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  Subjects -> ENGINEERING (Total: 2266 journals)
    - CHEMICAL ENGINEERING (190 journals)
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    - ENGINEERING (1195 journals)
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CHEMICAL ENGINEERING (190 journals)                     

Showing 1 - 190 of 190 Journals sorted alphabetically
AATCC Journal of Research     Full-text available via subscription   (Followers: 6)
ACS Sustainable Chemistry & Engineering     Hybrid Journal   (Followers: 4)
Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials     Hybrid Journal   (Followers: 5)
Acta Polymerica     Hybrid Journal   (Followers: 10)
Additives for Polymers     Full-text available via subscription   (Followers: 20)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 7)
Advanced Chemical Engineering Research     Open Access   (Followers: 30)
Advanced Powder Technology     Hybrid Journal   (Followers: 16)
Advances in Applied Ceramics     Hybrid Journal   (Followers: 5)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 24)
Advances in Chemical Engineering and Science     Open Access   (Followers: 53)
Advances in Polymer Technology     Hybrid Journal   (Followers: 14)
African Journal of Pure and Applied Chemistry     Open Access   (Followers: 7)
Annual Review of Analytical Chemistry     Full-text available via subscription   (Followers: 9)
Annual Review of Chemical and Biomolecular Engineering     Full-text available via subscription   (Followers: 12)
Anti-Corrosion Methods and Materials     Hybrid Journal   (Followers: 8)
Applied Petrochemical Research     Open Access   (Followers: 2)
Asia-Pacific Journal of Chemical Engineering     Hybrid Journal   (Followers: 7)
Biochemical Engineering Journal     Hybrid Journal   (Followers: 15)
Biofuel Research Journal     Open Access   (Followers: 4)
Biomass Conversion and Biorefinery     Partially Free   (Followers: 10)
Brazilian Journal of Chemical Engineering     Open Access   (Followers: 3)
Bulletin of Chemical Reaction Engineering & Catalysis     Open Access   (Followers: 2)
Bulletin of the Chemical Society of Ethiopia     Open Access   (Followers: 2)
Carbohydrate Polymers     Hybrid Journal   (Followers: 8)
Catalysts     Open Access   (Followers: 7)
ChemBioEng Reviews     Full-text available via subscription   (Followers: 1)
Chemical and Engineering News     Free   (Followers: 11)
Chemical and Materials Engineering     Open Access   (Followers: 12)
Chemical and Petroleum Engineering     Hybrid Journal   (Followers: 12)
Chemical and Process Engineering     Open Access   (Followers: 26)
Chemical and Process Engineering Research     Open Access   (Followers: 23)
Chemical Engineering & Technology     Hybrid Journal   (Followers: 33)
Chemical Engineering and Processing: Process Intensification     Hybrid Journal   (Followers: 18)
Chemical Engineering and Science     Open Access   (Followers: 18)
Chemical Engineering Communications     Hybrid Journal   (Followers: 14)
Chemical Engineering Education     Full-text available via subscription  
Chemical Engineering Journal     Hybrid Journal   (Followers: 33)
Chemical Engineering Research and Design     Hybrid Journal   (Followers: 23)
Chemical Engineering Research Bulletin     Open Access   (Followers: 11)
Chemical Engineering Science     Hybrid Journal   (Followers: 25)
Chemical Geology     Hybrid Journal   (Followers: 18)
Chemical Papers     Hybrid Journal   (Followers: 2)
Chemical Product and Process Modeling     Hybrid Journal   (Followers: 3)
Chemical Reviews     Full-text available via subscription   (Followers: 169)
Chemical Society Reviews     Full-text available via subscription   (Followers: 41)
Chemical Technology     Open Access   (Followers: 15)
ChemInform     Hybrid Journal   (Followers: 7)
Chemistry & Industry     Hybrid Journal   (Followers: 5)
Chemistry Central Journal     Open Access   (Followers: 4)
Chemistry of Materials     Full-text available via subscription   (Followers: 183)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
Chinese Chemical Letters     Full-text available via subscription   (Followers: 3)
Chinese Journal of Chemical Engineering     Full-text available via subscription   (Followers: 4)
Chinese Journal of Chemical Physics     Hybrid Journal   (Followers: 1)
Coke and Chemistry     Hybrid Journal   (Followers: 1)
Coloration Technology     Hybrid Journal  
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computer Aided Chemical Engineering     Full-text available via subscription   (Followers: 1)
Computers & Chemical Engineering     Hybrid Journal   (Followers: 9)
CORROSION     Full-text available via subscription   (Followers: 20)
Corrosion Engineering, Science and Technology     Hybrid Journal   (Followers: 36)
Corrosion Reviews     Hybrid Journal   (Followers: 4)
Crystal Research and Technology     Hybrid Journal   (Followers: 6)
Current Opinion in Chemical Engineering     Open Access   (Followers: 7)
Education for Chemical Engineers     Hybrid Journal   (Followers: 5)
Eksergi     Open Access  
Emerging Trends in Chemical Engineering     Full-text available via subscription   (Followers: 2)
European Polymer Journal     Hybrid Journal   (Followers: 41)
Fibers and Polymers     Full-text available via subscription   (Followers: 6)
Fluorescent Materials     Open Access   (Followers: 1)
Focusing on Modern Food Industry     Open Access   (Followers: 2)
Frontiers of Chemical Science and Engineering     Hybrid Journal   (Followers: 2)
Gels     Open Access  
Geochemistry International     Hybrid Journal   (Followers: 2)
Handbook of Powder Technology     Full-text available via subscription   (Followers: 6)
Heat Exchangers     Open Access   (Followers: 3)
High Performance Polymers     Hybrid Journal   (Followers: 1)
Hungarian Journal of Industry and Chemistry     Open Access  
Indian Chemical Engineer     Hybrid Journal   (Followers: 5)
Indian Journal of Chemical Technology (IJCT)     Open Access   (Followers: 10)
Indonesian Journal of Chemical Science     Open Access   (Followers: 1)
Industrial & Engineering Chemistry     Full-text available via subscription   (Followers: 11)
Industrial & Engineering Chemistry Research     Full-text available via subscription   (Followers: 21)
Industrial Chemistry Library     Full-text available via subscription   (Followers: 3)
Industrial Gases     Open Access  
Info Chimie Magazine     Full-text available via subscription   (Followers: 3)
International Journal of Chemical and Petroleum Sciences     Open Access   (Followers: 3)
International Journal of Chemical Engineering     Open Access   (Followers: 7)
International Journal of Chemical Reactor Engineering     Hybrid Journal   (Followers: 3)
International Journal of Chemical Technology     Open Access   (Followers: 5)
International Journal of Chemoinformatics and Chemical Engineering     Full-text available via subscription   (Followers: 2)
International Journal of Food Science     Open Access   (Followers: 3)
International Journal of Industrial Chemistry     Open Access   (Followers: 1)
International Journal of Polymeric Materials     Hybrid Journal   (Followers: 5)
International Journal of Science and Engineering     Open Access   (Followers: 4)
International Journal of Waste Resources     Open Access   (Followers: 4)
Journal of Chemical Engineering & Process Technology     Open Access   (Followers: 4)
Journal of Applied Crystallography     Hybrid Journal   (Followers: 6)
Journal of Applied Electrochemistry     Hybrid Journal   (Followers: 12)
Journal of Applied Polymer Science     Hybrid Journal   (Followers: 113)
Journal of Biomaterials Science, Polymer Edition     Hybrid Journal   (Followers: 9)
Journal of Bioprocess Engineering and Biorefinery     Full-text available via subscription   (Followers: 1)
Journal of Chemical & Engineering Data     Full-text available via subscription   (Followers: 11)
Journal of Chemical and Biological Interfaces     Full-text available via subscription   (Followers: 1)
Journal of Chemical Ecology     Hybrid Journal   (Followers: 6)
Journal of Chemical Engineering     Open Access   (Followers: 19)
Journal of Chemical Engineering and Materials Science     Open Access   (Followers: 2)
Journal of Chemical Science and Technology     Open Access   (Followers: 4)
Journal of Chemical Sciences     Partially Free   (Followers: 17)
Journal of Chemical Technology & Biotechnology     Hybrid Journal   (Followers: 10)
Journal of Chemical Theory and Computation     Full-text available via subscription   (Followers: 15)
Journal of CO2 Utilization     Hybrid Journal   (Followers: 2)
Journal of Combinatorial Chemistry     Full-text available via subscription  
Journal of Crystallization Process and Technology     Open Access   (Followers: 8)
Journal of Environmental Chemical Engineering     Hybrid Journal   (Followers: 5)
Journal of Food Measurement and Characterization     Hybrid Journal  
Journal of Food Processing & Technology     Open Access   (Followers: 1)
Journal of Fuel Chemistry and Technology     Full-text available via subscription   (Followers: 4)
Journal of Geochemical Exploration     Hybrid Journal   (Followers: 1)
Journal of Industrial and Engineering Chemistry     Hybrid Journal   (Followers: 1)
Journal of Information Display     Hybrid Journal   (Followers: 1)
Journal of Inorganic and Organometallic Polymers and Materials     Partially Free   (Followers: 9)
Journal of Modern Chemistry & Chemical Technology     Full-text available via subscription   (Followers: 2)
Journal of Molecular Catalysis A: Chemical     Hybrid Journal   (Followers: 6)
Journal of Non-Crystalline Solids     Hybrid Journal   (Followers: 8)
Journal of Organic Semiconductors     Open Access   (Followers: 5)
Journal of Physics and Chemistry of Solids     Hybrid Journal   (Followers: 5)
Journal of Polymer and Biopolymer Physics Chemistry     Open Access   (Followers: 4)
Journal of Polymer Engineering     Hybrid Journal   (Followers: 9)
Journal of Polymer Research     Hybrid Journal   (Followers: 6)
Journal of Polymer Science Part C : Polymer Letters     Hybrid Journal   (Followers: 6)
Journal of Polymers     Open Access   (Followers: 3)
Journal of Polymers and the Environment     Hybrid Journal   (Followers: 1)
Journal of Pure and Applied Chemistry Research     Open Access   (Followers: 1)
Journal of the American Chemical Society     Full-text available via subscription   (Followers: 285)
Journal of the Bangladesh Chemical Society     Open Access  
Journal of the Brazilian Chemical Society     Open Access   (Followers: 2)
Journal of The Institution of Engineers (India) : Series E     Hybrid Journal   (Followers: 1)
Journal of the Pakistan Institute of Chemical Engineers     Open Access   (Followers: 1)
Journal of the Taiwan Institute of Chemical Engineers     Hybrid Journal   (Followers: 2)
Journal of Water Chemistry and Technology     Hybrid Journal   (Followers: 9)
Jurnal Bahan Alam Terbarukan     Open Access  
Jurnal Inovasi Pendidikan Kimia     Open Access   (Followers: 5)
Jurnal Reaktor     Open Access  
Jurnal Teknologi Dan Industri Pangan     Open Access   (Followers: 1)
Konversi     Open Access  
Korean Journal of Chemical Engineering     Hybrid Journal   (Followers: 3)
Main Group Metal Chemistry     Hybrid Journal   (Followers: 1)
Materials Chemistry and Physics     Full-text available via subscription   (Followers: 15)
Materials Science and Applied Chemistry     Open Access  
Materials Sciences and Applied Chemistry     Full-text available via subscription  
Modern Chemistry & Applications     Open Access  
Molecular Imprinting     Open Access  
Nanocontainers     Open Access  
Nanofabrication     Open Access  
Noise Control Engineering Journal     Full-text available via subscription   (Followers: 3)
Ochrona Srodowiska i Zasobów Naturalnych : Environmental Protection and Natural Resources     Open Access  
Petroleum Chemistry     Hybrid Journal   (Followers: 1)
Physics and Chemistry of Glasses - European Journal of Glass Science and Technology Part B     Full-text available via subscription   (Followers: 4)
Plasma Processes and Polymers     Hybrid Journal   (Followers: 1)
Plasmas and Polymers     Hybrid Journal  
Polymer     Hybrid Journal   (Followers: 113)
Polymer Bulletin     Hybrid Journal   (Followers: 7)
Polymer Composites     Hybrid Journal   (Followers: 15)
Polyolefins Journal     Open Access  
Powder Technology     Hybrid Journal   (Followers: 13)
Recyclable Catalysis     Open Access   (Followers: 1)
Research on Chemical Intermediates     Hybrid Journal  
Reviews in Chemical Engineering     Hybrid Journal   (Followers: 5)
Revista ION     Open Access  
Revista Mexicana de Ingeniería Química     Open Access  
Rubber Chemistry and Technology     Full-text available via subscription   (Followers: 2)
Russian Chemical Bulletin     Hybrid Journal   (Followers: 2)
Russian Journal of Applied Chemistry     Hybrid Journal   (Followers: 1)
Science and Engineering of Composite Materials     Hybrid Journal   (Followers: 60)
Solid Fuel Chemistry     Hybrid Journal  
South African Journal of Chemical Engineering     Open Access   (Followers: 2)
South African Journal of Chemistry     Open Access   (Followers: 2)
Surface Engineering and Applied Electrochemistry     Hybrid Journal   (Followers: 5)
Sustainable Chemical Processes     Open Access   (Followers: 2)
Synthesis Lectures on Chemical Engineering and Biochemical Engineering     Full-text available via subscription  
The Canadian Journal of Chemical Engineering     Hybrid Journal   (Followers: 3)
The Chemical Record     Hybrid Journal   (Followers: 1)
Theoretical Foundations of Chemical Engineering     Hybrid Journal   (Followers: 2)
Transition Metal Chemistry     Hybrid Journal   (Followers: 4)
Transylvanian Review of Systematical and Ecological Research     Open Access  
Visegrad Journal on Bioeconomy and Sustainable Development     Open Access   (Followers: 2)
Zeitschrift für Naturforschung B : A Journal of Chemical Sciences     Open Access   (Followers: 1)

           

Journal Cover Computational Biology and Chemistry
  [SJR: 0.491]   [H-I: 47]   [12 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1476-9271
   Published by Elsevier Homepage  [3031 journals]
  • Hidden Markov model and Chapman Kolmogrov for protein structures
           prediction from images
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Md. Sarwar Kamal, Linkon Chowdhury, Mohammad Ibrahim Khan, Amira S. Ashour, João Manuel R.S. Tavares, Nilanjan Dey
      Protein structure prediction and analysis are more significant for living organs to perfect asses the living organ functionalities. Several protein structure prediction methods use neural network (NN). However, the Hidden Markov model is more interpretable and effective for more biological data analysis compared to the NN. It employs statistical data analysis to enhance the prediction accuracy. The current work proposed a protein prediction approach from protein images based on Hidden Markov Model and Chapman Kolmogrov equation. Initially, a preprocessing stage was applied for protein images’ binarization using Otsu technique in order to convert the protein image into binary matrix. Subsequently, two counting algorithms, namely the Flood fill and Warshall are employed to classify the protein structures. Finally, Hidden Markov model and Chapman Kolmogrov equation are applied on the classified structures for predicting the protein structure. The execution time and algorithmic performances are measured to evaluate the primary, secondary and tertiary protein structure prediction.
      Graphical abstract image

      PubDate: 2017-04-21T02:31:08Z
       
  • Comprehensive Comparison of Two Protein Family of P-ATPases (13A1 and
           13A3) in Insects
    • Abstract: Publication date: Available online 15 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Samin Seddigh
      The P- type ATPases (P-ATPases) are present in all living cells where they mediate ion transport across membranes on the expense of ATP hydrolysis. Different ions which are transported by these pumps are protons like calcium, sodium, potassium, and heavy metals such as manganese, iron, copper, and zinc. Maintenance of the proper gradients for essential ions across cellular membranes makes P-ATPases crucial for cell survival. In this study, characterization of two families of P-ATPases including P-ATPase 13A1 and P-ATPase 13A3 protein was compared in two different insect species from different orders. According to the conserved motifs found with MEME, nine motifs were shared by insects of 13A1 family but eight in 13A3 family. Seven different insect species from 13A1 and five samples from 13A3 family were selected as the representative samples for functional and structural analyses. The structural and functional analyses were performed with ProtParam, SOPMA, SignalP 4.1, TMHMM 2.0, ProtScale and ProDom tools in the ExPASy database. The tertiary structure of Bombus terrestris as a sample of each family of insects were predicted by the Phyre2 and TM-score servers and their similarities were verified by SuperPose server. The tertiary structures were predicted via the “c3b9bA” model (PDB Accession Code: 3B9B) in P-ATPase 13A1 family and “c2zxeA” model (PDB Accession Code: 2ZXE) in P-ATPase 13A3 family. A phylogenetic tree was constructed with MEGA 6.06 software using the Neighbor-joining method. According to the results, there was a high identity of P-ATPase families so that they should be derived from a common ancestor however they belonged to separate groups. In protein–protein interaction analysis by STRING 10.0, six common enriched pathways of KEGG were identified in B. terrestris in both families. The obtained data provide a background for bioinformatic studies of the function and evolution of other insects and organisms.
      Graphical abstract image

      PubDate: 2017-04-21T02:31:08Z
       
  • Genome-wide predicting disease-related protein complexes by walking on the
           heterogeneous network based on data integration and laplacian
           normalization
    • Abstract: Publication date: Available online 13 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Zhiming Liu, Jiawei Luo
      Background Associating protein complexes to human inherited diseases is critical for better understanding of biological processes and functional mechanisms of the disease. Many protein complexes have been identified and functionally annotated by computational and purification methods so far, however, the particular roles they were playing in causing disease have not yet been well determined. Results In this study, we present a novel method to identify associations between protein complexes and diseases. First, we construct a disease-protein heterogeneous network based on data integration and laplacian normalization. Second, we apply a random walk with restart on heterogeneous network (RWRH) algorithm on this network to quantify the strength of the association between proteins and the query disease. Third, we sum over the scores of member proteins to obtain a summary score for each candidate protein complex, and then rank all candidate protein complexes according to their scores. With a series of leave-one-out cross-validation experiments, we found that our method not only possesses high performance but also demonstrates robustness regarding the parameters and the network structure. We test our approach with breast cancer and select top 20 highly ranked protein complexes, 17 of the selected protein complexes are evidenced to be connected with breast cancer. Conclusions Our proposed method is effective in identifying disease-related protein complexes based on data integration and laplacian normalization.
      Graphical abstract image

      PubDate: 2017-04-21T02:31:08Z
       
  • QSAR, docking studies of 1,3-thiazinan-3-yl isonicotinamide derivatives
           for antitubercular activity
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Trupti S. Chitre, Kalyani D. Asgaonkar, Shital M. Patil, Shiva Kumar, Vijay M. Khedkar, Dinesh R. Garud
      The enzyme – enoyl acyl carrier protein reductase (enoyl ACP reductase) is a validated target for antitubercular activity. Inhibition of this enzyme interferes with mycolic acid synthesis which is crucial for Mycobacterium tuberculosis cell growth. In the present work 2D and 3D quantitative structure activity relationship (QSAR) studies were carried out on a series of thiazinan–Isoniazid pharmacophore to design newer analogues. For 2D QSAR, the best statistical model was generated using SA-MLR method (r 2 =0.958, q 2 =0.922) while 3D QSAR model was derived using the SA KNN method (q 2 =0.8498). These studies could guide the topological, electrostatic, steric, hydrophobic substitutions around the nucleus based on which the NCEs were designed. Furthermore, molecular docking was performed to gauze the binding affinity of the designed analogues for enoyl ACP reductase enzyme. Amongst all the designed analogues the binding energies of SKS 01 and SKS 05 were found to be −5.267kcal/mol and −5.237kcal/mol respectively which was comparable with the binding energy of the standard Isoniazid (−6.254kcal/mol).
      Graphical abstract image

      PubDate: 2017-04-13T20:03:39Z
       
  • Prediction and feature analysis of intron retention events in plant genome
    • Abstract: Publication date: Available online 13 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Ying Cui, Chao Zhang, Meng Cai
      Alternative splicing (AS) is a major contributor to increase the potential informational content of eukaryotic genomes by creating multiple mRNA species and proteins from a single gene. In plants, up to 60% genes are alternatively spliced and the most common type of AS is intron retention (IR). Genomic analyses of IR have illuminated its crucial role in shaping the evolution of genomes, in the control of developmental processes, and in the dynamic regulation of the transcriptome to influence phenotype. To explore the relationship between the sequence feature and the formation mechanism of IR, we statistically analyzed the retained introns and proposed an improved random forest-based hybrid method to predict intron retention events in plant genome. The results indicate that IR has significant relationship with individual introns which have weaker 5' splice sites, lower GC content and less termination codon occurrence. By the method we proposed, 93.48% retained introns can be correctly distinguished from constitutive introns. Strikingly, our study will facilitate a better understanding of underlying mechanisms involved in intron retention.
      Graphical abstract image

      PubDate: 2017-04-13T20:03:39Z
       
  • Discovery of Potential Inhibitor against Human Acetylcholinesterase: A
           Molecular Docking and Molecular Dynamics Investigation
    • Abstract: Publication date: Available online 12 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Surya Pratap Singh, Dwijendra Gupta
      Alzheimer’s disease (AD) is a progressive neurodegenerative disease of central nervous system among elderly people. Human acetylcholinesterase (hAChE), an important enzyme in neuronal signaling, is responsible for the degradation of acetylcholine which in turn prevents the post synaptic signal transmissions. hAChE has been an attractive target of drug discovery for the search of therapeutics against AD. In the recent past hAChE has become hot target for the investigation of new potential therapeutics. We performed virtual screening of entire database against hAChE. Further, the extra precision molecular docking was carried out to refine the docking results and the best complex was passed for molecular dynamics simulations in order of understanding the hAChE dynamics and its behavior in complex with the ligand which corroborate the outcomes of virtual screening. This also provides binding free energy data that establishes the ligands efficiency for inhibiting hAChE. The computational findings discussed in this paper provide initial information of inhibitory effects of ligand, (drugbank entry DB00983), over hAChE.
      Graphical abstract image

      PubDate: 2017-04-13T20:03:39Z
       
  • Structural space of intramolecular peptide disulfides: Analysis of peptide
           toxins retrieved from venomous peptide databases
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Panchada Ch. V. Govindu, Priyanka Chakraborty, Angshu Dutta, Konkallu Hanumae Gowd
      Structural space of intramolecular peptide disulfides is the combination of arrangement of even number of cysteine residues in single polypeptide and the disulfide isomers resulting from differential connectivity between cysteine residues. In the current report, we are documenting theoretical analysis and derivation of general formula [ 2 × 4 { ( n 2 ) − 1 } ] to predict possible distinct cysteine patterns for given ‘n’ even number of cysteine residues in a sequence. Combined formula of predicting distinct cysteine patterns and different disulfide isomers can be used to deduce the truly available structural space of intramolecular peptide disulfides, which may be used in structural analysis of disulfide rich peptides and proteins. In this report, we have also analyzed cysteine patterns and disulfide connectivities of peptide toxins, which is the largest group of intramolecular peptide disulfide natural products, retrieved from publically available animal toxin databases. Observed 29 distinct cysteine patterns of toxins exhibited 61 unique intramolecular disulfide folds, with limitation of having up to eight cysteine residues in a sequence, compared to theoretically available 170 different cysteine patterns generating 13,946 distinct intramolecular disulfide folds. Database analysis of peptide toxins has also revealed the features of presence of same intramolecular disulfide motif in functionally divergent peptide toxins and adaptation of the same disulfide fold with similar functions in different venomous species. Calculations of relative accessible surface area of cystine and average value of non-cysteine residues in the representative intramolecular disulfide folds of peptide toxins has revealed the feature of poor accessibility of cystine to external agents and their dependency on number of disulfide bonds in the sequence. Implementation of new generation sequencing methods and novel disulfide mapping techniques will unravel hidden diversity of intramolecular disulfide motifs of toxins and current report points to the selection of disulfide motifs in peptide toxins.
      Graphical abstract image

      PubDate: 2017-04-06T19:51:34Z
       
  • DrugClust: A machine learning approach for drugs side effects prediction
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Giovanna Maria Dimitri, Pietro Lió
      Background Identification of underlying mechanisms behind drugs side effects is of extreme interest and importance in drugs discovery today. Therefore machine learning methodology, linking such different multi features aspects and able to make predictions, are crucial for understanding side effects. Methods In this paper we present DrugClust, a machine learning algorithm for drugs side effects prediction. DrugClust pipeline works as follows: first drugs are clustered with respect to their features and then side effects predictions are made, according to Bayesian scores. Biological validation of resulting clusters can be done via enrichment analysis, another functionality implemented in the methodology. This last tool is of extreme interest for drug discovery, given that it can be used as a validation of the clusters obtained, as well as for the study of new possible interactions between certain side effects and nontargeted pathways. Results Results were evaluated on a 5-folds cross validations procedure, and extensive comparisons were made with available datasets in the field: Zhang et al. (2015), Liu et al. (2012) and Mizutani et al. (2012). Results are promising and show better performances in most of the cases with respect to the available literature. Availability DrugClust is an R package freely available at: https://cran.r-project.org/web/packages/DrugClust/index.html.
      Graphical abstract image Highlights

      PubDate: 2017-04-06T19:51:34Z
       
  • A novel way of comparing protein sequences represented under
           physio-chemical properties of their amino acids
    • Abstract: Publication date: Available online 5 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Jayanta Pal, Soumen Ghosh, Bansibadan Maji, Dilip Kumar Bhattacharya
      The paper considers representation of Protein sequences based on numerical values associated with Physio- chemical properties of their amino acids and uses FFT on the represented time series to get the spectrum. Based on the analysis of this spectrum by ICD method, it compares Protein sequences of ND4, ND5 and ND6 category. Three types of Physio-chemical properties are used for the representation. The results of comparison of Protein sequences based on each type of physio-chemical property are now compared among themselves and the process is repeated for all categories of protein sequences separately. It is found that the property of polarity (Hydropathy Index) is the best one in such protein sequence comparison. Finally the results of comparison of Protein sequences based on Hydropathy index are compared with the results obtained earlier by other methods on the same category of Protein sequences to verify the effectiveness of our method.

      PubDate: 2017-04-06T19:51:34Z
       
  • Node-based differential network analysis in genomics
    • Abstract: Publication date: Available online 4 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Xiao-Fei Zhang, Le Ou-Yang, Hong Yan
      Gene dependency networks often undergo changes in response to different conditions. Understanding how these networks change across two conditions is an important task in genomics research. Most previous differential network analysis approaches assume that the difference between two condition-specific networks is driven by individual edges. Thus, they may fail in detecting key players which might represent important genes whose mutations drive the change of network. In this work, we develop a node-based differential network analysis (N-DNA) model to directly estimate the differential network that is driven by certain hub nodes. We model each condition-specific gene network as a precision matrix and the differential network as the difference between two precision matrices. Then we formulate a convex optimization problem to infer the differential network by combing a D-trace loss function and a row-column overlap norm penalty function. Simulation studies demonstrate that N-DNA provides more accurate estimate of the differential network than previous competing approaches. We apply N-DNA to ovarian cancer and breast cancer gene expression data. The model rediscovers known cancer-related genes and contains interesting predictions.

      PubDate: 2017-04-06T19:51:34Z
       
  • GQSAR Modeling and Combinatorial library generation of
           4-phenylquinazoline-2-carboxamide derivatives as Antiproliferative Agents
           in Human Glioblastoma Tumors
    • Abstract: Publication date: Available online 4 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Debolina Goswami, Sukriti Goyal, Salma Jamal, Ritu Jain, Divya Wahi, Abhinav Grover
      Background TSPO translocator protein, encoded in humans by the Tspo gene plays a crucial role in mitochondria mediated apoptosis and necrotic cell death through its association with Mitochondrial Permeability Transition pore (MPTP). It has been shown that this function can be exploited as a potential treatment for human Glioblastoma Multiforme. In this study, a novel robust fragment based QSAR model has been developed for a series of 4-phenylquinazoline-2-carboxamides experimentally known to be ligands for TSPO, thus triggering apoptotic mechanism cascade. Results Model developed showed satisfactory statistical parameters for the experimentally reported dataset (r2 =0.8259, q2 =0.6788, pred_r2 =0.8237 and F-test=37.9). Low standard error values (r2_se=0.253, q2_se=0.34, pred_r2_se=0.14) confirmed the accuracy of the generated model. The model obtained had 4 descriptors, namely, R1-Volume, R2-SsCH3E-index, R3-SsCH3count and R5-EpsilonR. Two of them had positive contribution while the other two had negative correlation. Conclusion The high binding affinity and the presence of essential structural features in these compounds make them an ideal choice for the consideration as potent anti-GBM drugs. Activity predicted by GQSAR model reinforces their potential as worthy candidates for drugs against GBM. The detailed analysis carried out in this study provides a substantial basis for the prospective design and development of novel 4-phenylquinazoline-2-carboxamide compounds as TSPO ligands capable of inducing apoptosis in cancer cells.

      PubDate: 2017-04-06T19:51:34Z
       
  • Optimal hybrid sequencing and assembly: Feasibility conditions for
           accurate genome reconstruction and cost minimization strategy
    • Abstract: Publication date: Available online 3 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Chun-Chi Chen, Noushin Ghaffari, Xiaoning Qian, Byung-Jun Yoon
      Recent advances in high-throughput genome sequencing technologies have enabled the systematic study of various genomes by making whole genome sequencing affordable. Modern sequencers generate a huge number of small sequence fragments called reads, where the read length and the per-base sequencing cost depend on the technology used. To date, many hybrid genome assembly algorithms have been developed that can take reads from multiple read sources to reconstruct the original genome. However, rigorous investigation of the feasibility conditions for complete genome reconstruction and the optimal sequencing strategy for minimizing the sequencing cost has been conspicuously missing. An important aspect of hybrid sequencing and assembly is that the feasibility conditions for genome reconstruction can be satisfied by different combinations of the available read sources, opening up the possibility of optimally combining the sources to minimize the sequencing cost while ensuring accurate genome reconstruction. In this paper, we derive the conditions for whole genome reconstruction from multiple read sources at a given confidence level and also introduce the optimal strategy for combining reads from different sources to minimize the overall sequencing cost. We show that the optimal read set, which simultaneously satisfies the feasibility conditions for genome reconstruction and minimizes the sequencing cost, can be effectively predicted through constrained discrete optimization. Through extensive evaluations based on several genomes and different read sets, we verify the derived feasibility conditions and demonstrate the performance of the proposed optimal hybrid sequencing and assembly strategy.

      PubDate: 2017-04-06T19:51:34Z
       
  • Dihydropyrazole and dihydropyrrole structures based design of Kif15
           inhibitors as novel therapeutic agents for cancer
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Jomon Sebastian
      Mitotic Kinesin motors, Eg5 and Kif15, have recently emerged as good targets for cancer as they play an inevitable role during mitosis. But, most of the Eg5 inhibitors were found ineffective when the cancer cells develop resistance to them by escalating the expression of Kif15 as alternative to Eg5. Therefore, the drugs that target Kif15 became necessary to be used either as a single or in combination with Eg5 inhibitors. The present study used 39 dihydropyrazole and 13 dihydropyrrole derivatives that were having in vitro inhibitory potential against kinesin motors to develop a common pharmacophore hypothesis AHRR and atom-based QSAR model. The model was used for virtual screening of ZINC database and the resultant hits were docked against Kif15. The four drug candidates with high docking score were examined for their activity and pharmacokinetic behaviour. Based on the results these drugs could be considered as lead candidates in further drug development for cancer.
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      PubDate: 2017-03-30T19:28:06Z
       
  • Pharmacoinformatics exploration of polyphenol oxidases leading to novel
           inhibitors by virtual screening and molecular dynamic simulation study
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Mubashir Hassan, Qamar Abbas, Zaman Ashraf, Ahmed A. Moustafa, Sung-Yum Seo
      Polyphenol oxidases (PPOs)/tyrosinases are metal-dependent enzymes and known as important targets for melanogenesis. Although considerable attempts have been conducted to control the melanin-associated diseases by using various inhibitors. However, the exploration of the best anti-melanin inhibitor without side effect still remains a challenge in drug discovery. In present study, protein structure prediction, ligand-based pharmacophore modeling, virtual screening, molecular docking and dynamic simulation study were used to screen the strong novel inhibitor to cure melanogenesis. The 3D structures of PPO1 and PPO2 were built through homology modeling, while the 3D crystal structures of PPO3 and PPO4 were retrieved from PDB. Pharmacophore modeling was performed using LigandScout 3.1 software and top five models were selected to screen the libraries (2601 of Aurora and 727, 842 of ZINC). Top 10 hit compounds (C1-10) were short-listed having strong binding affinities for PPO1-4. Drug and synthetic accessibility (SA) scores along with absorption, distribution, metabolism, excretion and toxicity (ADMET) assessment were employed to scrutinize the best lead hit. C4 (name) hit showed the best predicted SA score (5.75), ADMET properties and drug-likeness behavior among the short-listed compounds. Furthermore, docking simulations were performed to check the binding affinity of C1-C10 compounds against target proteins (PPOs). The binding affinity values of complex between C4 and PPOs were higher than those of other complexes (−11.70, −12.1, −9.90 and −11.20kcal/mol with PPO1, PPO2, PPO3, or PPO4, respectively). From comparative docking energy and binding analyses, PPO2 may be considered as better target for melanogenesis than others. The potential binding modes of C4, C8 and C10 against PPO2 were explored using molecular dynamics simulations. The root mean square deviation and fluctuation (RMSD/RMSF) graphs results depict the significance of C4 over the other compounds. Overall, bioactivity and ligand efficiency profiles suggested that the proposed hit may be more effective inhibitors for melanogenesis.
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      PubDate: 2017-03-24T19:15:31Z
       
  • Structural modeling of human organic cation transporters
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Tikam Chand Dakal, Rajender Kumar, Dindial Ramotar
      Human organic cation transporters (hOCTs) belong to solute carriers (SLC) 22 family of membrane proteins that play a central role in transportation of chemotherapeutic drugs for several clinical and pathological conditions, including cancer and diabetes. These transporters mediate drug transport; however, the precise mechanism of drug-binding and transport by them is not fully uncovered yet, partly due to unavailability of any crystal structure record. In this work, we performed a multi-phasic approach to compute the 3D structural models of seven human organic cation transporters (hOCTs) starting from primary protein sequence. Our structure modeling approach included 1) I-TASSER based comparative sequence alignment, threading and ab-initio protein modeling; 2) models comparison with PSIPRED secondary structure prediction; 3) loop modeling for incongruent secondary structure in Chimera 1.10.1; 4) high resolution structure simulation, refinement, energy minimization using ModRefiner, and 5) validation of the structure models using PROCHECK at SAVEs. From structural point, the computed 3D structures of hOCTs consist of a typical major facilitator superfamily (MFS) fold of twelve α-transmembrane helix domains arranged in a manner rendering hOCTs a barrel shaped structure with a large cleft that opens in cytoplasm. The modeled 3D structure of all hOCTs closely resemble to human SLC2A3 (GLUT3) transporter (PDB ID: 5c65) and displayed an outward-open confirmation and putative cyclic C1 protein symmetry. In addition, hOCTs has a large (>100 amino acids) unique extracellular loop between TMH1 and TMH2 having potential glycosylation sites (Asn-Xaa-Ser/Thr) and cysteine residues, both features indicative of putative role in drug binding and uptake. There is an intracellular three/four-helix loop between TMH6 and TMH7 containing putative phosphorylation sites for precise regulation of hOCTs function as drug transporters. There are nine loops of 4 to 11 amino acids length that protrude from membrane, both intracellularly and extracellularly, and connect adjacent TMHs. The 2D structure prediction showed Nin-Cin topology of all hOCTs. In the unavailability of the crystal structures of hOCTs, the 3D structural models computed in-silico and presented herein can be used for studying the mechanism of drug binding and transport by hOCTs.
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      PubDate: 2017-03-24T19:15:31Z
       
  • Chemical Principles Additive Model Aligns Low Consensus DNA Targets of p53
           Tumor Suppressor Protein
    • Abstract: Publication date: Available online 22 March 2017
      Source:Computational Biology and Chemistry
      Author(s): Kelly M. Thayer, In Sub M. Han
      Computational prediction of the interaction between protein transcription factors and their cognate DNA binding sites in genomic promoters constitutes a formidable challenge in two situations: when the number of discriminatory interactions is small compared to the length of the binding site, and when DNA binding sites possess a poorly conserved consensus binding motif. The transcription factor p53 tumor suppressor protein and its target DNA exhibit both of these issues. From crystal structure analysis, only three discriminatory amino acid side chains contact the DNA for a binding site spanning ten base pairs. Furthermore, our analysis of a dataset of genome wide fragments binding to p53 revealed many sequences lacking the expected consensus. The low information content leads to an overestimation of binding sites, and the lack of conservation equates to a computational alignment problem. Within a fragment of DNA known to bind to p53, computationally locating the position of the site equates to aligning the DNA with the binding interface. From a molecular perspective, that alignment implies a specification of which DNA bases are interacting with which amino acid side chains, and aligning many sequences to the same protein interface concomitantly produces a multiple sequence alignment. From this vantage, we propose to cast prediction of p53 binding sites as an alignment to the protein binding surface with the novel approach of optimizing the alignment of DNA fragments to the p53 binding interface based on chemical principles. A scoring scheme based on this premise was successfully implemented to score a dataset of biological DNA fragments known to contain p53 binding sites. The results illuminate the mechanism of recognition for the protein-DNA system at the forefront of cancer research. These findings implicate that p53 may recognize its target binding sites via several different mechanisms which may include indirect readout.
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      PubDate: 2017-03-24T19:15:31Z
       
  • In Silico Analysis of Nonsynonymous Single Nucleotide Polymorphisms of the
           Human Adiponectin Receptor 2 (ADIPOR2) Gene
    • Abstract: Publication date: Available online 14 March 2017
      Source:Computational Biology and Chemistry
      Author(s): Md. Solayman, Md. Abu Saleh, Sudip Paul, Md. Ibrahim Khalil, Siew Hua Gan
      Polymorphisms of the ADIPOR2 gene are frequently linked to a higher risk of developing diseases including obesity, type 2 diabetes and cardiovascular diseases. Though mutations of the ADIPOR2 gene are detrimental, there is a lack of comprehensive in silico analyses of the functional and structural impacts at the protein level. Considering the involvement of ADIPOR2 in glucose uptake and fatty acid oxidation, an in silico functional analysis was conducted to explore the possible association between genetic mutations and phenotypic variations. A genomic analysis of 82 nonsynonymous SNPs in ADIPOR2 was initiated using SIFT followed by the SNAP2, nsSNPAnalyzer, PolyPhen-2, SNPs&GO, FATHMM and PROVEAN servers. A total of 10 mutations (R126W, L160Q, L195P, F201S, L235R, L235P, L256R, Y328H, E334K and Q349H) were predicted to have deleterious effects on the ADIPOR2 protein and were therefore selected for further analysis. Theoretical models of the variants were generated by comparative modelling via MODELLER 9.16. A protein structural analysis of these amino acid variants was performed using SNPeffect, I-Mutant, ConSurf, Swiss PDB viewer and NetSurfP to explore their solvent accessibility, molecular dynamics and energy minimization calculations. In addition, FTSite was used to predict the ligand binding sites, while NetGlycate, NetPhos2.0, UbPerd and SUMOplot were used to predict post-translational modification sites. All of the variants showed increased free energy, though F201S exhibited the highest energy increase. The root mean square deviation values of the modelled mutants strongly indicated likely pathogenicity. Remarkably, three binding sites were detected on ADIPOR2, and two mutations at positions 328 and 201 were found in the first and second binding pockets, respectively. Interestingly, no mutations were found at the post-translational modification sites. These genetic variants can provide a better understanding of the wide range of disease susceptibility associated with ADIPOR2 and aid the development of new molecular diagnostic markers for these diseases. The findings may also facilitate the development of novel therapeutic elements for associated diseases.
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      PubDate: 2017-03-24T19:15:31Z
       
  • A Bioinformatics approach to designing a Zika virus vaccine
    • Abstract: Publication date: Available online 10 March 2017
      Source:Computational Biology and Chemistry
      Author(s): Sumanta Dey, Ashesh Nandy, Subhash C. Basak, Papiya Nandy, Sukhen Das
      The Zika virus infections have reached epidemic proportions in the Latin American countries causing severe birth defects and neurological disorders. While several organizations have begun research into design of prophylactic vaccines and therapeutic drugs, computer assisted methods with adequate data resources can be expected to assist in these measures to reduce lead times through bioinformatics approaches. Using 60 sequences of the Zika virus envelope protein available in the GenBank database, our analysis with numerical characterization techniques and several web based bioinformatics servers identified four peptide stretches on the Zika virus envelope protein that are well conserved and surface exposed and are predicted to have reasonable epitope binding efficiency. These peptides can be expected to form the basis for a nascent peptide vaccine which, enhanced by incorporation of suitable adjuvants, can elicit immune response against the Zika virus infections.
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      PubDate: 2017-03-13T02:26:51Z
       
  • Pharmacophore based 3D-QSAR Modeling, Virtual Screening and Docking for
           Identification of Potential Inhibitors of β-secretase
    • Abstract: Publication date: Available online 6 March 2017
      Source:Computational Biology and Chemistry
      Author(s): Ravichand Palakurti, Ramakrishna Vadrevu
      The enzyme β-secretase-1 is responsible for the cleavage of the amyloid precursor protein, a vital step in the process of the formation of amyloid-β peptides which are known to lead to neurodegeneration causing Alzheimer’s disease. Challenges associated with toxicity and blood brain permeation inability of potential inhibitors, continue to evade a successful therapy, thus demanding the search and development of highly active and effective inhibitors. Towards these efforts, we used a ligand based pharmacophore model generation from a dataset of known inhibitors whose activities against β-secretase hovered in the nano molar range. The identified 5 feature pharmacophore model, AHHPR, was validated via three dimensional quantitative structure activity relationship as indicated by r2, q2 and Pearson R values of 0.9013, 0.7726 and 0.9041 respectively. For a dataset of compounds with nano molar activity, the important pharmacophore features present in the current model appear to be similar with those observed in the models resulting from much wider activity range of inhibitors. Virtual screening of the ChemBridge CNS-Set™, a database having compounds with a better suitability for central nervous system based disorders followed by docking and analysis of the ligand protein interactions resulted in the identification of eight prospective compounds with considerable diversity. The current pharmacophore model can thus be useful for the identification, design and development of potent β-secretase inhibitors which by optimization can be potential therapeutics for Alzheimer’s disease.
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      PubDate: 2017-03-10T02:21:39Z
       
  • Structure based design, synthesis and biological evaluation of amino
           phosphonate derivatives as human glucokinase activators
    • Abstract: Publication date: Available online 2 March 2017
      Source:Computational Biology and Chemistry
      Author(s): Nanda Kumar Yellapu, Raveendra Babu Kilaru, Nagaraju Chamarthi, PVGK Sarma, Bhaskar Matcha
      Glucokinase (GK) is a potential therapeutic target of type 2 diabetes and GK activators (GKAs) represent a promising class of small organic molecules which enhance GK activity. Based on the configuration and conformation of the allosteric site of GK, we have designed a novel class of amino phosphonate derivatives in order to develop potent GKAs. The QSAR model developed using numerous descriptors revealed its potential with the best effective statistical values of RMSE=1.52 and r 2 =0.30. Moreover, application of this model on the present test set GKAs proved to be worthy to predict their activities as a better linear relationship was observed with RMSE=0.14 and r 2 =0.88. ADME studies and Lipinski filters encouraged them as safer therapeutics. The molecular dynamics and docking studies against the GK allosteric site revealed that all GKAs bind with best affinities and the complexes are strengthened by H-bonding, phosphonate salt bridges, hydrophobic and arene cat ionic interactions. Finally, in vitro evaluation with human liver GK revealed their potential to increase the GK activity by different folds. The results from QSAR, ADME, molecular docking and in vitro assays strongly suggested that the present molecules could be used as effective and safer therapeutics to control and manage type 2 diabetes.
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      PubDate: 2017-03-05T08:43:57Z
       
  • Genome-wide identification and characterization of conserved and novel
           microRNAs in grass carp (Ctenopharyngodon idella) by deep sequencing
    • Abstract: Publication date: Available online 2 March 2017
      Source:Computational Biology and Chemistry
      Author(s): Wangbao Gong, Yong Huang, Jun Xie, Guangjun Wang, Deguang Yu, Xihong Sun
      MicroRNAs (miRNAs) are post-transcriptional regulators which bind to target to regulate protein expression by repressing translation or promoting degradation of the target mRNA. Studies have shown that deep sequencing is a powerful tool for the identification of miRNAs, and it is believed that may more miRNAs remain to be discovered in grass carp. In the present study, a pool of equal amounts of RNA obtained from 8 different adult grass carp tissues (spleen, liver, muscle, kidney, skin, testis, gut and heart) was sequenced using deep sequencing technology. A total of 16.579.334 raw reads were yielded from the pooled small RNA library. Using bioinformatics analysis, we identified 160 conserved miRNAs and 18 novel miRNAs in grass carp. Randomly selected 6 conserved and 2 novel miRNAs were confirmed their expression by stem-loop qRT-PCR assay. Furthermore, the 1212 potential targets of these miRNAs were predicted using miRNA target prediction tool. GO and KEGG pathway enrichment analyses indicated relevant biological processes. Our study provides the first genome-wide investigation of miRNAs from 8 mixed tissues of grass carp, and the data obtained expand the known grass carp miRNA inventory and provide a basis for further understanding functions of grass carp miRNAs.
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      PubDate: 2017-03-05T08:43:57Z
       
  • Pathway cross-talk network analysis identifies critical pathways in
           neonatal sepsis
    • Abstract: Publication date: Available online 27 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Yu-Xiu Meng, Quan-Hong Liu, Deng-Hong Chen, Ying Meng
      Background Despite advances in neonatal care, sepsis remains a major cause of morbidity and mortality in neonates worldwide. Pathway cross-talk analysis might contribute to the inference of the driving forces in bacterial sepsis and facilitate a better understanding of underlying pathogenesis of neonatal sepsis. Objective This study aimed to explore the critical pathways associated with the progression of neonatal sepsis by the pathway cross-talk analysis. Methods By integrating neonatal transcriptome data with known pathway data and protein-protein interaction data, we systematically uncovered the disease pathway cross-talks and constructed a disease pathway cross-talk network for neonatal sepsis. Then, attract method was employed to explore the dysregulated pathways associated with neonatal sepsis. To determine the critical pathways in neonatal sepsis, rank product (RP) algorithm, centrality analysis and impact factor (IF) were introduced sequentially, which synthetically considered the differential expression of genes and pathways, pathways cross-talks and pathway parameters in the network. The dysregulated pathways with the highest IF values as well as RP<0.01 were defined as critical pathways in neonatal sepsis. Results By integrating three kinds of data, only 6,919 common genes were included to perform the pathway cross-talk analysis. By statistic analysis, a total of 1,249 significant pathway cross-talks were selected to construct the pathway cross-talk network. Moreover, 47 dys-regulated pathways were identified via attract method, 20 pathways were identified under RP<0.01, and the top 10 pathways with the highest IF were also screened from the pathway cross-talk network. Among them, we selected 8 common pathways, i.e. critical pathways. Conclusions In this study, we systematically tracked 8 critical pathways involved in neonatal sepsis by integrating attract method and pathway cross-talk network. These pathways might be responsible for the host response in infection, and of great value for advancing diagnosis and therapy of neonatal sepsis.
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      PubDate: 2017-03-05T08:43:57Z
       
  • IFC Editorial Board
    • Abstract: Publication date: April 2017
      Source:Computational Biology and Chemistry, Volume 67


      PubDate: 2017-02-26T08:37:29Z
       
  • Title page
    • Abstract: Publication date: April 2017
      Source:Computational Biology and Chemistry, Volume 67


      PubDate: 2017-02-26T08:37:29Z
       
  • Exploration of Interaction Zones of β-tubulin Colchicine Binding Domainof
           Helminths and Binding Mechanism of Anthelmintics
    • Abstract: Publication date: Available online 24 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Prabodh Ranjan, Sivakumar Prasanth Kumar, Vijayakrishna Kari, Prakash Chandra Jha
      Numerous studies postulated the possible modes of anthelmintic activity by targeting alternate or extended regions of colchicine binding domain of helminth β-tubulin. We present three interaction zones (zones vide −1 to −3) in the colchicine binding domain of Haemonchus contortus (a helminth) β-tubulin homology model and developed zone-wise structure-based pharmacophore models coupled with molecular docking technique to unveil the binding hypotheses. The resulted ten structure-based hypotheses were then refined to essential three point pharmacophore features that captured recurring and crucial non-covalent receptor contacts and proposed three characteristics necessary for optimal zone-2 binding: a conserved pair of H bond acceptor (HBA to form H bond with Asn226 residue) and an aliphatic moiety of molecule separated by 3.75±0.44Å. Further, an aliphatic or a heterocyclic group distant (11.75±1.14Å) to the conserved aliphatic site formed the third feature component in the zone-2 specific anthelmintic pharmacophore model. Alternatively, an additional HBA can be substituted as a third component to establish H bonding with Asn204. We discern that selective zone-2 anthelmintics can be designed effectively by closely adapting the pharmacophore feature patterns and its geometrical constraints.
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      PubDate: 2017-02-26T08:37:29Z
       
  • Comparative and Evolutionary Studies of Mammalian Arylsulfatase and
           Sterylsulfatase Genes and Proteins Encoded on the X-Chromosome
    • Abstract: Publication date: Available online 24 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Roger S. Holmes
      At least 19 sulfatase genes have been reported on the human genome, including four arylsulfatase (ARS) genes (ARSD; ARSE; ARSF; ARSH) and a sterylsulfatase (STS) gene located together on the X-chromosome. Bioinformatic analyses of mammalian genomes were undertaken using known human STS and ARS amino acid sequences to study the evolution of these genes and proteins encoded on eutherian and marsupial genomes. Several domain regions and key residues were conserved including signal peptides, active site residues, metal (Ca2+) and substrate binding sequences, transmembranes and N-glycosylation sites. Phylogenetic analyses describe the relationships and potential origins of these genes during mammalian evolution. Primate ARSH enzymes lacked signal peptide sequences which may influence their biological functions. CpG117 and CpG92 were detected within the 5′ region of the human STS and ARSD genes, respectively, and miR-205 within the 3′-UTR for the human STS gene, using bioinformatic methods A proposal is described for a primordial invertebrate STS-like gene serving as an ancestor for unequal cross over events generating the gene complex on the eutherian mammalian X-chromosome.

      PubDate: 2017-02-26T08:37:29Z
       
  • IDENTIFICATION OF NOVEL ANTI CANCER AGENTS BY APPLYING INSILICO METHODS
           FOR INHIBITION OF TSPO PROTEIN
    • Abstract: Publication date: Available online 14 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Manan Bhargavi, Sree Kanth Sivan, Sarita Rajender Potlapally
      Cancer is a genomic disease characterised as impaired cellular energy metabolism. Cancer cells derive most of their energy from oxidative phosphorylation unlike normal ones during cell progression TSPO protein present in external mitochondrial membrane, is involved in various cellular functions like Cell proliferation, mitochondrial respiration, synthesis of steroids and also participates in import of cholesterol into the inner mitochondrial membrane from outside of the membrane of mitochondria. The 3D model of TSPO protein is built using comparative homology modelling techniques and validated by proSA, Ramachandran plot and ERRAT in the present work. Active site prediction is carried out using SiteMap and literature, which allows the prediction of the important binding pockets for the identification of putative active site. New molecular entities as TSPO inhibitors were obtained from Virtual screening using MS Spectrum databank in Schrodinger suite and were prioritised based on Glide Score. Docking was performed using Autodock to identify molecules with different scaffolds and were prioritised based on binding energy and RMSD values. Qikprop is used to calculate pharmacokinetic properties of the screened molecules which are found to be in permissible range as possible novel inhibitors of TSPO protein to supress cell proliferation.
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      PubDate: 2017-02-19T07:37:52Z
       
  • Enhanced identification of β-lactamases and its classes using sequence,
           physicochemical and evolutionary information with sequence feature
           characterization of the classes
    • Abstract: Publication date: Available online 14 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Abhigyan Nath, S. Karthikeyan
      β-lactamases provides one of the most successful means of evading the therapeutic effects of β lactam class of antibiotics by many gram positive and gram negative bacteria. On the basis of sequence identity, β-lactamases have been identified into four distinct classes- A, B, C and D. The classes A, C and D are the serine β-lactamases and class B is the metallo-lactamse. In the present study, we developed a two stage cascade classification system. The first-stage performs the classification of β-lactamases from non-β-lactamases and the second-stage performs the further classification of β-lactamases into four different β–lactamase classes. In the first-stage binary classification, we obtained an accuracy of 97.3% with a sensitivity of 89.1% and specificity of 98.0% and for the second stage multi-class classification, we obtained an accuracy of 87.3% for the class A, 91.0% for the class B, 96.3% for the class C and 96.4% for class D. A systematic statistical analysis is carried out on the sieved-out, correctly-predicted instances from the second stage classifier, which revealed some interesting patterns. We analyzed different classes of β-lactamases on the basis of sequence and physicochemical property differences between them. Among amino acid composition, H, W, Y and V showed significant differences between the different β-lactamases classes. Differences in average physicochemical properties are observed for isoelectric point, volume, flexibility, hydrophobicity, bulkiness and charge in one or more β-lactamase classes. The key differences in physicochemical property groups can be observed in small and aromatic groups. Among amino acid property group n-grams except charged n-grams, all other property group n-grams are significant in one or more classes. Statistically significant differences in dipeptide counts among different β-lactamase classes are also reported.
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      PubDate: 2017-02-19T07:37:52Z
       
  • In silico structural and functional analysis of Mesorhizobium ACC
           deaminase
    • Abstract: Publication date: Available online 11 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Krishnendu Pramanik, Tithi Soren, Soumik Mitra, Tushar Kanti Maiti
      Nodulation is one of the very important processes of legume plants as it is the initiating event of fixing nitrogen. Although ethylene has essential role in normal plant metabolism but it has also negative impact on plants particularly in nodule formation in legume plants. It is also produced due to a variety of biotic or abiotic stresses. 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase is a rhizobial enzyme which cleaves ACC (immediate precursor of ethylene) into α-ketobutyrate and ammonia. As a result, the level of ethylene from the plant cells is decreased and the negative impact of ethylene on nodule formation is reduced. ACC deaminase is widely studied in several plant growth promoting rhizobacterial (PGPR) strains including many legume nodulating bacteria like Mesorhizobium sp. It is an important symbiotic nitrogen fixer belonging to the class – alphaproteobacteria under the order Rhizobiales. ACC deaminase has positive role in Legume-rhizobium symbiosis. Rhizobial ACC deaminase has the potentiality to reduce the adverse effects of ethylene, thereby triggering the nodulation process. The present study describes an in silico comparative structural (secondary structure prediction, homology modeling) and functional analysis of ACC deaminase from Mesorhizobium spp. to explore physico-chemical properties using a number of bio-computational tools. M. loti was selected as a representative species of Mesorhizobium genera for 3D modelling of ACC deaminase protein. Correlation by the phylogenetic relatedness on the basis of both ACC deaminase enzymes and respective acdS genes of different strains of Mesorhizobium has also studied.
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      PubDate: 2017-02-13T16:04:54Z
       
  • An in-silico approach to find a peptidomimetic targeting extracellular
           domain of HER3 from a HER3 Nanobody
    • Abstract: Publication date: Available online 10 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Z. Pourhashem, M. Mehrpouya, N. Yardehnavi, A. Eslamparast, F. Kazemi-Lomedasht
      HER3 is an important therapeutic target in cancer treatments. HER3 Nanobodies (Nbs) are a novel class of antibodies with several competitive advantages over conventional antibodies. A peptidomimetic derived from these Nbs can be considered to be a small peptide mimicking some of the molecular recognition interactions of a natural peptide or protein in a three-dimensional (3D) space, with a receptor that has improved properties. In this study, we introduce a new approach to design a peptidomimetic derived from HER3 Nb through an in silico analysis. We propose that the complementarity determining region (CDR3) of HER3 Nb is large enough to effectively interact with HER3 antigen as well as with the entire Nb. A computational analysis has been performed using Nb models retrieved from SWISS-pdb Viewer 4.1.0 (spdbv) as a target spot and HER3 extracellular domain as its antigenic target to identify the interactions between them by the protein-protein docking method. Detailed analysis of selected models with docked complex help us to identify the interacting amino acid residues between the two molecules. The results of in silico analysis show that the CDR3 of HER3 Nb might be used by itself as a peptidomimetic drug instead of the full Nb. HER3 peptidomimetic-derived HER3 Nb may reduce Nb production costs and be used as a substitute for HER3 Nb after further experimental work. The paper demonstrates the feasibility of peptidomimetics designs using bioinformatic tools.
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      PubDate: 2017-02-13T16:04:54Z
       
  • Computer Evaluation of VirE2 Protein Complexes for ssDNA Transfer Ability
    • Abstract: Publication date: Available online 9 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Irina Volokhina, Yury Gusev, Svyatoslav Mazilov, Yelizaveta Moiseeva, Mikhail Chumakov
      The single-stranded transfer DNA from the Ti plasmid of the soil bacteria Agrobacterium nonspecifically integrates into the plant chromosome and is inherited at subsequent cell divisions. How it is transferred across host membranes is unknown, but it is believed that VirE2 proteins form a membrane-spanning pore or channel in a lipid bilayer and possibly mediate the delivery of the single-stranded transfer DNA–VirD2–VirE2 complex to the plant cell chromosomes. The aim of this work was to perform a computer simulation of VirE2’s pore-forming capacity and an evaluation of constructed VirE2 complexes. The oscillating motions of complexes consisting of two and four VirE2 subunits were estimated by the molecular dynamics and normal modes methods. We did not predict any large changes in domain orientation for two and four-subunit VirE2 complexes within simulation times of 1ns. A possible gating mechanism similar to that seen in the ion channels of the complex formed from two VirE2 proteins was proposed, whereas no conformational changes were predicted inside the pore in the complex formed from four VirE2 proteins.
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      PubDate: 2017-02-13T16:04:54Z
       
  • 2,4-Ditellurouracil and its 5-fluoro derivative: Theoretical
           investigations of structural, energetics and ADME parameters
    • Abstract: Publication date: Available online 9 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Ibrahim A. Alswaidan, Kritish Sooknah, Lydia Rhyman, Cemal Parlak, Derek T. Ndinteh, Mohamed I. Elzagheid, Ponnadurai Ramasami
      2,4-Ditellurouracil exhibits keto-enol tautomerism via different pathways resulting in seven tautomers. These pathways were studied in the gas phase using density functional theory method. The functionals used were BLYP, B3LYP and BHLYP and the basis sets were 6–311++G(d,p) for all atoms except that LanL2DZ ECP was used for tellurium atom only. The results indicate that the diketo form is more stable as observed for uracil and its sulfur and selenium analogues. The effect of introducing fluorine at position 5 was also investigated and the energy difference between the diketo and dienol forms is reduced. 2,4-Ditellurouracil and its 5-fluoro analogue are expected to exist exclusively as the diketo form due to the high interconversion energy barrier. We extended the investigation to predict ADME parameters of the most stable diketo and dienol tautomers in view of understanding their biological properties. This research enlightens keto-enol tautomerism of 2,4-ditellurouracil and its 5-fluoro derivative with additional insights to biological functions.
      Graphical abstract image

      PubDate: 2017-02-13T16:04:54Z
       
  • Simultaneous estimation of detection sensitivity and absolute copy number
           from digital PCR serial dilution
    • Abstract: Publication date: Available online 1 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Xutao Deng, Brian S. Custer, Michael P. Busch, Sonia Bakkour, Tzong-Hae Lee
      Digital polymerase chain reaction (dPCR) is a refinement of the conventional PCR approach to nucleic acid detection and absolute quantification. Digital PCR works by partitioning a sample of DNA or cDNA into many individual, parallel PCR reactions. Current quantification methods rely on the assumption that the PCR reactions are always able to detect single target molecules. When the assumption does not hold, the copy numbers will be severely underestimated. We developed a novel dPCR quantification method which determines whether the single copy assumption is violated or not by simultaneously estimating the assay sensitivity and the copy numbers using serial dilution data sets. The implemented method is available as an R package “digitalPCR”.

      PubDate: 2017-02-06T15:49:06Z
       
  • A new search subspace to compensate failure of cavity-based localization
           of ligand-binding sites
    • Abstract: Publication date: Available online 31 January 2017
      Source:Computational Biology and Chemistry
      Author(s): Kalpana Singh, Tapobrata Lahiri
      The common exercise adopted in almost all the ligand-binding sites (LBS) predictive methods is to considerably reduce the search space up to a meager fraction of the whole protein. In this exercise it is assumed that the LBS are mostly localized within a search subspace, cavities, which topologically appear to be valleys within a protein surface. Therefore, extraction of cavities is considered as a most important preprocessing step for finally predicting LBS. However, prediction of LBS based on cavity search subspace is found to fail for some proteins. To solve this problem a new search subspace was introduced which was found successful to localize LBS in most of the proteins used in this work for which cavity-based method MetaPocket 2.0 failed. Therefore this work appeared to augment well the existing binding site predictive methods through its applicability for complementary set of proteins for which cavity-based methods might fail. Also, to decide on the proteins for which instead of cavity-subspace the new subspace should be explored, a decision framework based on simple heuristic is made which uses geometric parameters of cavities extracted through MetaPocket 2.0. It is found that option for selecting the new or cavity-search subspace can be predicted correctly for nearly 87.5% of test proteins.
      Graphical abstract image

      PubDate: 2017-02-06T15:49:06Z
       
  • Disruption of redox catalytic functions of peroxiredoxin-thioredoxin
           complex in Mycobacterium tuberculosis H37Rv using small interface binding
           molecules
    • Abstract: Publication date: April 2017
      Source:Computational Biology and Chemistry, Volume 67
      Author(s): Arun Bahadur Gurung, Amit Kumar Das, Atanu Bhattacharjee
      Mycobacterium tuberculosis has distinctive ability to detoxify various microbicidal superoxides and hydroperoxides via a redox catalytic cycle involving thiol reductants of peroxiredoxin (Prx) and thioredoxin (Trx) systems which has conferred on it resistance against oxidative killing and survivability within host. We have used computational approach to disrupt catalytic functions of Prx-Trx complex which can possibly render the pathogen vulnerable to oxidative killing in the host. Using protein–protein docking method, we have successfully constructed the Prx-Trx complex. Statistics of interface region revealed contact area of each monomer less than 1500Å2 and enriched in polar amino acids indicating transient interaction between Prx and Trx. We have identified ZINC40139449 as a potent interface binding molecule through virtual screening of drug-like compounds from ZINC database. Molecular dynamics (MD) simulation studies showed differences in structural properties of Prx-Trx complex both in apo and ligand bound states with regard to root mean square deviation (RMSD), radius of gyration (Rg), root mean square fluctuations (RMSF), solvent accessible surface area (SASA) and number of hydrogen bonds (NHBs). Interestingly, we found stability of two conserved catalytic residues Cys61 and Cys174 of Prx and conserved catalytic motif, WCXXC of Trx upon binding of ZINC40139449. The time dependent displacement study reveals that the compound is quite stable in the interface binding region till 30ns of MD simulation. The structural properties were further validated by principal component analysis (PCA). We report ZINC40139449 as promising lead which can be further evaluated by in vitro or in vivo enzyme inhibition assays.
      Graphical abstract image

      PubDate: 2017-01-10T14:49:24Z
       
  • The in silico identification of small molecules for protein-protein
           interaction inhibition in AKAP-Lbc–RhoA signaling complex
    • Abstract: Publication date: April 2017
      Source:Computational Biology and Chemistry, Volume 67
      Author(s): Asifullah Khan, Mehwish Munir, Sara Aiman, Abdul Wadood, Arif-ullah Khan
      The rational design of small molecules that mimic key residues at the interface of interacting proteins can be a successful approach to target certain biological signaling cascades causing pathophysiological outcome. The A-Kinase Anchoring Protein, i.e. AKAP-Lbc, catalyses nucleotide exchange on RhoA and is involved in cardiac repolarization. The oncogenic AKAP-Lbc induces the RhoA GTPase hyperactivity and aberrantly amplifies the signaling pathway leading to hypertrophic cardiomyocytes. We took advantage of the AKAP-Lbc–RhoA complex crystal structure to design in silico small molecules predicted to inhibit the associated pathological signaling cascade. We adopted the strategies of pharmacophore building, virtual screening and molecular docking to identify the small molecules capable to target AKAP-Lbc and RhoA interactions. The pharmacophore model based virtual screening unveils two lead compounds from the TIMBAL database of small molecules modulating the targeted protein-protein interactions. The molecular docking analysis revealed the lead compounds’ potentialities to establish the essential chemical interactions with the key interactive residues of the complex. These features provided a road map for designing additional potent chemical derivatives and fragments of the original lead compounds to perturb the AKAP-Lbc and RhoA interactions. Experimental validations may elucidate the therapeutic potential of these lead chemical scaffolds to deal with aberrant AKAP-Lbc signaling based cardiac hypertrophy.
      Graphical abstract image

      PubDate: 2017-01-10T14:49:24Z
       
  • A L1-regularized feature selection method for local dimension reduction on
           microarray data
    • Abstract: Publication date: April 2017
      Source:Computational Biology and Chemistry, Volume 67
      Author(s): Shun Guo, Donghui Guo, Lifei Chen, Qingshan Jiang
      Dimension reduction is a crucial technique in machine learning and data mining, which is widely used in areas of medicine, bioinformatics and genetics. In this paper, we propose a two-stage local dimension reduction approach for classification on microarray data. In first stage, a new L1-regularized feature selection method is defined to remove irrelevant and redundant features and to select the important features (biomarkers). In the next stage, PLS-based feature extraction is implemented on the selected features to extract synthesis features that best reflect discriminating characteristics for classification. The suitability of the proposal is demonstrated in an empirical study done with ten widely used microarray datasets, and the results show its effectiveness and competitiveness compared with four state-of-the-art methods. The experimental results on St Jude dataset shows that our method can be effectively applied to microarray data analysis for subtype prediction and the discovery of gene coexpression.
      Graphical abstract image

      PubDate: 2017-01-10T14:49:24Z
       
  • Comparative analysis of amino acid composition in the active site of nirk
           gene encoding copper-containing nitrite reductase (CuNiR) in bacterial
           spp.
    • Abstract: Publication date: April 2017
      Source:Computational Biology and Chemistry, Volume 67
      Author(s): Utpal Kumar Adhikari, M. Mizanur Rahman
      The nirk gene encoding the copper-containing nitrite reductase (CuNiR), a key catalytic enzyme in the environmental denitrification process that helps to produce nitric oxide from nitrite. The molecular mechanism of denitrification process is definitely complex and in this case a theoretical investigation has been conducted to know the sequence information and amino acid composition of the active site of CuNiR enzyme using various Bioinformatics tools. 10 Fasta formatted sequences were retrieved from the NCBI database and the domain and disordered regions identification and phylogenetic analyses were done on these sequences. The comparative modeling of protein was performed through Modeller 9v14 program and visualized by PyMOL tools. Validated protein models were deposited in the Protein Model Database (PMDB) (PMDB id: PM0080150 to PM0080159). Active sites of nirk encoding CuNiR enzyme were identified by Castp server. The PROCHECK showed significant scores for four protein models in the most favored regions of the Ramachandran plot. Active sites and cavities prediction exhibited that the amino acid, namely Glycine, Alanine, Histidine, Aspartic acid, Glutamic acid, Threonine, and Glutamine were common in four predicted protein models. The present in silico study anticipates that active site analyses result will pave the way for further research on the complex denitrification mechanism of the selected species in the experimental laboratory.
      Graphical abstract image

      PubDate: 2017-01-10T14:49:24Z
       
  • A DFT study on the complex formation between desferrithiocin and metal
           ions (Mg2+, Al3+, Ca2+, Mn2+, Fe3+, Co2+, Ni2+, Cu2+, Zn2+)
    • Abstract: Publication date: April 2017
      Source:Computational Biology and Chemistry, Volume 67
      Author(s): Sadegh Kaviani, Mohammad Izadyar, Mohammad Reza Housaindokht
      In recent years, Metal-chelating compounds, namely siderphores have been considered very much because of their crucial role in various fields of the environmental researches. Their importance lies in the fact that they are able to be bonded to a variety of metals in addition to iron. A theoretical study on the structures of desferrithiocin siderphore coordinated to Mg2+, Al3+, Ca2+, Mn2+, Fe3+, Co2+, Ni2+, Cu2+ and Zn2+ metal ions was carried out, using the CAM-B3LYP/6-31G(d) level of the theory in the water. In order to understand the factors which control the stability, reactivity and the strength of toxic metals excretion as well as microbial uptake of the metal-siderphore complexes, we examined the stability and binding energies of the desferrithiocin and various metal ions with different spin states. The binding affinity of desferrithiocin to Fe3+ (log β2 =23.88) showed that the desferrithiocin can scavenge the excess iron(III) from the labile sources. Also, the binding energy values were well described by addition of the dispersion-corrected D3 functional. Because of the importance of the charge transfer in the complex formation, donor-acceptor interaction energies were evaluated. Based on this analysis, an increase in the effective nuclear charge increases E(2) values. Vibrational analysis showed that the critical bonds (CO stretching and CH bending) are in the range of 1300–1800cm−1. Finally, some probable correlations between the complexation behavior and quantum chemistry descriptors have been analyzed.
      Graphical abstract image

      PubDate: 2017-01-10T14:49:24Z
       
  • Comparative genome-wide phylogenetic and expression analysis of SBP genes
           from potato (Solanum tuberosum)
    • Abstract: Publication date: April 2017
      Source:Computational Biology and Chemistry, Volume 67
      Author(s): Musa Kavas, Aslıhan Kurt Kızıldoğan, Büşra Abanoz
      Flowering time is a very important phase in transition to reproductive stage of life in higher plants. SQUAMOSA promoter-binding protein (SBP) gene family encodes plant-specific transcription factors that are involved in regulation of several developmental processes, especially flowering. Although SBP-box genes have been identified in different plants, there have been no study indicating the regulatory effect of SBP box in potato flowering. Here, we report for the first time the identification and characterization of SBP-box transcription factors as well as determination of expression level of SBP-box genes in Solanum tuberosum L. an important crop worldwide. Fifteen different SBP-box transcription factor genes were identified in potato genome. They were found to be distributed in nine chromosomes and eight of them included miR156 and miRNA157 target sites. Characterization of amino acid sequences were performed and protein interactions were predicted. In addition, expression levels of five S. tuberosum SBP-box genes were analysed by both in silico and qRT-PCR. All these results provide a better understanding of functional role of SBP-box gene family members in flowering time in potato.
      Graphical abstract image

      PubDate: 2017-01-10T14:49:24Z
       
  • Bipartite network analysis reveals metabolic gene expression profiles that
           are highly associated with the clinical outcomes of acute myeloid leukemia
           
    • Abstract: Publication date: Available online 6 January 2017
      Source:Computational Biology and Chemistry
      Author(s): Fanfan Xie, Mingxiong He, Li He, Keqin Liu, Menglong Li, Guoquan Hu, Zhining Wen
      Dysregulated and reprogrammed metabolism is one of the most important characteristics of cancer, and exploiting cancer cell metabolism can aid in understanding the diverse clinical outcomes for patients. To investigate the differences in metabolic pathways among patients with acute myeloid leukemia (AML) and differential survival outcomes, we systematically conducted microarray data analysis of the metabolic gene expression profiles from 384 patients available from the Gene Expression Omnibus and Cancer Genome Atlas databases. Pathway enrichment analysis of differentially expressed genes (DEGs) showed that the metabolic differences between low-risk and high-risk patients mainly existed in two pathways: biosynthesis of unsaturated fatty acids and oxidative phosphorylation. Using the gene-pathway bipartite network, 62 metabolic genes were identified from 272 DEGs involved in 88 metabolic pathways. Based on the expression patterns of the 62 genes, patients with shorter overall survival (OS) durations in the training set (hazard ratio (HR)=1.58, p =0.038) and in two test sets (HR=1.69 and 1.56 and p =0.089 and 0.029, respectively) were well discriminated by hierarchical clustering analysis. Notably, the expression profiles of ALAS2, BCAT1, BLVRB, and HK3 showed distinct differences between the low-risk and high-risk patients. In addition, models for predicting the OS outcome of AML from the 62 gene signatures achieved improved performance compared with previous studies. In conclusion, our findings reveal significant differences in metabolic processes of patients with AML with diverse survival durations and provide valuable information for clinical translation.
      Graphical abstract image

      PubDate: 2017-01-10T14:49:24Z
       
  • Effect of the R119G Mutation on Human P5CR Structure and Its Interactions
           with NAD: Insights Derived from Molecular Dynamics Simulation and Free
           Energy Analysis
    • Abstract: Publication date: Available online 5 January 2017
      Source:Computational Biology and Chemistry
      Author(s): Peng Sang, Yue-Hui Xie, Lin-Hua Li, Yu-Jia Ye, Wei Hu, Jing Wang, Wen Wan, Rui Li, Long-Jun Li, Lin-Ling Ma, Zhi Li, Shu-Qun Liu, Zhao-Hui Meng
      Pyrroline-5-carboxylate reductase (P5CR), an enzyme with conserved housekeeping roles, is involved in the etiology of cutis laxa. While previous work has shown that the R119G point mutation in the P5CR protein is involved, the structural mechanism behind the pathology remains to be elucidated. In order to probe the role of the R119G mutation in cutis laxa, we performed molecular dynamics (MD) simulations, essential dynamics (ED) analysis, and Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations on wild type (WT) and mutant P5CR-NAD complex. These MD simulations and ED analyses suggest that the R119G mutation decreases the flexibility of P5CR, specifically in the substrate binding pocket, which could decrease the kinetics of the cofactor entrance and egress. Furthermore, the MM-PBSA calculations suggest the R119G mutant has a lower cofactor binding affinity for NAD than WT. Our study provides insight into the possible role of the R119G mutation during interactions between P5CR and NAD, thus bettering our understanding of how the mutation promotes cutis laxa.
      Graphical abstract image

      PubDate: 2017-01-10T14:49:24Z
       
  • An efficient perturbation method to predict the functionally key sites of
           glutamine binding protein
    • Abstract: Publication date: Available online 29 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Dashuai Lv, Cunxin Wang, Chunhua Li, Jianjun Tan, Xiaoyi Zhang
      Glutamine-Binding Protein (GlnBP) of Escherichia coli, an important member of the periplasmic binding protein family, is responsible for the first step in the active transport of glutamine across the cytoplasmic membrane. In this work, the functionally key regulation sites of GlnBP were identified by utilizing a perturbation method proposed by our group, in which the residues whose perturbations markedly change the binding free energy between GlnBP and glutamine are considered to be functionally key residues. The results show that besides the substrate binding sites, some other residues distant from the binding pocket, including the ones in the hinge regions between the two domains, the front- and back- door channels and the exposed region, are important for the function of glutamine binding and transport. The predicted results are well consistent with the theoretical and experimental data, which indicates that our method is an effective approach to identify the key residues important for both ligand binding and long-range allosteric signal transmission. This work can provide some insights into the function performance of GlnBP and the physical mechanism of its allosteric regulation.
      Graphical abstract image

      PubDate: 2016-12-30T14:40:09Z
       
  • In silico approach to identify non-synonymous SNPs in human obesity
           related gene, MC3R (melanocortin-3-receptor)
    • Abstract: Publication date: Available online 29 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Rajan Kumar Singh, Kulandaivelu Mahalingam
      The melanocortin-3-receptor (MC3R) is a novel gene candidate for human obesity, which involved in controlling the energy homeostasis and food intake behavior. The main aim behind this work is to investigate the potentially deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in obesity related gene MC3R by using six computational tools viz., PolyPhen, I-Mutant, PROVEAN, SIFT, PANTHER and PhD-SNP. In our study, we predicted eight nsSNPs i.e., rs74315393 (Ile146Asn), rs368205448 (Asp121Tyr), rs143321797 (Phe45Ser), rs17847261 (Cys274Ser), rs144166442 (Pro257His), rs370533946 (Leu224Pro), rs371354428 (Pro72Leu) and rs373708098 (Gly249Ser) found to be potentially deleterious. The functional impact of three nsSNPs i.e., rs74315393, rs368205448 and rs143321797 have already been validated experimentally in the context of human obesity. Moreover, Homology modeling and structural analysis were carried out for already experimentally validated nsSNPs i.e., rs74315393, rs368205448 and rs143321797 to check the stability of predicted models. The mutant models showed higher energy and RMSD (Root mean square deviation) values. In addition, FTSite server predicted one nsSNP i.e., rs368205448 (Asp121Tyr) out of eight identified nsSNPs found in the MC3R protein binding site. Thus, the present computational study may suggest that predicted nsSNPs possibly be a better drug target and contribute to the treatment and better understanding of human obesity.
      Graphical abstract image

      PubDate: 2016-12-30T14:40:09Z
       
  • Comparative QSAR studies using HQSAR, CoMFA, and CoMSIA methods on cyclic
           sulfone hydroxyethylamines as BACE1 inhibitors
    • Abstract: Publication date: Available online 23 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Shuqun Zhang, Zichun Lin, Yinglan Pu, Yunqin Zhang, Li Zhang, Zhili Zuo
      The inhibition of β-secretase (BACE1) is currently the main pharmacological strategy available for Alzheimer’s disease (AD). 2D QSAR and 3D QSAR analysis on some cyclic sulfone hydroxyethylamines inhibitors against β-secretase (IC50: 0.002 to 2.75μM) were carried out using hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods. The best model based on the training set was generated with a HQSAR q2 value of 0.693 and r2 value of 0.981; a CoMFA q2 value of 0.534 and r2 value of 0.913; and a CoMSIA q2 value of 0.512 and r2 value of 0.973. In order to gain further understand of the vital interactions between cyclic sulfone hydroxyethylamines and the protease, the analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the BACE1. The final QSAR models could be helpful in the design and development of novel active BACE1 inhibitors.
      Graphical abstract image

      PubDate: 2016-12-30T14:40:09Z
       
  • A comparative QSAR analysis and molecular docking studies of phenyl
           piperidine derivatives as potent dual NK1R antagonists/serotonin
           transporter (SERT) inhibitors
    • Abstract: Publication date: Available online 23 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Somayeh Zare, Masood Fereidoonnezhad, Davoud Afshar, Zahra Ramezani
      Depression is a critical mood disorder that affects millions of patients. Available therapeutic antidepressant agents are associated with several undesirable side effects. Recently, it has been shown that Neurokinin 1 receptor (NK1R) antagonists can potentiate the antidepressant effects of serotonin-selective reuptake inhibitors (SSRIs). In this study, a series of phenyl piperidine derivatives as potent dual NK1R antagonists/serotonin transporter (SERT) inhibitors were applied to quantitative structure–activity relationship (QSAR) analysis. A collection of chemometrics methods such as multiple linear regression (MLR), factor analysis–based multiple linear regression (FA-MLR), principal component regression (PCR), and partial least squared combined with genetic algorithm for variable selection (GA-PLS) were applied to make relations between structural characteristics and NK1R antagonism/SERT inhibitory of these compounds. The best multiple linear regression equation was obtained from GA-PLS and MLR for NK1R and SERT, respectively. Based on the resulted model, an in silico-screening study was also conducted and new potent lead compounds based on new structural patterns were designed for both targets. Molecular docking studies of these compounds on both targets were also conducted and encouraging results were acquired. There was a good correlation between QSAR and docking results. The results obtained from validated docking studies indicate that the important amino acids inside the active site of the cavity that are responsible for essential interactions are Glu33, Asp395 and Arg26 for SERT and Ala30, Lys7, Asp31, Phe5 and Tyr82 for NK1R receptors.
      Graphical abstract image

      PubDate: 2016-12-30T14:40:09Z
       
  • Role of computational efficiency indices and pose clustering in effective
           decision making: an example of annulated furanones in Pf-DHFR space
    • Abstract: Publication date: Available online 23 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Manoj Kumar, Tanpreet Kaur, Anuj Sharma
      In the present report, the role of computationally estimated efficiency indices and pose clustering has been demonstrated in effective decision making, resource management and chemical prioritization. As an example, 720 annulated furanones from six different scaffold classes were computationally docked against Pf-DHFR active site using AutoDock 4.2. Many trends were established by navigating efficiency indices (BEI and SEI) in 2D planes. These trends were then explained by comparing interaction profiles of docked poses with that of known actives/inhibitors. Cases where trends emerged from efficiency plotsresonated well with the pattern of a particular cluster diagram were considered as guidelines for optimization purpose. These kind of guidelines could help medicinal chemists in prioritization their work and in effective management of time, energy and chemical resources.
      Graphical abstract image

      PubDate: 2016-12-30T14:40:09Z
       
  • Structure and expression of dna methyltransferase genes from apomictic and
           sexual Boechera species
    • Abstract: Publication date: Available online 20 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Kemal Melik Taşkin, Aslıhan Özbilen, Fatih Sezer, Kaan Hürkan, Şebnem Güneş
      In this study, we determined the structure of DNA methyltransferase (DNMT) genes in apomict and sexual Boechera species and investigated the expression levels during seed development. Protein and DNA sequences of diploid sexual Boechera stricta DNMT genes obtained from Phytozome 10.3 were used to identify the homologues in apomicts, Boechera holboellii and Boechera divaricarpa. Geneious R8 software was used to map the short-paired reads library of B. holboellii whole genome or B. divaricarpa transcriptome reads to the reference gene sequences. We determined three DNMT genes; for Boechera spp. METHYLTRANSFERASE1 (MET1), CHROMOMETHYLASE 3 (CMT3) and DOMAINS REARRANGED METHYLTRANSFERASE 1/2 (DRM2). We examined the structure of these genes with bioinformatic tools and compared with other DNMT genes in plants. We also examined the levels of expression in silique tissues after fertilization by semi-quantitative PCR. The structure of DNMT proteins in apomict and sexual Boechera species share common features. However, the expression levels of DNMT genes were different in apomict and sexual Boechera species. We found that DRM2 was upregulated in apomictic Boechera species after fertilization. Phylogenetic trees showed that three genes are conserved among green algae, monocotyledons and dicotyledons. Our results indicated a deregulation of DNA methylation machinery during seed development in apomicts.
      Graphical abstract image

      PubDate: 2016-12-23T14:35:32Z
       
  • In silico Study of Porphyrin-Anthraquinone Hybrids as CDK2 Inhibitor
    • Abstract: Publication date: Available online 19 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Muhammad Arba, Sunandar Iksan, La Ode Ahmad Nur Ramadhan, Daryono Hadi Tjahjono
      Cyclin-Dependent Kinases (CDKs) are known to play crucial roles in controlling cell cycle progression of eukaryotic cell and inhibition of their activity has long been considered as potential strategy in anti-cancer drug research. In the present work, a series of porphyrin-anthraquinone hybrids bearing meso-substituents, i.e. either pyridine or pyrazole rings were designed and computationally evaluated for their Cyclin Dependent Kinase-2 (CDK2) inhibitory activity using molecular docking, molecular dynamics simulation, and binding free energy calculation. The molecular docking simulation revealed that all six porphyrin hybrids were able to bind to ATP-binding site of CDK2 and interacted with key residues constituted the active cavity of CDK2, while molecular dynamics simulation indicated that all porphyrins bound to CDK2 were stable for 6ns. The binding free energies predicted by MM-PBSA method showed that most compounds exhibited higher affinity than that of native ligand (4-anilinoquinazoline, DTQ) and the affinity of mono-H2PyP-AQ was about three times better than that of DTQ, indicating its potential to be advanced as a new CDK2 inhibitor.
      Graphical abstract image

      PubDate: 2016-12-23T14:35:32Z
       
  • Exploring the resistance mechanism of imipenem in carbapenem hydrolysing
           class D beta-lactamases OXA-143 and its variant OXA-231 (D224A) expressing
           Acinetobacter baumannii: An in-silico approach
    • Abstract: Publication date: Available online 7 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Kullappan Malathi, Anand Anbarasu, Sudha Ramaiah
      Acinetobacter baumannii (A. baumannii), is a Gram negative, coccobacilli and is associated with nosocomial infections. The bacterium has developed resistance to all known classes of antibiotics. Multi-drug resistant A. baumannii infections have been treated with the carbapenem group of antibiotics like imipenem and meropenem. Recent reports indicate that A. baumannii has acquired resistance to imipenem due to the secretion of carbapenem hydrolysing class D beta-lactamases (CHDLs). Such CHDLs found in carbapenem resistant A. baumannii belongs to OXA-143 and its variant OXA-231, which has Alanine (A) in place of Aspartic acid (D) at sequence position 224. The mutation of the OXA-231 CHDL alters the catalytic activity of the enzyme. Hence, the present study was carried out to find the probable mechanism of imipenem resistance in OXA-143 and OXA-231 (D224A) CHDLs expressing A. baumannii by employing molecular docking and dynamics. Methods Our study reveals that OXA-143 CHDL-imipenem complex has more binding affinity than OXA-231 (D224A) CHDL-imipenem complex. Our results indicate that there is a strong binding affinity of OXA-143 with imipenem when compared with OXA-243 and this mechanism might be the probable reason for imipenem resistance in OXA-143 expressing A. baumannii strains.
      Graphical abstract image

      PubDate: 2016-12-09T13:21:14Z
       
 
 
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