment report, two glycine residues (Gly96 and Gly98) were observed in the T3 loop of the β subunit which get substituted by Asp98 and Ala100 in the T3 loop of the α subunit. The higher mobility of glycine residues contributes to the flexibility of the T3 loop of inter-dimer when they come in direct contact with the GTPase Activating Protein (GAP) domain of the subunit. This was confirmed through RMSD, RMSF and Radius of Gyration based studies. Conversely, the intra-dimer exhibited a lower mobility in the absence of glycine residues. As per ANM based analysis, positive domain correlations were observed between T3 loop and GAP domain of intra- and inter- dimeric contact regions. However, these correlation motions were higher in the intra-dimer as compared to the inter-dimer interface. Thus on the basis of our findings, we hypothesize that the higher flexibility of T3 loop and the GAP domain of the inter-dimer is required for structural rearrangement and protofilament stability during hydrolysis. Furthermore, the slightly rigid nature of the T3 loop and the GAP domain of the intra-dimer assists in enhancing the monomer-monomer interaction through the higher positive domain correlation.Graphical abstractGraphical abstract for this article
  • 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
  • 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 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
  • 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
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