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CHEMICAL ENGINEERING (189 journals)                     

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AATCC Journal of Research     Full-text available via subscription   (Followers: 4)
ACS Sustainable Chemistry & Engineering     Hybrid Journal   (Followers: 1)
Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials     Hybrid Journal   (Followers: 4)
Acta Polymerica     Hybrid Journal   (Followers: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 20)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 6)
Advanced Chemical Engineering Research     Open Access   (Followers: 28)
Advanced Powder Technology     Hybrid Journal   (Followers: 15)
Advances in Applied Ceramics     Hybrid Journal   (Followers: 4)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 23)
Advances in Chemical Engineering and Science     Open Access   (Followers: 52)
Advances in Polymer Technology     Hybrid Journal   (Followers: 12)
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: 10)
Anti-Corrosion Methods and Materials     Hybrid Journal   (Followers: 6)
Applied Petrochemical Research     Open Access   (Followers: 2)
Asia-Pacific Journal of Chemical Engineering     Hybrid Journal   (Followers: 7)
Biochemical Engineering Journal     Hybrid Journal   (Followers: 14)
Biofuel Research Journal     Open Access   (Followers: 3)
Biomass Conversion and Biorefinery     Partially Free   (Followers: 11)
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: 3)
Carbohydrate Polymers     Hybrid Journal   (Followers: 7)
Catalysts     Open Access   (Followers: 6)
ChemBioEng Reviews     Full-text available via subscription  
Chemical and Engineering News     Free   (Followers: 10)
Chemical and Materials Engineering     Open Access   (Followers: 8)
Chemical and Petroleum Engineering     Hybrid Journal   (Followers: 10)
Chemical and Process Engineering     Open Access   (Followers: 22)
Chemical and Process Engineering Research     Open Access   (Followers: 19)
Chemical Engineering & Technology     Hybrid Journal   (Followers: 32)
Chemical Engineering and Processing: Process Intensification     Hybrid Journal   (Followers: 17)
Chemical Engineering and Science     Open Access   (Followers: 14)
Chemical Engineering Communications     Hybrid Journal   (Followers: 12)
Chemical Engineering Journal     Hybrid Journal   (Followers: 30)
Chemical Engineering Research and Design     Hybrid Journal   (Followers: 21)
Chemical Engineering Research Bulletin     Open Access   (Followers: 9)
Chemical Engineering Science     Hybrid Journal   (Followers: 21)
Chemical Geology     Hybrid Journal   (Followers: 15)
Chemical Papers     Hybrid Journal   (Followers: 2)
Chemical Product and Process Modeling     Hybrid Journal   (Followers: 3)
Chemical Reviews     Full-text available via subscription   (Followers: 141)
Chemical Society Reviews     Full-text available via subscription   (Followers: 38)
Chemical Technology     Open Access   (Followers: 12)
ChemInform     Hybrid Journal   (Followers: 7)
Chemistry & Industry     Hybrid Journal   (Followers: 4)
Chemistry Central Journal     Open Access   (Followers: 4)
Chemistry of Materials     Full-text available via subscription   (Followers: 159)
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: 10)
Computer Aided Chemical Engineering     Full-text available via subscription   (Followers: 1)
Computers & Chemical Engineering     Hybrid Journal   (Followers: 10)
CORROSION     Full-text available via subscription   (Followers: 18)
Corrosion Engineering, Science and Technology     Hybrid Journal   (Followers: 33)
Corrosion Reviews     Hybrid Journal   (Followers: 3)
Crystal Research and Technology     Hybrid Journal   (Followers: 5)
Current Opinion in Chemical Engineering     Open Access   (Followers: 7)
Education for Chemical Engineers     Hybrid Journal   (Followers: 4)
Eksergi     Open Access  
Emerging Trends in Chemical Engineering     Full-text available via subscription   (Followers: 1)
European Polymer Journal     Hybrid Journal   (Followers: 41)
Fibers and Polymers     Full-text available via subscription   (Followers: 4)
Fluorescent Materials     Open Access   (Followers: 1)
Focusing on Modern Food Industry     Open Access   (Followers: 2)
Frontiers of Chemical Science and Engineering     Hybrid Journal   (Followers: 1)
Gels     Open Access  
Geochemistry International     Hybrid Journal   (Followers: 2)
Handbook of Powder Technology     Full-text available via subscription   (Followers: 5)
Heat Exchangers     Open Access   (Followers: 2)
High Performance Polymers     Hybrid Journal  
Hungarian Journal of Industry and Chemistry     Open Access  
Indian Chemical Engineer     Hybrid Journal   (Followers: 5)
Indian Journal of Chemical Technology (IJCT)     Open Access   (Followers: 9)
Indonesian Journal of Chemical Science     Open Access   (Followers: 1)
Industrial & Engineering Chemistry     Full-text available via subscription   (Followers: 9)
Industrial & Engineering Chemistry Research     Full-text available via subscription   (Followers: 20)
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: 2)
International Journal of Chemical Engineering     Open Access   (Followers: 6)
International Journal of Chemical Reactor Engineering     Hybrid Journal   (Followers: 2)
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: 3)
Journal of Chemical Engineering & Process Technology     Open Access   (Followers: 4)
Journal of Applied Crystallography     Hybrid Journal   (Followers: 5)
Journal of Applied Electrochemistry     Hybrid Journal   (Followers: 10)
Journal of Applied Polymer Science     Hybrid Journal   (Followers: 111)
Journal of Biomaterials Science, Polymer Edition     Hybrid Journal   (Followers: 9)
Journal of Bioprocess Engineering and Biorefinery     Full-text available via subscription  
Journal of Chemical & Engineering Data     Full-text available via subscription   (Followers: 9)
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: 15)
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: 14)
Journal of CO2 Utilization     Hybrid Journal   (Followers: 2)
Journal of Crystallization Process and Technology     Open Access   (Followers: 7)
Journal of Environmental Chemical Engineering     Hybrid Journal   (Followers: 3)
Journal of Food Measurement and Characterization     Hybrid Journal  
Journal of Food Processing & Technology     Open Access  
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  
Journal of Inorganic and Organometallic Polymers and Materials     Partially Free   (Followers: 8)
Journal of Modern Chemistry & Chemical Technology     Full-text available via subscription   (Followers: 2)
Journal of Molecular Catalysis A: Chemical     Hybrid Journal   (Followers: 5)
Journal of Non-Crystalline Solids     Hybrid Journal   (Followers: 7)
Journal of Organic Semiconductors     Open Access   (Followers: 4)
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: 8)
Journal of Polymer Research     Hybrid Journal   (Followers: 6)
Journal of Polymer Science Part C : Polymer Letters     Hybrid Journal   (Followers: 5)
Journal of Polymers     Open Access   (Followers: 2)
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: 264)
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: 8)
Jurnal Bahan Alam Terbarukan     Open Access  
Jurnal Inovasi Pendidikan Kimia     Open Access  
Jurnal Reaktor     Open Access  
Jurnal Teknologi Dan Industri Pangan     Open Access   (Followers: 1)
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: 14)
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: 2)
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: 3)
Plasma Processes and Polymers     Hybrid Journal  
Plasmas and Polymers     Hybrid Journal  
Polymer     Hybrid Journal   (Followers: 116)
Polymer Bulletin     Hybrid Journal   (Followers: 7)
Polymer Composites     Hybrid Journal   (Followers: 14)
Polyolefins Journal     Open Access  
Powder Technology     Hybrid Journal   (Followers: 14)
Recyclable Catalysis     Open Access   (Followers: 1)
Research on Chemical Intermediates     Hybrid Journal  
Reviews in Chemical Engineering     Hybrid Journal   (Followers: 5)
Revista Colombiana de Ciencias Químico-Farmacéuticas     Open Access  
Revista Cubana de Química     Open Access  
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: 58)
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: 4)
The Chemical Record     Hybrid Journal   (Followers: 1)
Theoretical Foundations of Chemical Engineering     Hybrid Journal   (Followers: 2)
Transition Metal Chemistry     Hybrid Journal   (Followers: 2)
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]   [10 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1476-9271
   Published by Elsevier Homepage  [3038 journals]
  • Predicting protein subcellular localization based on information content
           of gene ontology terms
    • Authors: Shu-Bo Zhang; Qiang-Rong Tang
      Pages: 1 - 7
      Abstract: Publication date: Available online 14 September 2016
      Source:Computational Biology and Chemistry
      Author(s): Shu-Bo Zhang, Qiang-Rong Tang
      Predicting the location where a protein resides within a cell is important in cell biology. Computational approaches to this issue have attracted more and more attentions from the community of biomedicine. Among the protein features used to predict the subcellular localization of proteins, the feature derived from Gene Ontology (GO) has been shown to be superior to others. However, most of the sights in this field are set on the presence or absence of some predefined GO terms. We proposed a method to derive information from the intrinsic structure of the GO graph. The feature vector was constructed with each element in it representing the information content of the GO term annotating to a protein investigated, and the support vector machines was used as classifier to test our extracted features. Evaluation experiments were conducted on three protein datasets and the results show that our method can enhance eukaryotic and human subcellular location prediction accuracy by up to 1.1% better than previous studies that also used GO-based features. Especially in the scenario where the cellular component annotation is absent, our method can achieved satisfied results with an overall accuracy of more than 87%.
      Graphical abstract image

      PubDate: 2016-09-19T04:30:37Z
      DOI: 10.1016/j.compbiolchem.2016.09.009
      Issue No: Vol. 65 (2016)
  • 1,3-oxazole derivatives as potential anticancer agents: computer modeling
           and experimental study
    • Authors: Ivan Semenyuta; Vasyl Kovalishyn; Vsevolod Tanchuk; Stepan Pilyo; Vladimir Zyabrev; Volodymyr Blagodatnyy; Olena Trokhimenko; Volodymyr Brovarets; Larysa Metelytsia
      Pages: 8 - 15
      Abstract: Publication date: Available online 21 September 2016
      Source:Computational Biology and Chemistry
      Author(s): Ivan Semenyuta, Vasyl Kovalishyn, Vsevolod Tanchuk, Stepan Pilyo, Vladimir Zyabrev, Volodymyr Blagodatnyy, Olena Trokhimenko, Volodymyr Brovarets, Larysa Metelytsia
      Microtubules play a significant role in cell growth and functioning. Therefore inhibition of the microtubule assemblies has emerged as one of the most promising cancer treatment strategies. Predictive QSAR models were built on a series of selective inhibitors of the tubulin were performed by using Associative Neural Networks (ANN). To overcome the problem of data overfitting due to the descriptor selection, a 5-fold cross-validation with variable selection in each step of the analysis was used. All developed QSAR models showed excellent statistics on the training (total accuracy: 0.96–0.97) and test sets (total accuracy: 0.95–97). The models were further validated by 11 synthesized 1,3-oxazole derivatives and all of them showed inhibitory effect on the Hep-2 cancer cell line. The most promising compound showed inhibitory activity IC50 =60.2 μM. In order to hypothesize their mechanism of action the top three compounds were docked in the colchicine binding site of tubulin and showed reasonable docking scores as well as favorable interactions with the protein.
      Graphical abstract image

      PubDate: 2016-09-23T04:35:10Z
      DOI: 10.1016/j.compbiolchem.2016.09.012
      Issue No: Vol. 65 (2016)
  • Investigating ego modules involved in TGFβ3-induced chondrogenesis in
           mesenchymal stem cells based on ego network
    • Authors: Jing-Guo Wu; Qing-Wei Jia; Yong Li; Fei-Fei Cao; Xi-Shan Zhang; Cong Liu
      Pages: 16 - 20
      Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65
      Author(s): Jing-Guo Wu, Qing-Wei Jia, Yong Li, Fei-Fei Cao, Xi-Shan Zhang, Cong Liu
      Objective This paper aimed to investigate ego modules for TGFβ3-induced chondrogenesis in mesenchymal stem cells (MSCs) using ego network algorithm. Methods The ego network algorithm comprised three parts, extracting differential expression network (DEN) based on gene expression data and protein-protein interaction (PPI) data; exploring ego genes by reweighting DEN; and searching ego modules by ego gene expansions. Subsequently, permutation test was carried out to evaluate the statistical significance of the ego modules. Finally, pathway enrichment analysis was conducted to investigate ego pathways enriched by the ego modules. Results A total of 15 ego genes were obtained from the DEN, such as PSMA4, HNRNPM and WDR77. Starting with each ego genes, 15 candidate modules were gained. When setting the thresholds of the area under the receiver operating characteristics curve (AUC) ≥0.9 and gene size ≥4, three ego modules (Module 3, Module 8 and Module 14) were identified, and all of them had statistical significances between normal and TGFβ3-induced chondrogenesis in MSCs. By mapping module genes to confirmed pathway database, their ego pathways were detected, Cdc20:Phospho-APC/C mediated degradation of Cyclin A for Module 3, Mitotic G1-G1/S phases for Module 8, and mRNA Splicing for Module 14. Conclusions We have successfully identified three ego modules, evaluated their statistical significances and investigated their functional enriched ego pathways. The findings might provide potential biomarkers and give great insights to reveal molecular mechanism underlying this process.
      Graphical abstract image

      PubDate: 2016-10-02T17:51:38Z
      DOI: 10.1016/j.compbiolchem.2016.09.017
      Issue No: Vol. 65 (2016)
  • Identification of potential inhibitor and enzyme-inhibitor complex on
           trypanothione reductase to control Chagas disease
    • Authors: Mohammad Uzzal Hossain; Arafat Rahman Oany; Shah Adil Ishtiyaq Ahmad; Md. Anayet Hasan; Md. Arif Khan; Md Al Ahad Siddikey
      Pages: 29 - 36
      Abstract: Publication date: Available online 7 October 2016
      Source:Computational Biology and Chemistry
      Author(s): Mohammad Uzzal Hossain, Arafat Rahman Oany, Shah Adil Ishtiyaq Ahmad, Md. Anayet Hasan, Md. Arif Khan, Md Al Ahad Siddikey
      Chagas is a parasitic disease with major threat to public health due to its resistance against commonly available drugs. Trypanothione reductase (TryR) is the key enzyme to develop this disease. Though this enzyme is well thought-out as potential drug target, the accurate structure of enzyme-inhibitor complex is required to design a potential inhibitor which is less available for TryR. In this research, we aimed to investigate the advanced drug over the available existing drugs by designing inhibitors as well as to identify a new enzyme-inhibitor complex that may act as a template for drug design. A set of analogues were designed from a known inhibitor Quinacrine Mustard (QUM) to identify the effective inhibitor against this enzyme. Further, the pharmacoinformatics elucidation and structural properties of designed inhibitor proposed effective drug candidates against Chagas disease. Molecular docking study suggests that a designed inhibitor has higher binding affinity in both crystal and modeled TryR and also poses similar interacting residues as of crystal TryR-QUM complex structure. The comparative studies based on in silico prediction proposed an enzyme-inhibitor complex which could be effective to control the disease activity. So our in silico analysis based on TryR built model, Pharmacophore and docking analysis might play an important role for the development of novel therapy for Chagas disease. But both animal model experiments and clinical trials must be done to confirm the efficacy of the therapy.

      PubDate: 2016-10-09T18:58:02Z
      DOI: 10.1016/j.compbiolchem.2016.10.002
      Issue No: Vol. 65 (2016)
    • Authors: Tariq Mahmood; Najeeb Ullah Bakht; Ejaz Aziz
      Pages: 1 - 8
      Abstract: Publication date: Available online 7 May 2016
      Source:Computational Biology and Chemistry
      Author(s): Tariq Mahmood, Najeeb Ullah Bakht, Ejaz Aziz
      Apple is the fourth most important fruit crop grown in temperate areas of the world belongs to the family Rosaceae. In the present study, the promoter (∼1000bp) region of atpB gene was used to evaluate the genetic diversity and phylogeny of six local apple varieties. atpB gene is one of the large chloroplastic region which encodes β-subunit of ATP synthase and previously it had been used largely in phylogenetic studies. During the present study, atpB promoter was amplified, sequenced and analyzed using various bioinformatics tools including Place Signal Scan, MEGA6 and BLASTn. During the phylogenetic analysis, obtained phylogram divided the studied varieties into two clusters revealing the monophyletic origin of studied apple varieties. Pairwise distance revealed moderate genetic diversity that ranges from 0.047-0.170 with an average of 0.101. While identifying different cis-acting elements present in the atpB promoter region, results exhibited the occurrence of 56 common and 20 unique cis-regulatory elements among studied varieties. The identified cis-acting regulatory elements were mapped as well. It was observed that Kala Kulu has the highest unique features with reference to the availability of cis-acting elements. Moreover, the possible functions of all regulatory elements present on the promoter sequence of atpB gene were predicted based on already reported information regarding their in vivo role.
      Graphical abstract image

      PubDate: 2016-05-08T08:43:54Z
      DOI: 10.1016/j.compbiolchem.2016.05.002
      Issue No: Vol. 64 (2016)
  • Molecular cloning, computational analysis and expression pattern of
           forkhead box l2 (Foxl2) gene in Catfish
    • Authors: Irfan Ahmad Bhat; Mohd Ashraf Rather; Jaffer Yousuf Dar; Rupam Sharma
      Pages: 9 - 18
      Abstract: Publication date: Available online 18 May 2016
      Source:Computational Biology and Chemistry
      Author(s): Irfan Ahmad Bhat, Mohd Ashraf Rather, Jaffer Yousuf Dar, Rupam Sharma
      Foxl2 belongs to forkhead/HNF-3-related family of transcription factors which is involved in ovarian differentiation and development. In present study, the Foxl2 mRNA was cloned from ovary of C. batrachus. The full length cDNA sequence of the Foxl2 was 1056bp which consists of 5' (41bp) and 3' (106bp) non-coding regions, as well as a 909bp of open reading frame (ORF) that encodes 302 amino acids. The putative protein was having the theoretical molecular weight (MW) of 34.018kD and a calculated isoelectric point (pI) of 9.38. There were 11 serine (Ser), 5 threonine (Thr), and 5 tyrosine (Tyr) phosphorylation sites and 2 putative N-glycosylation sites on the predicted protein. The ligand binding sites were predicted to be present on amino acids 42, 49, 50, 91, 92 and 95 respectively. The signal peptide analysis predicted that C. batrachus Foxl2 is a non-secretory protein. The hydropathy profile of Foxl2 protein revealed that this protein is hydrophilic in nature. Protein-protein interaction demonstrated that Foxl2 protein chiefly interacts with cytochrome P450 protein family. The mRNA transcript analysis of various tissues indicated that the C. batrachus Foxl2 mRNA was more expressed in the brain, pituitary and ovary in female while, the former two tissues and testis showed low expression in male. This study provides a basis for further structural and functional exploration of the Foxl2 from C. batrachus, including its deduced protein and its signal transduction function.
      Graphical abstract image

      PubDate: 2016-05-23T09:19:10Z
      DOI: 10.1016/j.compbiolchem.2016.05.001
      Issue No: Vol. 64 (2016)
  • Hidden Heterogeneity of Transcription Factor Binding Sites: A Case Study
           of SF-1
    • Authors: V.G. Levitsky; D.Yu. Oshchepkov; N.V. Klimova; E.V Ignatieva; G.V. Vasiliev; V.M. Merkulov; T.I. Merkulova
      Pages: 19 - 32
      Abstract: Publication date: Available online 7 May 2016
      Source:Computational Biology and Chemistry
      Author(s): V.G. Levitsky, D.Yu. Oshchepkov, N.V. Klimova, E.V Ignatieva, G.V. Vasiliev, V.M. Merkulov, T.I. Merkulova
      Steroidogenic factor 1 (SF-1) belongs to a small group of the transcription factors that bind DNA only as a monomer. Three different approaches—Sitecon, SiteGA, and oPWM—constructed using the same training sample of experimentally confirmed SF-1 binding sites have been used to recognize these sites. The appropriate prediction thresholds for recognition models have been selected. Namely, the thresholds concordant by false positive or negative rates for various methods were used to optimize the discrimination of steroidogenic gene promoters from the datasets of non-specific promoters. After experimental verification, the models were used to analyze the ChIP-seq data for SF-1. It has been shown that the sets of sites recognized by different models overlap only partially and that an integration of these models allows for identification of SF-1 sites in up to 80% of the ChIP-seq loci. The structures of the sites detected using the three recognition models in the ChIP-seq peaks falling within the [–5000, +5000] region relative to the transcription start sites (TSS) extracted from the FANTOM5 project have been analyzed. The MATLIGN classified the frequency matrices for the sites predicted by oPWM, Sitecon, and SiteGA into two groups. The first group is described by oPWM/Sitecon and the second, by SiteGA. Gene ontology (GO) analysis has been used to clarify the differences between the sets of genes carrying different variants of SF-1 binding sites. Although this analysis in general revealed a considerable overlap in GO terms for the genes carrying the binding sites predicted by oPWM, Sitecon, or SiteGA, only the last method elicited notable trend to terms related to negative regulation and apoptosis. The results suggest that the SF-1 binding sites are different in both their structure and the functional annotation of the set of target genes correspond to the predictions by oPWM+Sitecon and SiteGA. Further application of Homer software for de novo identification of enriched motifs in ChIP-Seq data for SF-1ChIP-seq dataset gave the data similar to oPWM+Sitecon.
      Graphical abstract image

      PubDate: 2016-05-08T08:43:54Z
      DOI: 10.1016/j.compbiolchem.2016.04.008
      Issue No: Vol. 64 (2016)
    • Authors: Ramesh Itteboina; Srilata Ballu; Sree Kanth Sivan; Vijjulatha Manga
      Pages: 33 - 46
      Abstract: Publication date: Available online 17 May 2016
      Source:Computational Biology and Chemistry
      Author(s): Ramesh itteboina, Srilata Ballu, Sree Kanth Sivan, Vijjulatha Manga
      Janus kinase 1 (JAK 1) plays a critical role in initiating responses to cytokines by the JAK − signal transducer and activator of transcription (JAK-STAT). This controls survival, proliferation and differentiation of a variety of cells. Docking, 3D quantitative structure activity relationship (3D-QSAR) and molecular dynamics (MD) studies were performed on a series of Imidazo-pyrrolopyridine derivatives reported as JAK 1 inhibitors. QSAR model was generated using 30 molecules in the training set; developed model showed good statistical reliability, which is evident from r2 ncv and r2 loo values. The predictive ability of this model was determined using a test set of 13 molecules that gave acceptable predictive correlation (r2 Pred) values. Finally, molecular dynamics simulation was performed to validate docking results and MM/GBSA calculations. This facilitated us to compare binding free energies of cocrystal ligand and newly designed molecule R1. The good concordance between the docking results and CoMFA/CoMSIA contour maps afforded obliging clues for the rational modification of molecules to design more potent JAK 1 inhibitors.
      Graphical abstract image

      PubDate: 2016-05-18T09:02:33Z
      DOI: 10.1016/j.compbiolchem.2016.04.009
      Issue No: Vol. 64 (2016)
  • Molecular dynamics and high throughput binding free energy calculation of
           anti-actin anticancer drugs—New insights for better design
    • Authors: Roopa. L; Pravin Kumar. R; Sudheer Mohammed M.M.
      Pages: 47 - 55
      Abstract: Publication date: Available online 24 May 2016
      Source:Computational Biology and Chemistry
      Author(s): L. Roopa, R. Pravin Kumar, L.M.M. Sudheer Mohammed
      Actin cytoskeleton plays an important role in cancerous cell progression. Till date many anticancer toxins are discovered that binds to different sites of actin. Mechanism of action of these toxins varies with respect to the site where they bind to actin. Latrunculin A (LAT) binds closely to nucleotide binding site and Reidispongiolide binds to the barbed end of actin. LAT is reported to reduce the displacement of domain 2 with respect to domain 1 and allosterically modulate nucleotide exchange. On the other hand Reidispongiolide binds with the higher affinity to actin and competes with the DNaseI binding loop once the inter-monomer interaction has been formed. Evolving better actin binders being the aim of this study we conducted a comparative molecular dynamics of these two actin-drug complexes and actin complexed with ATP alone, 50ns each. High throughput binding free energy calculations in conjugation with the high-throughput MD simulations was used to predict modifications in these two renowned anti-actin anticancer drugs for better design. Per residue energy profiling that contribute to free energy of binding shows that there is an unfavourable energy at the site where Asp157 interacts with 2-thiazolidinone moiety of LAT. Similarly, unfavourable energies are reported near macrocyclic region of Reidispongiolide specifically near carbons 7, 11 & 25 and tail region carbons 27 & 30. These predicted sites can be used for modifications and few of these are discussed in this work based on the interactions with the binding site residues. The study reveals specific interactions that are involved in the allosteric modulation of ATP by these two compounds. Glu207 closely interacting with LAT A initiates the allosteric effect on ATP binding site specifically affecting residues Asp184, Lys215 and Lys336. RGA bound actin shows high anti-correlated motions between sub domain 3 and 4. Unlike LAT A, Reidispongiolide induces a flat structure of actin which definitely should affect actin polymerisation and lead to disassembly of actin filaments.
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      PubDate: 2016-05-28T09:34:06Z
      DOI: 10.1016/j.compbiolchem.2016.05.008
      Issue No: Vol. 64 (2016)
  • Small molecule ligand docking to genotype specific bundle structures of
           hepatitis C virus (HCV) p7 protein
    • Authors: Niklas Laasch; Monoj Mon Kalita; Stephen Griffin; Wolfgang B. Fischer
      Pages: 56 - 63
      Abstract: Publication date: Available online 20 May 2016
      Source:Computational Biology and Chemistry
      Author(s): Niklas Laasch, Monoj Mon Kalita, Stephen Griffin, Wolfgang B. Fischer
      The genome of hepatitis C virus encodes for an essential 63 amino acid polytopic protein p7 of most likely two transmembrane domains (TMDs). The protein is identified to self-assemble thereby rendering lipid membranes permeable to ions. A series of small molecules such as adamantanes, imino sugars and guanidinium compounds are known to interact with p7. A set of 9 of these small molecules is docked against hexameric bundles of genotypes 5a (bundle-5a) and 1b (bundle-1b) using LeadIT. Putative sites for bundle-5a are identified within the pore and at pockets on the outside of the bundle. For bundle-1b preferred sites are found at the site of the loops. Binding energies are in favour of the guanidinium compounds. Rescoring of the identified poses with HYDE reveals a dehydration penalty for the guanidinium compounds, leaving the adamantanes and imino sugar in a better position. Binding energies calculated by HYDE and those by LeadIT indicate that all compounds are moderate binders.
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      PubDate: 2016-05-23T09:19:10Z
      DOI: 10.1016/j.compbiolchem.2016.04.010
      Issue No: Vol. 64 (2016)
  • Design, Synthesis and Computational evaluation of a novel intermediate
           salt of N-cyclohexyl-N-(cyclohexylcarbamoyl)-4-(trifluoromethyl) benzamide
           as potential Potassium channel blocker in Epileptic paroxysmal seizures
    • Authors: V. Natchimuthu; Srinivas Bandaru; Anuraj Nayarisseri; S. Ravi
      Pages: 64 - 73
      Abstract: Publication date: Available online 20 May 2016
      Source:Computational Biology and Chemistry
      Author(s): V. Natchimuthu, Srinivas Bandaru, Anuraj Nayarisseri, S. Ravi
      The narrow therapeutic range and limited pharmacokinetics of available Antiepileptic drugs (AEDs) have raised serious concerns in the proper management of epilepsy. To overcome this, the present study attempts to identify a candidate molecule targeting voltage gated potassium channels anticipated to have superior pharmacological than existing potassium channel blockers. The compound was synthesized by reacting (S)-(+)-2,3-Dihydro-1H-pyrrolo[2,1-c][1,4] benzodiazepine5,11(10H,11aH)-dione with 4-(Trifluoromethyl) benzoic acid (C8H5F3O2) in DMF and N,N'-Dicyclohexylcarbodiimide (DCC) which lead to the formation of an intermediate salt of N-cyclohexyl-N-(cyclohexylcarbamoyl)-4-(trifluoromethyl)benzamide with a perfect crystalline structure. The structure of the compound was characterized by FTIR, 1H-NMR and 13C-NMR analysis. The crystal structure is confirmed by single crystal X-ray diffraction analysis. The Structure-Activity Relationship (SAR) studies revealed that substituent of fluoro or trifluoromethyl moiety into the compound had a great effect on the biological activity in comparison to clinically used drugs. Employing computational approaches the compound was further tested for its affinity against potassium protein structure by molecular docking in addition, bioactivity and ADMET properties were predicted through computer aided programs.
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      PubDate: 2016-05-23T09:19:10Z
      DOI: 10.1016/j.compbiolchem.2016.05.003
      Issue No: Vol. 64 (2016)
  • Increasing thermal stability and catalytic activity of glutamate
           decarboxylase in E. coli: An in silico study
    • Authors: Yasaman Tavakoli; Abolghasem Esmaeili; Hossein Saber
      Pages: 74 - 81
      Abstract: Publication date: Available online 31 May 2016
      Source:Computational Biology and Chemistry
      Author(s): Yasaman Tavakoli, Abolghasem Esmaeili, Hossein Saber
      Glutamate decarboxylase (GAD) is an enzyme that converts L-glutamate to gamma amino butyric acid (GABA) that is a widely used drug to treat mental disorders like Alzheimer’s disease. In this study for the first time point mutation was performed virtually in the active site of the E. coli GAD in order to increase thermal stability and catalytic activity of the enzyme. Energy minimization and addition of water box were performed using GROMACS 5.4.6 package. PoPMuSiC 2.1 web server was used to predict potential spots for point mutation and Modeller software was used to perform point mutation on three dimensional model. Molegro virtual docker software was used for cavity detection and stimulated docking study. Results indicate that performing mutation separately at positions 164, 302, 304, 393, 396, 398 and 410 increase binding affinity to substrate. The enzyme is predicted to be more thermo- stable in all 7 mutants based on ΔΔG value.
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      PubDate: 2016-06-02T09:52:28Z
      DOI: 10.1016/j.compbiolchem.2016.05.006
      Issue No: Vol. 64 (2016)
  • Chemical Reaction Optimization for solving Shortest Common Supersequence
    • Authors: C.M. Khaled Saifullah; Md. Rafiqul Islam
      Pages: 82 - 93
      Abstract: Publication date: Available online 31 May 2016
      Source:Computational Biology and Chemistry
      Author(s): C.M. Khaled Saifullah, Md. Rafiqul Islam
      Shortest Common Supersequence (SCS) is a classical NP-hard problem, where a string to be constructed that is the supersequence of a given string set. The SCS problem has an enormous application of data compression, query optimization in the database and different bioinformatics activities. Due to NP-hardness, the exact algorithms fail to compute SCS for larger instances. Many heuristics and meta-heuristics approaches were proposed to solve this problem. In this paper, we propose a meta-heuristics approach based on Chemical Reaction Optimization, CRO_SCS that is designed inspired by the nature of the chemical reactions. For different optimization problems like 0-1 knapsack, quadratic assignment, global numeric optimization problems CRO algorithm shows very good performance. We have redesigned the reaction operators and a new reform function to solve the SCS problem. The outcomes of the proposed CRO_SCS algorithm are compared with those of the enhanced beam search (IBS_SCS), deposition and reduction (DR), ant colony optimization (ACO) and artificial bee colony (ABC) algorithms. The length of supersequence, execution time and standard deviation of all related algorithms show that CRO_SCS gives better results on the average than all other algorithms.
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      PubDate: 2016-06-02T09:52:28Z
      DOI: 10.1016/j.compbiolchem.2016.05.004
      Issue No: Vol. 64 (2016)
  • A model for the clustered distribution of SNPs in the human genome
    • Authors: Chang-Yong Lee
      Pages: 94 - 98
      Abstract: Publication date: Available online 8 June 2016
      Source:Computational Biology and Chemistry
      Author(s): Chang-Yong Lee
      Motivated by a non-random but clustered distribution of SNPs, we introduce a phenomenological model to account for the clustering properties of SNPs in the human genome. The phenomenological model is based on a preferential mutation to the closer proximity of existing SNPs. With the Hapmap SNP data, we empirically demonstrate that the preferential model is better for illustrating the clustered distribution of SNPs than the random model. Moreover, the model is applicable not only to autosomes but also to the X chromosome, although the X chromosome has different characteristics from autosomes. The analysis of the estimated parameters in the model can explain the pronounced population structure and the low genetic diversity of the X chromosome. In addition, correlation between the parameters reveals the population-wise difference of the mutation probability. These results support the mutational non-independence hypothesis against random mutation.
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      PubDate: 2016-06-13T10:05:05Z
      DOI: 10.1016/j.compbiolchem.2016.06.003
      Issue No: Vol. 64 (2016)
  • A theoretical study on the electronic structures and equilibrium constants
           evaluation of Deferasirox iron complexes
    • Authors: Samie Salehi; Amir Shokooh Saljooghi; Mohammad Izadyar
      Pages: 99 - 106
      Abstract: Publication date: Available online 1 June 2016
      Source:Computational Biology and Chemistry
      Author(s): Samie Salehi, Amir Shokooh Saljooghi, Mohammad Izadyar
      Elemental iron is essential for cellular growth and homeostasis but it is potentially toxic to the cells and tissues. Excess iron can contribute in tumor initiation and tumor growth. Obviously, in iron overload issues using an iron chelator in order to reduce iron concentration seems to be vital. This study presents the density functional theory calculations of the electronic structure and equilibrium constant for iron-deferasirox (Fe-DFX) complexes in the gas phase, water and DMSO. A comprehensive study was performed to investigate the Deferasirox-iron complexes in chelation therapy. Calculation was performed in CAMB3LYP/6-31G(d,p) to get the optimized structures for iron complexes in high and low spin states. Natural bond orbital and quantum theory of atoms in molecules analyses was carried out with B3LYP/6-311G(d,p) to understand the nature of complex bond character and electronic transition in complexes. Electrostatic potential effects on the complexes were evaluated using the CHELPG calculations. The results indicated that higher affinity for Fe (III) is not strictly a function of bond length but also the degree of Fe–X (X=O,N) covalent bonding. Based on the quantum reactivity parameters which have been investigated here, it is possible reasonable design of the new chelators to improve the chelator abilities.
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      PubDate: 2016-06-02T09:52:28Z
      DOI: 10.1016/j.compbiolchem.2016.05.010
      Issue No: Vol. 64 (2016)
  • Designing Peptide Inhibitor of Insulin Receptor to Induce Diabetes
           Mellitus Type 2 in Animal Model Mus musculus
    • Authors: Galuh W. Permatasari; Didik H. Utomo; Widodo
      Pages: 107 - 112
      Abstract: Publication date: Available online 27 May 2016
      Source:Computational Biology and Chemistry
      Author(s): Galuh W. Permatasari, Didik H. Utomo, Nashi Widodo
      A designing peptide as agent for inducing diabetes mellitus type 2 (T2DM) in an animal model is challenging. The computational approach provides a sophisticated tool to design a functional peptide that may block the insulin receptor activity. The peptide that able to inhibit the binding between insulin and insulin receptor is a warrant for inducing T2DM. Therefore, we designed a potential peptide inhibitor of insulin receptor as an agent to generate T2DM animal model by bioinformatics approach. The peptide has been developed based on the structure of insulin receptor binding site of insulin and then modified it to obtain the best properties of half life, hydrophobicity, antigenicity, and stability binding into insulin receptor. The results showed that the modified peptide has characteristics 100hours half-life, high-affinity −95.1±20, and high stability 28.17 in complex with the insulin receptor. Moreover, the modified peptide has molecular weight 4420.8g/Mol and has no antigenic regions. Based on the molecular dynamic simulation, the complex of modified peptide-insulin receptor is more stable than the commercial insulin receptor blocker. This study suggested that the modified peptide has the promising performance to block the insulin receptor activity that potentially induce diabetes mellitus type 2 in mice.
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      PubDate: 2016-05-28T09:34:06Z
      DOI: 10.1016/j.compbiolchem.2016.05.005
      Issue No: Vol. 64 (2016)
  • Structure-based Optimization of Salt-bridge Network across the Complex
           Interface of PTPN4 PDZ Domain with Its Peptide Ligands in Neuroglioma
    • Abstract: Publication date: Available online 30 November 2016
      Source:Computational Biology and Chemistry
      Author(s): Xian Xiao, Qiang-Hua He, Li-Yan Yu, Song-Qing Wang, Yang Li, Hua Yang, Ai-Hua Zhang, Xiao-Hong Ma, Yu-Jie Peng, Bing Chen
      The PTP non-receptor type 4 (PTPN4) is an important regulator protein in learning, spatial memory and cerebellar synaptic plasticity; targeting the PDZ domain of PTPN4 has become as attractive therapeutic strategy for human neuroglioma. Here, we systematically examined the complex crystal structures of PTPN4 PDZ domain with its known peptide ligands; a number of charged amino acid residues were identified in these ligands and in the peptide-binding pocket of PDZ domain, which can constitute a complicated salt-bridge network across the complex interface. Molecular dynamics (MD) simulations, binding free energy calculations and continuum model analysis revealed that the electrostatic effect plays a predominant role in domain–peptide binding, while other noncovalent interactions such as hydrogen bonds and hydrophobic forces are also responsible for the binding. The computational findings were then used to guide structure-based optimization of the interfacial salt-bridge network. Consequently, five peptides were rationally designed using the high-affinity binder Cyto8-RETEV (RETEV−COOH) as template, including four single-point mutants (i.e. Cyto8-mtxe0: RETE E −COOH, Cyto8-mtxd-1: RET D V−COOH, Cyto8-mtxd-3: R D TEV−COOH and Cyto8-mtxk-4: K ETEV−COOH) and one double-point mutant (i.e. Cyto8-mtxd-1k-4: K ET D V−COOH). Binding assays confirmed that three (Cyto8-mtxd-1, Cyto8-mtxk-4 and Cyto8-mtxd-1k-4) out of the five designed peptides exhibit moderately or considerably increased affinity as compared to the native peptide Cyto8-RETEV.
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      PubDate: 2016-12-03T21:53:35Z
  • PrAS: Prediction of amidation sites using multiple feature extraction
    • Abstract: Publication date: February 2017
      Source:Computational Biology and Chemistry, Volume 66
      Author(s): Tong Wang, Wei Zheng, Qiqige Wuyun, Zhenfeng Wu, Jishou Ruan, Gang Hu, Jianzhao Gao
      Amidation plays an important role in a variety of pathological processes and serious diseases like neural dysfunction and hypertension. However, identification of protein amidation sites through traditional experimental methods is time consuming and expensive. In this paper, we proposed a novel predictor for Prediction of Amidation Sites (PrAS), which is the first software package for academic users. The method incorporated four representative feature types, which are position-based features, physicochemical and biochemical properties features, predicted structure-based features and evolutionary information features. A novel feature selection method, positive contribution feature selection was proposed to optimize features. PrAS achieved AUC of 0.96, accuracy of 92.1%, sensitivity of 81.2%, specificity of 94.9% and MCC of 0.76 on the independent test set. PrAS is freely available at
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      PubDate: 2016-12-03T21:53:35Z
  • A novel fuzzy set based multifactor dimensionality reduction method for
           detecting gene–gene interaction
    • Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65
      Author(s): Hye-Young Jung, Sangseob Leem, Sungyoung Lee, Taesung Park
      Background Gene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. In order to identify the best interaction model associated with disease susceptibility, MDR compares all possible genotype combinations in terms of their predictability of disease status from a simple binary high(H) and low(L) risk classification. However, this simple binary classification does not reflect the uncertainty of H/L classification. Methods We regard classifying H/L as equivalent to defining the degree of membership of two risk groups H/L. By adopting the fuzzy set theory, we propose Fuzzy MDR which takes into account the uncertainty of H/L classification. Fuzzy MDR allows the possibility of partial membership of H/L through a membership function which transforms the degree of uncertainty into a [0,1] scale. The best genotype combinations can be selected which maximizes a new fuzzy set based accuracy measure. Results Two simulation studies are conducted to compare the power of the proposed Fuzzy MDR with that of MDR. Our results show that Fuzzy MDR has higher power than MDR. We illustrate the proposed Fuzzy MDR by analysing bipolar disorder (BD) trait of the WTCCC dataset to detect GGI associated with BD. Conclusions We propose a novel Fuzzy MDR method to detect gene–gene interaction by taking into account the uncertainly of H/L classification and show that it has higher power than MDR. Fuzzy MDR can be easily extended to handle continuous phenotypes as well. The program written in R for the proposed Fuzzy MDR is available at

      PubDate: 2016-11-26T20:09:57Z
  • NoisyGOA: Noisy GO annotations prediction using taxonomic and semantic
    • Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65
      Author(s): Chang Lu, Jun Wang, Zili Zhang, Pengyi Yang, Guoxian Yu
      Gene Ontology (GO) provides GO annotations (GOA) that associate gene products with GO terms that summarize their cellular, molecular and functional aspects in the context of biological pathways. GO Consortium (GOC) resorts to various quality assurances to ensure the correctness of annotations. Due to resources limitations, only a small portion of annotations are manually added/checked by GO curators, and a large portion of available annotations are computationally inferred. While computationally inferred annotations provide greater coverage of known genes, they may also introduce annotation errors (noise) that could mislead the interpretation of the gene functions and their roles in cellular and biological processes. In this paper, we investigate how to identify noisy annotations, a rarely addressed problem, and propose a novel approach called NoisyGOA. NoisyGOA first measures taxonomic similarity between ontological terms using the GO hierarchy and semantic similarity between genes. Next, it leverages the taxonomic similarity and semantic similarity to predict noisy annotations. We compare NoisyGOA with other alternative methods on identifying noisy annotations under different simulated cases of noisy annotations, and on archived GO annotations. NoisyGOA achieved higher accuracy than other alternative methods in comparison. These results demonstrated both taxonomic similarity and semantic similarity contribute to the identification of noisy annotations. Our study shows that annotation errors are predictable and removing noisy annotations improves the performance of gene function prediction. This study can prompt the community to study methods for removing inaccurate annotations, a critical step for annotating gene and pathway functions.

      PubDate: 2016-11-26T20:09:57Z
  • Differentially expressed genes selection via Laplacian regularized
           low-rank representation method
    • Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65
      Author(s): Ya-Xuan Wang, Jin-Xing Liu, Ying-Lian Gao, Chun-Hou Zheng, Jun-Liang Shang
      With the rapid development of DNA microarray technology and next-generation technology, a large number of genomic data were generated. So how to extract more differentially expressed genes from genomic data has become a matter of urgency. Because Low-Rank Representation (LRR) has the high performance in studying low-dimensional subspace structures, it has attracted a chunk of attention in recent years. However, it does not take into consideration the intrinsic geometric structures in data. In this paper, a new method named Laplacian regularized Low-Rank Representation (LLRR) has been proposed and applied on genomic data, which introduces graph regularization into LRR. By taking full advantages of the graph regularization, LLRR method can capture the intrinsic non-linear geometric information among the data. The LLRR method can decomposes the observation matrix of genomic data into a low rank matrix and a sparse matrix through solving an optimization problem. Because the significant genes can be considered as sparse signals, the differentially expressed genes are viewed as the sparse perturbation signals. Therefore, the differentially expressed genes can be selected according to the sparse matrix. Finally, we use the GO tool to analyze the selected genes and compare the P-values with other methods. The results on the simulation data and two real genomic data illustrate that this method outperforms some other methods: in differentially expressed gene selection.
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      PubDate: 2016-11-26T20:09:57Z
  • SnpFilt: A pipeline for reference-free assembly-based identification of
           SNPs in bacterial genomes
    • Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65
      Author(s): Carmen H.S. Chan, Sophie Octavia, Vitali Sintchenko, Ruiting Lan
      De novo assembly of bacterial genomes from next-generation sequencing (NGS) data allows a reference-free discovery of single nucleotide polymorphisms (SNP). However, substantial rates of errors in genomes assembled by this approach remain a major barrier for the reference-free analysis of genome variations in medically important bacteria. The aim of this report was to improve the quality of SNP identification in bacterial genomes without closely related references. We developed a bioinformatics pipeline (SnpFilt) that constructs an assembly using SPAdes and then removes unreliable regions based on the quality and coverage of re-aligned reads at neighbouring regions. The performance of the pipeline was compared against reference-based SNP calling for Illumina HiSeq, MiSeq and NextSeq reads from a range of bacterial pathogens including Salmonella, which is one of the most common causes of food-borne disease. The SnpFilt pipeline removed all false SNP in all test NGS datasets consisting of paired-end Illumina reads. We also showed that for reliable and complete SNP calls, at least 40-fold coverage is required. Analysis of bacterial isolates associated with epidemiologically confirmed outbreaks using the SnpFilt pipeline produced results consistent with previously published findings. The SnpFilt pipeline improves the quality of de-novo assembly and precision of SNP calling in bacterial genomes by removal of regions of the assembly that may potentially contain assembly errors. SnpFilt is available from
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      PubDate: 2016-11-26T20:09:57Z
  • BS-RNA: An efficient mapping and annotation tool for RNA bisulfite
           sequencing data
    • Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65
      Author(s): Fang Liang, Lili Hao, Jinyue Wang, Shuo Shi, Jingfa Xiao, Rujiao Li
      Cytosine methylation is one of the most important RNA epigenetic modifications. With the development of experimental technology, scientists attach more importance to RNA cytosine methylation and find bisulfite sequencing is an effective experimental method for RNA cytosine methylation study. However, there are only a few tools can directly deal with RNA bisulfite sequencing data efficiently. Herein, we developed a specialized tool BS-RNA, which can analyze cytosine methylation of RNA based on bisulfite sequencing data and support both paired-end and single-end sequencing reads from directional bisulfite libraries. For paired-end reads, simply removing the biased positions from the 5′ end may result in “dovetailing” reads, where one or both reads seem to extend past the start of the mate read. BS-RNA could map “dovetailing” reads successfully. The annotation result of BS-RNA is exported in BED (.bed) format, including locations, sequence context types (CG/CHG/CHH, H=A,T, or C), reference sequencing depths, cytosine sequencing depths, and methylation levels of covered cytosine sites on both Watson and Crick strands. BS-RNA is an efficient, specialized and highly automated mapping and annotation tool for RNA bisulfite sequencing data. It performs better than the existing program in terms of accuracy and efficiency. BS-RNA is developed by Perl language and the source code of this tool is freely available from the website:

      PubDate: 2016-11-26T20:09:57Z
  • Comparison among dimensionality reduction techniques based on Random
           Projection for cancer classification
    • Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65
      Author(s): Haozhe Xie, Jie Li, Qiaosheng Zhang, Yadong Wang
      Random Projection (RP) technique has been widely applied in many scenarios because it can reduce high-dimensional features into low-dimensional space within short time and meet the need of real-time analysis of massive data. There is an urgent need of dimensionality reduction with fast increase of big genomics data. However, the performance of RP is usually lower. We attempt to improve classification accuracy of RP through combining other reduction dimension methods such as Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Feature Selection (FS). We compared classification accuracy and running time of different combination methods on three microarray datasets and a simulation dataset. Experimental results show a remarkable improvement of 14.77% in classification accuracy of FS followed by RP compared to RP on BC-TCGA dataset. LDA followed by RP also helps RP to yield a more discriminative subspace with an increase of 13.65% on classification accuracy on the same dataset. FS followed by RP outperforms other combination methods in classification accuracy on most of the datasets.

      PubDate: 2016-11-26T20:09:57Z
  • Title page
    • Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65

      PubDate: 2016-11-26T20:09:57Z
  • IFC Editorial Board
    • Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65

      PubDate: 2016-11-26T20:09:57Z
  • DNA methylation-regulated microRNA pathways in ovarian serous
           cystadenocarcinoma: A meta-analysis
    • Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65
      Author(s): David Agustriawan, Chien-Hung Huang, Jim Jinn-Chyuan Sheu, Shan-Chih Lee, Jeffrey J.P. Tsai, Nilubon Kurubanjerdjit, Ka-Lok Ng
      Epigenetic regulation has been linked to the initiation and progression of cancer. Aberrant expression of microRNAs (miRNAs) is one such mechanism that can activate or silence oncogenes (OCGs) and tumor suppressor genes (TSGs) in cells. A growing number of studies suggest that miRNA expression can be regulated by methylation modification, thus triggering cancer development. However, there is no comprehensive in silico study concerning miRNA regulation by direct DNA methylation in cancer. Ovarian serous cystadenocarcinoma (OSC) was therefore chosen as a tumor model for the present work. Twelve batches of OSC data, with at least 35 patient samples in each batch, were obtained from The Cancer Genome Atlas (TCGA) database. The Spearman rank correlation coefficient (SRCC) was used to quantify the correlation between the CpG DNA methylation level and miRNA expression level. Meta-analysis was performed to reduce the effects of biological heterogeneity among different batches. MiRNA-target interactions were also inferred by computing SRCC and meta-analysis to assess the correlation between miRNA expression and cancer-associated gene expression and the interactions were further validated by a query against the miRTarBase database. A total of 26 potential epigenetic-regulated miRNA genes that can target OCGs or TSGs in OSC were found to show biological relevance between DNA methylation and miRNA gene expression. Furthermore, some of the identified DNA-methylated miRNA genes; for instance, the miR-200 family, were previously identified as epigenetic-regulated miRNAs and correlated with poor survival of ovarian cancer. We also found that several miRNA target genes, BTG3, NDN, HTRA3, CDC25A, and HMGA2 were also related to the poor outcomes in ovarian cancer. The present study proposed a systematic strategy to construct highly confident epigenetic-regulated miRNA pathways for OSC. The findings are validated and are in line with the literature. The inclusion of direct DNA methylated miRNA events may offer another layer of explanation that along with genetics can give a better understanding of the carcinogenesis process.

      PubDate: 2016-11-26T20:09:57Z
  • Modeling of the catalytic core of Arabidopsis thaliana Dicer-like 4
           protein and its complex with double-stranded RNA
    • Abstract: Publication date: Available online 17 November 2016
      Source:Computational Biology and Chemistry
      Author(s): Agnieszka Mickiewicz, Joanna Sarzyńska, Maciej Miłostan, Anna Kurzyńska-Kokorniak, Agnieszka Rybarczyk, Piotr Łukasiak, Tadeusz Kuliński, Marek Figlerowicz, Jacek Błażewicz
      Plant Dicer-like proteins (DCLs) belong to the Ribonuclease III (RNase III) enzyme family. They are involved in the regulation of gene expression and antiviral defense through RNA interference pathways. A model plant, Arabidopsis thaliana encodes four DCL proteins (AtDCL1-4) that produce different classes of small regulatory RNAs. Our studies focus on AtDCL4 that processes double-stranded RNAs (dsRNAs) into 21 nucleotide trans-acting small interfering RNAs. So far, little is known about the structures of plant DCLs and the complexes they form with dsRNA. In this work, we present models of the catalytic core of AtDCL4 and AtDCL4-dsRNA complex constructed by computational methods. We built a homology model of the catalytic core of AtDCL4 comprising Platform, PAZ, Connector helix and two RNase III domains. To assemble the AtDCL4-dsRNA complex two modeling approaches were used. In the first method, to establish conformations that allow building a consistent model of the complex, we used Normal Mode Analysis for both dsRNA and AtDCL4. The second strategy involved template-based approach for positioning of the PAZ domain and manual arrangement of the Connector helix. Our results suggest that the spatial orientation of the Connector helix, Platform and PAZ relative to the RNase III domains is crucial for measuring dsRNA of defined length. The modeled complexes provide information about interactions that may contribute to the relative orientations of these domains and to dsRNA binding. All these information can be helpful for understanding the mechanism of AtDCL4-mediated dsRNA recognition and binding, to produce small RNA of specific size.
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      PubDate: 2016-11-21T20:01:17Z
  • Insights into structure and function of 30S Ribosomal Protein S2 (30S2) in
           Chlamydophila Pneumoniae: A potent target of Pneumonia
    • Abstract: Publication date: Available online 9 November 2016
      Source:Computational Biology and Chemistry
      Author(s): G. Koteswara Reddy, K. Nagamalleswara Rao, Kiran Yarrakula
      The gene 30S ribosomal protein S2 (30S2) is identified as a potential drug and vaccine target for Pneumonia. Its structural characterization is an important to understand the mechanism of action for identifying its receptor and/or other binding partners. The comparative genomics and proteomics studies are useful for structural characterization of 30S2 in C. Pneumoniae using different bioinformatics tools and web servers. In this study, the protein 30S2 structure was modelled and validated by Ramachandran plot. It is found that the modelled protein under most favoured “core” region was 88.7% and overall G-factor statistics with average score was −0.20. However, seven sequential motifs have been identified for 30S2 with reference codes (PR0095, PF0038, TIGR01012, PTHR11489, SSF52313 and PTHR11489). In addition, seven structural highly conserved residues have been identified in the large cleft are Lys160, Gly161and Arg162 with volume 1288.83Å3 and average depth of the cleft was 10.75Å. Moreover, biological functions, biochemical process and structural constituents of ribosome are also explored. The study will be helped us to understand the sequential, structural, functional and evolutionary clues of unknown proteins available in C. Pneumoniae.
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      PubDate: 2016-11-15T13:00:54Z
  • Identification of miRNA from Bouteloua gracilis, a drought tolerant grass,
           by deep sequencing and their in silico analysis
    • Abstract: Publication date: Available online 9 November 2016
      Source:Computational Biology and Chemistry
      Author(s): Perla Lucía Ordóñez-Baquera, Everardo González-Rodríguez, Gerardo Armando Aguado-Santacruz, Quintín Rascón-Cruz, Ana Conesa, Verónica Moreno-Brito, Raquel Echavarria, Joel Dominguez-Viveros
      Background MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate signal transduction, development, metabolism, and stress responses in plants through post-transcriptional degradation and/or translational repression of target mRNAs. Several studies have addressed the role of miRNAs in model plant species, but miRNA expression and function in economically important forage crops, such as Bouteloua gracilis (Poaceae), a high-quality and drought-resistant grass distributed in semiarid regions of the United States and northern Mexico remain unknown. Results We applied high-throughput sequencing technology and bioinformatics analysis and identified 31 conserved miRNA families and 53 novel putative miRNAs with different abundance of reads in chlorophyllic cell cultures derived from B. gracilis. Some conserved miRNA families were highly abundant and possessed predicted targets involved in metabolism, plant growth and development, and stress responses. We also predicted additional identified novel miRNAs with specific targets, including B. gracilis ESTs, which were detected under drought stress conditions. Conclusions Here we report 31 conserved miRNA families and 53 putative novel miRNAs in B. gracilis. Our results suggested the presence of regulatory miRNAs involved in modulating physiological and stress responses in this grass species.
      Graphical abstract image

      PubDate: 2016-11-15T13:00:54Z
  • Investigating dysregulated pathways in Staphylococcus aureus (SA) exposed
           macrophages based on pathway interaction network
    • Abstract: Publication date: Available online 13 November 2016
      Source:Computational Biology and Chemistry
      Author(s): Wei Zhou, Yan Zhang, Yue-Hua Li, Shuang Wang, Jing-Jing Zhang, Cui-Xia Zhang, Zhi-Sheng Zhang
      Objective This work aimed to identify dysregulated pathways for Staphylococcus aureus (SA) exposed macrophages based on pathway interaction network (PIN). Methods The inference of dysregulated pathways was comprised of four steps: preparing gene expression data, protein-protein interaction (PPI) data and pathway data; constructing a PIN dependent on the data and Pearson correlation coefficient (PCC); selecting seed pathway from PIN by computing activity score for each pathway according to principal component analysis (PCA) method; and investigating dysregulated pathways in a minimum set of pathways (MSP) utilizing seed pathway and the area under the receiver operating characteristics curve (AUC) index implemented in support vector machines (SVM) model. Results A total of 20,545 genes, 449,833 interactions and 1,189 pathways were obtained in the gene expression data, PPI data and pathway data, respectively. The PIN was consisted of 8,388 interactions and 1,189 nodes, and Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins was identified as the seed pathway. Finally, 15 dysregulated pathways in MSP (AUC=0.999) were obtained for SA infected samples, such as Respiratory electron transport and DNA Replication. Conclusions We have identified 15 dysregulated pathways for SA infected macrophages based on PIN. The findings might provide potential biomarkers for early detection and therapy of SA infection, and give insights to reveal the molecular mechanism underlying SA infections. However, how these dysregulated pathways worked together still needs to be studied.
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      PubDate: 2016-11-15T13:00:54Z
  • Computational analysis, structural modeling and ligand binding site
           prediction of Plasmodium falciparum 1-deoxy-d-xylulose-5-phosphate
    • Abstract: Publication date: Available online 5 November 2016
      Source:Computational Biology and Chemistry
      Author(s): Achintya Mohan Goswami
      Malaria remains one of the most serious infectious diseases in the world. Though there are four species of Plasmodium genus, but the most responsible and virulent among them is Plasmodium falciparum. The unique biochemical processes that exist in Plasmodium falciparum provide a useful way to develop novel inhibitors. One such biochemical pathway is the methyl erythritol phosphate pathway (MEP), required to synthesize isoprenoid precursors. In the present study, a detailed computational analysis has been performed for 1-deoxy-d-xylulose-5-phosphate synthase, a key enzyme in MEP. The protein is found to be stable and residues from 825 to 971 are highly conserved across species. The homology model of the enzyme is developed using three web-based servers and Modeller software. It has twelve disordered regions indicating its druggability. Virtual screening of ZINC database identifies ten potential compounds in thiamine diphosphate binding region of the enzyme.
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      PubDate: 2016-11-09T12:45:46Z
  • Development of a sugar-binding residue prediction system from protein
           sequences using support vector machine
    • Abstract: Publication date: Available online 9 November 2016
      Source:Computational Biology and Chemistry
      Author(s): Masaki Banno, Yusuke Komiyama, Wei Cao, Yuya Oku, Kokoro Ueki, Kazuya Sumikoshi, Shugo Nakamura, Tohru Terada, Kentaro Shimizu
      Several methods have been proposed for protein–sugar binding site prediction using machine learning algorithms. However, they are not effective to learn various properties of binding site residues caused by various interactions between proteins and sugars. In this study, we classified sugars into acidic and nonacidic sugars and showed that their binding sites have different amino acid occurrence frequencies. By using this result, we developed sugar-binding residue predictors dedicated to the two classes of sugars: an acid sugar binding predictor and a nonacidic sugar binding predictor. We also developed a combination predictor which combines the results of the two predictors. We showed that when a sugar is known to be an acidic sugar, the acidic sugar binding predictor achieves the best performance, and showed that when a sugar is known to be a nonacidic sugar or is not known to be either of the two classes, the combination predictor achieves the best performance. Our method uses only amino acid sequences for prediction. Support vector machine was used as a machine learning algorithm and the position-specific scoring matrix created by the position-specific iterative basic local alignment search tool was used as the feature vector. We evaluated the performance of the predictors using five-fold cross-validation. We have launched our system, as an open source freeware tool on the GitHub repository (
      Graphical abstract image Highlights

      PubDate: 2016-11-09T12:45:46Z
  • In silico designing breast cancer peptide vaccine for binding to MHC class
           I and II: A molecular docking study
    • Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65
      Author(s): Manijeh Mahdavi, Violaine Moreau
      Antigenic peptides or cancer peptide vaccines can be directly delivered to cancer patients to produce immunologic responses against cancer cells. Specifically, designed peptides can associate with Major Histocompatibility Complex (MHC) class I or II molecules on the cell surface of antigen presenting cells activating anti-tumor effector mechanisms by triggering helper T cell (Th) or cytotoxic T cells (CTL). In general, high binding to MHCs approximately correlates with in vivo immunogenicity. Consequently, a molecular docking technique was run on a library of novel discontinuous peptides predicted by PEPOP from Human epidermal growth factor receptor 2 (HER2 ECD) subdomain III. This technique is expected to improve the prediction accuracy in order to identify the best MHC class I and II binder peptides. Molecular docking analysis through GOLD identified the peptide 1412 as the best MHC binder peptide to both MHC class I and II molecules used in the study. The GOLD results predicted HLA-DR4, HLA-DP2 and TCR as the most often targeted receptors by the peptide 1412. These findings, based on bioinformatics analyses, can be exploited in further experimental analyses in vaccine design and cancer therapy to find possible proper approaches providing beneficial effects.
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      PubDate: 2016-11-03T12:31:54Z
  • SVM and SVR-based MHC-binding prediction using a mathematical presentation
           of peptide sequences
    • Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65
      Author(s): Davorka R. Jandrlić
      At present, there are a number of methods for the prediction of T-cell epitopes and major histocompatibility complex (MHC)-binding peptides. Despite numerous methods for predicting T-cell epitopes, there still exist limitations that affect the reliability of prevailing methods. For this reason, the development of models with high accuracy are crucial. An accurate prediction of the peptides that bind to specific major histocompatibility complex class I and II (MHC-I and MHC-II) molecules is important for an understanding of the functioning of the immune system and the development of peptide-based vaccines. Peptide binding is the most selective step in identifying T-cell epitopes. In this paper, we present a new approach to predicting MHC-binding ligands that takes into account new weighting schemes for position-based amino acid frequencies, BLOSUM and VOGG substitution of amino acids, and the physicochemical and molecular properties of amino acids. We have made models for quantitatively and qualitatively predicting MHC-binding ligands. Our models are based on two machine learning methods support vector machine (SVM) and support vector regression (SVR), where our models have used for feature selection, several different encoding and weighting schemes for peptides. The resulting models showed comparable, and in some cases better, performance than the best existing predictors. The obtained results indicate that the physicochemical and molecular properties of amino acids (AA) contribute significantly to the peptide-binding affinity.
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      PubDate: 2016-11-03T12:31:54Z
  • IFC Editorial Board
    • Abstract: Publication date: October 2016
      Source:Computational Biology and Chemistry, Volume 64

      PubDate: 2016-11-03T12:31:54Z
  • Title page
    • Abstract: Publication date: October 2016
      Source:Computational Biology and Chemistry, Volume 64

      PubDate: 2016-11-03T12:31:54Z
  • Editorial
    • Abstract: Publication date: October 2016
      Source:Computational Biology and Chemistry, Volume 64
      Author(s): Jaap Heringa, Wentian Li, Jeffry Madura

      PubDate: 2016-11-03T12:31:54Z
  • Small Family, Big Impact: In silico analysis of DREB2 transcription factor
           family in rice
    • Abstract: Publication date: Available online 29 October 2016
      Source:Computational Biology and Chemistry
      Author(s): Venura Herath
      Dehydration-responsive element- (DREB) proteins are considered as the master regulators of plant abiotic stress responses including drought, salinity and cold. They are also involved in other developmental processes such as embryo and endosperm development. DREB family of transcription factors consist of two sub families namely CBF1/DREB1 and DREB2. In this study, a genome-wide in silico analysis was carried out to dissect the structure and function of DREB2 family transcription factors in the rice genome. Using Arabidopsis DREB2 sequences a total of five rice DREB2 homologs were identified and they were distributed among four chromosomes. All OsDREBs contained the AP2 domain and unique [K/R]GKKGPxN motif characteristic to DREB2 family. During rice growth and development, three OsDREB2s namely OsDREB2A, OsDREB2B and OsABI4 were expressed and their expression was confined to embryo and endosperm tissues. OsDREB2A, OsDREB2B and OsDREB2C were expressed under abiotic stress conditions. OsDREB2B was expressed under drought, salinity and cold stress conditions while OsDREB2A and OsDREB2C were expressed only under drought and salinity conditions. The putative promoter regions of OsDREB2s were enriched with elements related to cellular development, hormonal regulation and stress response validating the observed expression dynamics. Co-expression analysis revealed that embryo development and stress related genes were expressed together with OsDREB2s. Predicted post-translational modifications indicated the fine regulation of OsDREB2s. These findings may shed light in uncovering the complex abiotic stress signaling networks and future genomics studies targeting the development of climate ready crops.
      Graphical abstract image

      PubDate: 2016-11-03T12:31:54Z
  • Solvent Effect on Hydrogen Bonded Tyr⋯Asp⋯Arg Triads:
           Enzymatic Catalyzed Model System
    • Abstract: Publication date: Available online 2 November 2016
      Source:Computational Biology and Chemistry
      Author(s): Shihai Yan, Lishan Yao, Baotao Kang, Jin Yong Lee
      The hydrogen bond plays a vital role in structural arrangement, intermediate state stabilization, materials function, and biological activity of certain enzymatic reactions. The solvent and electronic effects on hydrogen bonds are illustrated employing the polarizable contimuum model at B3LYP/6–311++G(d,p) level. Geometry optimizations reflect the significant solvent and electronic effect. The proton departs spontaneously upon oxidation from the hydroxyl group of tyrosyl in hydrogen bonded Tyr⋯Asp⋯Arg triads in both gas phase and solvents. The electron transfer isomers are observed for anionic triads, no matter what the solvent is. The difference of distance between two hydrogen bonds is enlarged in solvent as compared to that in gas phase. The electronic effect on IR spectra is distinctive. The tyrosyl fragment tends to be oxidized and the arginine moiety is easier to bind an excess electron. The variations of chemical shift and spin-spin coupling constant are more significant upon electron transfer than upon solvent dielectric constant. The augmentation of solvent dielectric constant stabilizes the system, enhances the difference of isomers, and increases the vertical ionization potential and vertical electron affinity values.
      Graphical abstract image

      PubDate: 2016-11-03T12:31:54Z
  • In silico investigation of the impact of synonymous variants in ABCB4 gene
           on mRNA stability/structure, splicing accuracy and codon usage: Potential
           contribution to PFIC3 disease
    • Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65
      Author(s): Boudour Khabou, Olfa Siala-Sahnoun, Lamia Gargouri, Emna Mkaouar-Rebai, Leila Keskes, Mongia Hachicha, Faiza Fakhfakh
      Progressive Familial Intrahepatic Cholestasis type 3 (PFIC3) is an autosomal-recessive liver disease due to mutations in the ABCB4 gene encoding for the MDR3 protein. In the present study, we performed molecular and bioinformatic analyses in PFIC3 patients in order to understand the molecular basis of the disease. The three studied patients with PFIC3 were screened by PCR amplification followed by direct sequencing of the 27 coding exons of ABCB4. In silico analysis was performed by bioinformatic programs. We revealed three synonymous polymorphisms c.175C>T, c.504C>T, c.711A>T respectively in exon 4, 6, 8 and an intronic c.3487-16T>C variation in intron 26. The computational study of these polymorphic variants using Human Splicing Finder, ex-skip, Mfold and kineFold tools showed the putative impact on the composition of the cis-acting regulatory elements of splicing as well as on the mRNA structure and stability. Moreover, the protein level was affected by codon usage changes estimated by the calculation of ΔRSCU and ΔLog Ratio of codon frequencies interfering as consequence with the accurate folding of the MDR3 protein. As the first initiative of the mutational study of ABCB4 genes in Tunisia, our results are suggestive of a potential downstream molecular effect for the described polymorphisms on the expression pattern of the ABCB4 underlining the importance of synonymous variants.
      Graphical abstract image

      PubDate: 2016-11-03T12:31:54Z
  • Molecular dynamics simulations reveal the allosteric effect of F1174C
           resistance mutation to ceritinib in ALK-associated lung cancer
    • Abstract: Publication date: Available online 11 October 2016
      Source:Computational Biology and Chemistry
      Author(s): Zhong Ni, Xiting Wang, Tianchen Zhang, Rong Zhong Jin
      Anaplastic lymphoma kinase (ALK) has become as an important target for the treatment of various human cancers, especially non-small-cell lung cancer. A mutation, F1174C, suited in the C-terminal helix αC of ALK and distal from the small-molecule inhibitor ceritinib bound to the ATP-binding site, causes the emergence of drug resistance to ceritinib. However, the detailed mechanism for the allosteric effect of F1174C resistance mutation to ceritinib remains unclear. Here, molecular dynamics (MD) simulations and binding free energy calculations [Molecular Mechanics/Generalized Born Surface Area (MM/GBSA)] were carried out to explore the advent of drug resistance mutation in ALK. MD simulations observed that the exquisite aromatic-aromatic network formed by residues F1098, F1174, F1245, and F1271 in the wild-type ALK-ceritinib complex was disrupted by the F1174C mutation. The resulting mutation allosterically affected the conformational dynamic of P-loop and caused the upward movement of the P-loop from the ATP-binding site, thereby weakening the interaction between ceritinib and the P-loop. The subsequent MM/GBSA binding free energy calculations and decomposition analysis of binding free energy validated this prediction. This study provides mechanistic insight into the allosteric effect of F1174C resistance mutation to ceritinib in ALK and is expected to contribute to design the next-generation of ALK inhibitors.
      Graphical abstract image

      PubDate: 2016-10-16T11:30:26Z
  • In Silico identification of outer membrane protein (Omp) and subunit
           vaccine design against pathogenic Vibrio cholerae
    • Abstract: Publication date: Available online 12 October 2016
      Source:Computational Biology and Chemistry
      Author(s): Pradipta Ranjan Rauta, Sarbani Ashe, Debasis Nayak, Bismita Nayak
      Virulence-related outer membrane proteins (Omps) are expressed in bacteria (Gram-negative) such as V. cholerae and are vital to bacterial invasion in to eukaryotic cell and survival within macrophages that could be best candidate for development of vaccine against V. cholerae. Applying in silico approaches, the 3-D model of the Omp was developed using Swiss model server and validated byProSA and Procheck web server. The continuous stretch of amino acid sequences 26mer: RTRSNSGLLTWGDKQTITLEYGDPAL and 31mer: FFAGGDNNLRGYGYKSISPQDASGALTGAKY having B-cell binding sites were selected from sequence alignment after B cell epitopes prediction by BCPred and AAP prediction modules of BCPreds. Further, the selected antigenic sequences (having B-cell epitopes) were analyzed for T-cell epitopes (MHC I and MHC II alleles binding sequence) by using ProPred 1 and ProPred respectively. The epitope (9 mer: YKSISPQDA) that binds to both the MHC classes (MHC I and MHC II) and covers maximum MHC alleles were identified. The identified epitopes can be useful in designing comprehensive peptide vaccine development against V. cholerae by inducing optimal immune response.
      Graphical abstract image

      PubDate: 2016-10-16T11:30:26Z
  • Side-chain Dynamics Analysis of KE07 Series
    • Abstract: Publication date: Available online 15 October 2016
      Source:Computational Biology and Chemistry
      Author(s): Xin Geng, Jiaogen Zhou, Jihong Guan
      The significant improvement of KE07 series in catalytic activities shows the great success of computational design approaches combined with directed evolution in protein design. Understanding the protein dynamics in the evolutionary optimization process of computationally designed enzyme will provide profound implication to study enzyme function and guide protein design. Here, side chain squared generalized order parameters and entropy of each protein are calculated using 50ns molecular dynamics simulation data in both apo and bound states. Our results show a correlation between the increase of side chain motion amplitude and catalytic efficiency. By analyzing the relationship between these two values, we find side chain squared generalized order parameter is linearly related to side chain entropy, which indicates the computationally designed KE07 series have similar dynamics property with natural enzymes.

      PubDate: 2016-10-16T11:30:26Z
  • Free radical scavenging and COX-2 inhibition by simple colon metabolites
           of polyphenols: A theoretical approach
    • Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65
      Author(s): Ana Amić, Zoran Marković, Jasmina M. Dimitrić Marković, Svetlana Jeremić, Bono Lučić, Dragan Amić
      Free radical scavenging and inhibitory potency against cyclooxygenase-2 (COX-2) by two abundant colon metabolites of polyphenols, i.e., 3-hydroxyphenylacetic acid (3-HPAA) and 4-hydroxyphenylpropionic acid (4-HPPA) were theoretically studied. Different free radical scavenging mechanisms are investigated in water and pentyl ethanoate as a solvent. By considering electronic properties of scavenged free radicals, hydrogen atom transfer (HAT) and sequential proton loss electron transfer (SPLET) mechanisms are found to be thermodynamically probable and competitive processes in both media. The Gibbs free energy change for reaction of inactivation of free radicals indicates 3-HPAA and 4-HPPA as potent scavengers. Their reactivity toward free radicals was predicted to decrease as follows: hydroxyl>>alkoxyls>phenoxyl≈peroxyls>>superoxide. Shown free radical scavenging potency of 3-HPAA and 4-HPPA along with their high μM concentration produced by microbial colon degradation of polyphenols could enable at least in situ inactivation of free radicals. Docking analysis with structural forms of 3-HPAA and 4-HPPA indicates dianionic ligands as potent inhibitors of COX-2, an inducible enzyme involved in colon carcinogenesis. Obtained results suggest that suppressing levels of free radicals and COX-2 could be achieved by 3-HPAA and 4-HPPA indicating that these compounds may contribute to reduced risk of colon cancer development.
      Graphical abstract image

      PubDate: 2016-10-16T11:30:26Z
  • Comparison of Ebola virus polymerase domains to gain insight on its
    • Abstract: Publication date: Available online 15 October 2016
      Source:Computational Biology and Chemistry
      Author(s): Seema Patel
      Hepatitis C (HCV) is a deadly virus from family Flaviviridae, causing acute or chronic liver inflammation. Given its lethality and no known vaccine to curb it, understanding its pathogenic mechanism is critical. By analyzing the domains in its protein sequence, a plethora can be learnt about its immune manipulation strategies. In this regard, current in silico study compares publicly-available HCV polyprotein sequences and their domain profiles. Apart from using UniProt sequences and SMART (Simple modular architecture research tool) platform for domain profiling, a set of customized scripts were developed to extract the patterns of protein domain distribution. Also, the total domain set in representative HCV sequences were compared with that of other viral pathogens (Ebola, HIV, Dengue, Zika) and allergens (plant pollen, cockroach allergens) to find the conserved domains of key role in pathogenesis.
      Graphical abstract image

      PubDate: 2016-10-16T11:30:26Z
  • Weighted Edge Based Clustering to Identify Protein Complexes in Protein
           Protein Interaction Networks incorporating Gene Expression Profile
    • Authors: Seketoulie Keretsu; Rosy Sarmah
      Abstract: Publication date: Available online 8 October 2016
      Source:Computational Biology and Chemistry
      Author(s): Seketoulie Keretsu, Rosy Sarmah
      Protein complex detection from Protein Protein Interaction (PPI) network has received a lot of focus in recent years. A number of methods identify protein complexes as dense sub-graphs using network information while several other methods detect protein complexes based on topological information. While the methods based on identifying dense sub-graphs are more effective in identifying protein complexes, not all protein complexes have high density. Moreover, existing methods focus more on static PPI networks and usually overlook the dynamic nature of protein complexes. Here, we propose a new method, Weighted Edge based Clustering (WEC), to identify protein complexes based on the weight of the edge between two interacting proteins, where the weight is defined by the edge clustering coefficient and the gene expression correlation between the interacting proteins. Our WEC method is capable of detecting highly inter-connected and co-expressed protein complexes. The experimental results of WEC on three real life data shows that our method can detect protein complexes effectively in comparison with other highly cited existing methods. Availability: The WEC tool is available at

      PubDate: 2016-10-09T18:58:02Z
      DOI: 10.1016/j.compbiolchem.2016.10.001
  • Protein-protein interaction and molecular dynamics analysis for
           identification of novel inhibitors in Burkholderia cepacia GG4
    • Authors: Money Gupta; Rashi Chauhan; Yamuna Prasad; Gulshan Wadhwa; Chakresh Kumar Jain
      Abstract: Publication date: Available online 8 October 2016
      Source:Computational Biology and Chemistry
      Author(s): Money Gupta, Rashi Chauhan, Yamuna Prasad, Gulshan Wadhwa, Chakresh Kumar Jain
      The lack of complete treatments and appearance of multiple drug-resistance strains of Burkholderia cepacia complex (Bcc) are causing an increased risk of lung infections in cystic fibrosis patients. Bcc infection is a big risk to human health and demands an urgent need to identify new therapeutics against these bacteria. Network biology has emerged as one of the prospective hope in identifying novel drug targets and hits. We have applied protein-protein interaction methodology to identify new drug-target candidates (orthologs) in Burkhloderia cepacia GG4, which is an important strain for studying the quorum-sensing phenomena. An evolutionary based ortholog mapping approach has been applied for generating the large scale protein-protein interactions in B. Cepacia. As a case study, one of the identified drug targets; GEM_3202, a NH (3)-dependent NAD synthetase protein has been studied and the potential ligand molecules were screened using the ZINC database. The three dimensional structure (NH (3)-dependent NAD synthetase protein) has been predicted from MODELLERv9.11 tool using multiple PDB templates such as 3DPI, 2PZ8 and 1NSY with sequence identity of 76%, 50% and 50% respectively. The structure has been validated with Ramachandaran plot having 100% residues of NadE in allowed region and overall quality factor of 81.75 using ERRAT tool. High throughput screening and Vina resulted in two potential hits against NadE such as ZINC83103551 and ZINC38008121. These molecules showed lowest binding energy of −5.7kcal/mol and high stability in the binding pockets during molecular dynamics simulation analysis. The similar approach for target identification could be applied for clinical strains of other pathogenic microbes.
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      PubDate: 2016-10-09T18:58:02Z
      DOI: 10.1016/j.compbiolchem.2016.10.003
  • Building and analysis of protein-protein interactions related to diabetes
           mellitus using support vector machine, biomedical text mining and network
    • Authors: Renu Vyas; Sanket Bapat Esha Jain Muthukumarasamy Karthikeyan Sanjeev Tambe
      Abstract: Publication date: Available online 30 September 2016
      Source:Computational Biology and Chemistry
      Author(s): Renu Vyas, Sanket Bapat, Esha Jain, Muthukumarasamy Karthikeyan, Sanjeev Tambe, Bhaskar D. Kulkarni
      In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein–protein interactions therefore assume significance. 1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies.
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      PubDate: 2016-10-02T17:51:38Z
  • A Post-Decoding Re-Ranking Algorithm for Predicting Interacting Residues
           in Proteins with Hidden Markov Models Incorporating Long-Distance
    • Authors: Colin Kern; Li Liao
      Abstract: Publication date: Available online 29 September 2016
      Source:Computational Biology and Chemistry
      Author(s): Colin Kern, Li Liao
      Protein-protein interactions play a central role in the biological processes of cells. Accurate prediction of the interacting residues in protein-protein interactions enhances understanding of the interaction mechanisms and enables in silico mutagenesis, which can help facilitate drug design and deepen our understanding of the inner workings of cells. Correlations have been found among interacting residues as a result of selection pressure to retain the interaction during evolution. In previous work, incorporation of such correlations in the interaction profile hidden Markov models with a special decoding algorithm (ETB-Viterbi) has led to improvement in prediction accuracy. In this work, we first demonstrated the sub-optimality of the ETB-Viterbi algorithm, and then reformulated the optimality of decoding paths to include correlations between interacting residues. To identify optimal decoding paths, we propose a post-decoding re-ranking algorithm based on a genetic algorithm with simulated annealing and show that the new method gains an increase of near 14% in prediction accuracy over the ETB-Viterbi algorithm.

      PubDate: 2016-10-02T17:51:38Z
      DOI: 10.1016/j.compbiolchem.2016.09.015
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