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AATCC Journal of Research     Full-text available via subscription   (Followers: 3)
ACS Sustainable Chemistry & Engineering     Hybrid Journal  
Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials     Hybrid Journal   (Followers: 4)
Acta Polymerica     Hybrid Journal   (Followers: 7)
Additives for Polymers     Full-text available via subscription   (Followers: 20)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 6)
Advanced Chemical Engineering Research     Open Access   (Followers: 27)
Advanced Powder Technology     Hybrid Journal   (Followers: 12)
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: 50)
Advances in Polymer Technology     Hybrid Journal   (Followers: 12)
African Journal of Pure and Applied Chemistry     Open Access   (Followers: 6)
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: 9)
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: 13)
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: 11)
Chemical and Materials Engineering     Open Access   (Followers: 6)
Chemical and Petroleum Engineering     Hybrid Journal   (Followers: 10)
Chemical and Process Engineering     Open Access   (Followers: 19)
Chemical and Process Engineering Research     Open Access   (Followers: 17)
Chemical Engineering & Technology     Hybrid Journal   (Followers: 32)
Chemical Engineering and Processing: Process Intensification     Hybrid Journal   (Followers: 17)
Chemical Engineering and Science     Open Access   (Followers: 13)
Chemical Engineering Communications     Hybrid Journal   (Followers: 11)
Chemical Engineering Journal     Hybrid Journal   (Followers: 26)
Chemical Engineering Research and Design     Hybrid Journal   (Followers: 20)
Chemical Engineering Research Bulletin     Open Access   (Followers: 8)
Chemical Engineering Science     Hybrid Journal   (Followers: 19)
Chemical Geology     Hybrid Journal   (Followers: 14)
Chemical Papers     Hybrid Journal   (Followers: 2)
Chemical Product and Process Modeling     Hybrid Journal   (Followers: 3)
Chemical Reviews     Full-text available via subscription   (Followers: 123)
Chemical Society Reviews     Full-text available via subscription   (Followers: 39)
Chemical Technology     Open Access   (Followers: 11)
ChemInform     Hybrid Journal   (Followers: 4)
Chemistry & Industry     Hybrid Journal   (Followers: 2)
Chemistry Central Journal     Open Access   (Followers: 5)
Chemistry of Materials     Full-text available via subscription   (Followers: 141)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 5)
Chinese Chemical Letters     Full-text available via subscription   (Followers: 3)
Chinese Journal of Chemical Engineering     Full-text available via subscription   (Followers: 3)
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: 9)
Computer Aided Chemical Engineering     Full-text available via subscription   (Followers: 1)
Computers & Chemical Engineering     Hybrid Journal   (Followers: 9)
CORROSION     Full-text available via subscription   (Followers: 19)
Corrosion Engineering, Science and Technology     Hybrid Journal   (Followers: 35)
Corrosion Reviews     Hybrid Journal   (Followers: 3)
Crystal Research and Technology     Hybrid Journal   (Followers: 5)
Current Opinion in Chemical Engineering     Open Access   (Followers: 6)
Education for Chemical Engineers     Hybrid Journal   (Followers: 4)
Eksergi     Open Access  
Emerging Trends in Chemical Engineering     Full-text available via subscription  
European Polymer Journal     Hybrid Journal   (Followers: 40)
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: 3)
Heat Exchangers     Open Access   (Followers: 1)
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  
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  
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: 103)
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: 10)
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: 13)
Journal of Chemical Engineering and Materials Science     Open Access   (Followers: 2)
Journal of Chemical Science and Technology     Open Access   (Followers: 4)
Journal of Chemical Sciences     Partially Free   (Followers: 17)
Journal of Chemical Technology & Biotechnology     Hybrid Journal   (Followers: 10)
Journal of Chemical Theory and Computation     Full-text available via subscription   (Followers: 15)
Journal of CO2 Utilization     Hybrid Journal   (Followers: 2)
Journal of Coatings     Open Access   (Followers: 4)
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 Fuels     Open Access  
Journal of Geochemical Exploration     Hybrid Journal  
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: 6)
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 Powder Technology     Open Access   (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: 234)
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 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  
MRS Communications     Hybrid Journal  
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: 94)
Polymer Bulletin     Hybrid Journal   (Followers: 7)
Polymer Composites     Hybrid Journal   (Followers: 13)
Polyolefins Journal     Open Access  
Powder Technology     Hybrid Journal   (Followers: 12)
Recyclable Catalysis     Open Access   (Followers: 1)
Research on Chemical Intermediates     Hybrid Journal  
Reviews in Chemical Engineering     Hybrid Journal   (Followers: 5)
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: 56)
Solid Fuel Chemistry     Hybrid Journal  
South African Journal of Chemical Engineering     Open Access   (Followers: 2)
South African Journal of Chemistry     Open Access   (Followers: 2)
Surface Engineering and Applied Electrochemistry     Hybrid Journal   (Followers: 5)
Sustainable Chemical Processes     Open Access   (Followers: 2)
Synthesis Lectures on Chemical Engineering and Biochemical Engineering     Full-text available via subscription  
The Canadian Journal of Chemical Engineering     Hybrid Journal   (Followers: 3)
The Chemical Record     Hybrid Journal   (Followers: 1)
Theoretical Foundations of Chemical Engineering     Hybrid Journal   (Followers: 2)
Transition Metal Chemistry     Hybrid Journal   (Followers: 2)
Transylvanian Review of Systematical and Ecological Research     Open Access  
Visegrad Journal on Bioeconomy and Sustainable Development     Open Access   (Followers: 1)
Zeitschrift für Naturforschung B : A Journal of Chemical Sciences     Open Access   (Followers: 1)


Journal Cover Computational Biology and Chemistry
  [SJR: 0.688]   [H-I: 43]   [9 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1476-9271
   Published by Elsevier Homepage  [2969 journals]
  • Identification and characterization of promoters and cis-regulatory
           elements of genes involved in secondary metabolites production in hop
           (Humulus lupulus. L)
    • Abstract: Publication date: Available online 24 August 2016
      Source:Computational Biology and Chemistry
      Author(s): Ganesh Selvaraj Duraisamy, Ajay Kumar Mishra, Tomas Kocabek, Jaroslav Matoušek
      Molecular and biochemical studies have shown that gene contains single or combination of different cis-acting regulatory elements are actively controlling the transcriptional regulation of associated genes, downstream effects of these result in modulation of various biological pathways such as biotic/abiotic stress responses, hormonal responses to growth and development processes and secondary metabolite production. Therefore, the identification of promoters and their cis-regulatory elements is one of intriguing area to study the dynamic complex regulatory network of genes activities by integrating computational, comparative, structural and functional genomics. A variety of bioinformatics servers or database have been established to predict the cis-acting elements present in the promoter region of target gene and their association with the expression profiles in the TFs. The aim of this study is to predict possible cis-acting regulatory elements that have putative role in the transcriptional regulation of a dynamic network of metabolite gene activities controlling prenylflavonoid and bitter acids biosynthesis in hop (Humulus lupulus). Recent release of hop draft genome enabled us to predict the possible cis-acting regulatory elements by extracting 2kbp of 5′ regulatory regions of genes important for lupulin metabolome biosynthesis, using Plant CARE, PLACE and Genomatix Matinspector professional databases. The result reveals the plausible role of cis-acting regulatory elements in the regulation of gene expression primarily involved in lupulin metabolome biosynthesis including under various stress conditions.
      Graphical abstract image

      PubDate: 2016-08-27T19:11:14Z
  • Hybrid docking-QSAR studies of DPP-IV inhibition activities of a series of
    • Abstract: Publication date: Available online 20 August 2016
      Source:Computational Biology and Chemistry
      Author(s): Zohreh Amini, Mohammad Hossein Fatemi, Sajjad Gharaghani
      In this study, the dipeptidyl peptidase-IV (DPP-IV) inhibition activities of a series of novel aminomethyl-piperidones were investigated by molecular docking studies and modeled by quantitative structure–activity relationship (QSAR) methodology. Molecular docking studies were used to find the best conformations of the studied molecules in the active site of DPP-IV protein. Then the best docking-derived conformation for each molecule was applied for calculating the molecular descriptors. Multiple linear regression (MLR) and Levenberg–Marquardt artificial neural network (LM-ANN) were conducted on descriptors derived by docking. The results of these models revealed the superiority of LM-ANN model over MLR which showed the nonlinear relationship between the selected molecular descriptors and DPP-IV inhibition activities of studied molecules. The correlation coefficient (R) and standard error (SE) of ANN model were 0.983 and 0.103 for the training set and 0.966 and 0.168 for the external test set. These results showed a close agreement between the experimental and calculated values of pIC50 which demonstrated the robustness of LM-ANN model in modeling of aminomethyl-piperidones. Applicability domain analysis and sensitivity analysis were applied on the obtained models. This study gives useful information for further experimental studies on DPP-IV inhibitors. The results of this work reveal the applicability of hybrid docking-QSAR methodology in ligand-protein studies.
      Graphical abstract image

      PubDate: 2016-08-23T19:01:12Z
  • Inhibitory activity of hibifolin on adenosine deaminase- experimental and
           molecular modeling study
    • Abstract: Publication date: Available online 22 August 2016
      Source:Computational Biology and Chemistry
      Author(s): K.G. Arun, C.S. Sharanya, P.M. Sandeep, C. Sadasivan
      Adenosine deaminase (ADA) is an enzyme involved in purine metabolism. ADA converts adenosine to inosine and liberates ammonia. Because of their critical role in the differentiation and maturation of cells, the regulation of ADA activity is considered as a potential therapeutic approach to prevent malignant and inflammatory disorders. In the present study, the inhibitory activity of a plant flavonoid, hibifolin on ADA is investigated using enzyme kinetic assay and isothermal titration calorimetry. The inhibitory constant of hibifolin was found to be 49.92μM±3.98 and the mode of binding was reversible. Isothermal titration calorimetry showed that the compound binds ADA with binding energy of −7.21Kcal/mol. The in silico modeling and docking studies showed that the bound ligand is stabilized by hydrogen bonds with active site residues of the enzyme. The study reveals that hibifolin can act as a potential inhibitor of ADA.
      Graphical abstract image

      PubDate: 2016-08-23T19:01:12Z
  • Roles of the respective loops at complementarity determining region on the
           antigen-antibody recognition
    • Abstract: Publication date: Available online 23 August 2016
      Source:Computational Biology and Chemistry
      Author(s): Tomonori Osajima, Tyuji Hoshino
      For the rational design of antibody, it is important to clarify the characteristics of the interaction between antigen and antibody. In this study, we evaluated a contribution of the respective complementarity determining region (CDR) loops on the antibody recognition of antigen by performing molecular dynamics simulations for 20 kinds of antigen-antibody complexes. Ser and Tyr showed high appearance rates at CDR loops and the sum of averaged appearance rates of Ser and Tyr was about 20 − 30% at all the loops. For example, Ser and Tyr occupied 23.9% at the light chain first loop (L1) and 23.6% at the heavy chain third loop (H3). The direct hydrogen bonds between antigen and antibody were not equally distributed over heavy and light chains. That is, about 70% of the hydrogen bonds were observed at CDRs of the heavy chain and also the direct hydrogen bond with the shortest distance mainly existed at the loops of the heavy chain for all the complexes. It was revealed from the comparison in contribution to the binding free energy among CDR loops that the heavy chain (especially at H2 and H3) had significant influence on the binding between antigen and antibody because three CDR loops of the heavy chain showed the lowest binding free energy (ΔG bind) in 19 complexes out of 20. Tyr in heavy chain (especially in H2 and H3) largely contributed to ΔG bind whereas Ser hardly contributed to ΔG bind even if the number of the direct hydrogen bond with Ser was the fourth largest and also the appearance rate at CDR was the highest among 20 kinds of amino acid residues. The contributions ofTrp and Phe, which bear aromatic ring in the side chain, were often observed in the heavy chain although the energetic contribution of these residues was not so high as Tyr. The present computational analysis suggests that Tyr plays an outstanding role for the antigen-antibody interaction and the CDR loops of the heavy chain is critically important for antibody recognition of antigen.
      Graphical abstract image

      PubDate: 2016-08-23T19:01:12Z
  • Recurrent Neural Network Based Hybrid Model for Reconstructing Gene
           Regulatory Network
    • Abstract: Publication date: Available online 16 August 2016
      Source:Computational Biology and Chemistry
      Author(s): Khalid Raza, Mansaf Alam
      One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model.
      Graphical abstract image

      PubDate: 2016-08-18T18:52:00Z
  • Structural insight into the glucokinase-ligands interactions. Molecular
           docking study
    • Abstract: Publication date: Available online 8 August 2016
      Source:Computational Biology and Chemistry
      Author(s): Elena Ermakova
      Glucokinase (GK) plays a key role in the regulation of hepatic glucose metabolism. Inactivation of GK is associated with diabetes, and an increase of its activity is linked to hypoglycemia. Possibility to regulate the GK activity using small chemical compounds as allosteric activators induces the scientific interest to the study of the activation mechanism and to the development of new allosteric glucokinase activators. Interaction of glucokinase with ligands is the first step of the complicated mechanism of regulation of the GK functioning. In this paper, we study the interaction of GK with native (glucose) and synthetic (allosteric activators) ligands using molecular docking method. Calculations demonstrate the ability of molecular docking programs to accurately reproduce crystallized ligand poses and conformations and to calculate a free energy of binding with satisfactory accuracy. Correlation between the free energy of binding and the bioactivity of activators is discussed. These results provide a new insight into protein–ligand interactions and can be used for the engineering of new activators.
      Graphical abstract image

      PubDate: 2016-08-11T12:45:08Z
  • Steric exclusion and constraint satisfaction in multi-scale coarse-grained
    • Abstract: Publication date: Available online 6 August 2016
      Source:Computational Biology and Chemistry
      Author(s): William R. Taylor
      An algorithm is described for the interaction of a hierarchy of objects that seeks to circumvent a fundamental problem in coarse-grained modelling which is the loss of fine detail when components become bundled together. A “currants-in-jelly” model is developed that provides a flexible approach in which the contribution of the soft high-level objects (jelly-like) are employed to protect the underlying atomic structure (currants), while still allowing them to interact. Idealised chains were used to establish the parameters to achieve this degree of interaction over a hierarchy spanning four levels and in a more realistic example, the distortion experienced by a protein domain structure during collision was measured and the parameters refined. This model of steric repulsion was then combined with sets of predicted distance constraints, derived from correlated mutation analysis. Firstly, an integral trans-membrane protein was modelled in which the packing of the seven helices was refined but without topological rearrangement. Secondly, an RNA structure was ‘folded’ under the predicted constraints, starting only from its 2-dimensional secondary structure prediction.
      Graphical abstract image Highlights

      PubDate: 2016-08-11T12:45:08Z
  • Protein–protein interface prediction based on hexagon structure
    • Abstract: Publication date: August 2016
      Source:Computational Biology and Chemistry, Volume 63
      Author(s): Fei Guo, Yijie Ding, Shuai Cheng Li, Chao Shen, Lusheng Wang
      Studies on protein–protein interaction are important in proteome research. How to build more effective models based on sequence information, structure information and physicochemical characteristics, is the key technology in protein–protein interface prediction. In this paper, we study the protein–protein interface prediction problem. We propose a novel method for identifying residues on interfaces from an input protein with both sequence and 3D structure information, based on hexagon structure similarity. Experiments show that our method achieves better results than some state-of-the-art methods for identifying protein–protein interface. Comparing to existing methods, our approach improves F-measure value by at least 0.03. On a common dataset consisting of 41 complexes, our method has overall precision and recall values of 63% and 57%. On Benchmark v4.0, our method has overall precision and recall values of 55% and 56%. On CAPRI targets, our method has overall precision and recall values of 52% and 55%.

      PubDate: 2016-08-07T02:45:57Z
  • Characterizing mutation–expression network relationships in multiple
    • Abstract: Publication date: August 2016
      Source:Computational Biology and Chemistry, Volume 63
      Author(s): Shila Ghazanfar, Jean Yee Hwa Yang
      Background Data made available through large cancer consortia like The Cancer Genome Atlas make for a rich source of information to be studied across and between cancers. In recent years, network approaches have been applied to such data in uncovering the complex interrelationships between mutational and expression profiles, but lack direct testing for expression changes via mutation. In this pan-cancer study we analyze mutation and gene expression information in an integrative manner by considering the networks generated by testing for differences in expression in direct association with specific mutations. We relate our findings among the 19 cancers examined to identify commonalities and differences as well as their characteristics. Results Using somatic mutation and gene expression information across 19 cancers, we generated mutation–expression networks per cancer. On evaluation we found that our generated networks were significantly enriched for known cancer-related genes, such as skin cutaneous melanoma (p <0.01 using Network of Cancer Genes 4.0). Our framework identified that while different cancers contained commonly mutated genes, there was little concordance between associated gene expression changes among cancers. Comparison between cancers showed a greater overlap of network nodes for cancers with higher overall non-silent mutation load, compared to those with a lower overall non-silent mutation load. Conclusions This study offers a framework that explores network information through co-analysis of somatic mutations and gene expression profiles. Our pan-cancer application of this approach suggests that while mutations are frequently common among cancer types, the impact they have on the surrounding networks via gene expression changes varies. Despite this finding, there are some cancers for which mutation-associated network behaviour appears to be similar: suggesting a potential framework for uncovering related cancers for which similar therapeutic strategies may be applicable. Our framework for understanding relationships among cancers has been integrated into an interactive R Shiny application, PAn Cancer Mutation Expression Networks (PACMEN), containing dynamic and static network visualization of the mutation–expression networks. PACMEN also features tools for further examination of network topology characteristics among cancers.

      PubDate: 2016-08-07T02:45:57Z
  • MOCCS: Clarifying DNA-binding motif ambiguity using ChIP-Seq data
    • Abstract: Publication date: August 2016
      Source:Computational Biology and Chemistry, Volume 63
      Author(s): Haruka Ozaki, Wataru Iwasaki
      Background As a key mechanism of gene regulation, transcription factors (TFs) bind to DNA by recognizing specific short sequence patterns that are called DNA-binding motifs. A single TF can accept ambiguity within its DNA-binding motifs, which comprise both canonical (typical) and non-canonical motifs. Clarification of such DNA-binding motif ambiguity is crucial for revealing gene regulatory networks and evaluating mutations in cis-regulatory elements. Although chromatin immunoprecipitation sequencing (ChIP-seq) now provides abundant data on the genomic sequences to which a given TF binds, existing motif discovery methods are unable to directly answer whether a given TF can bind to a specific DNA-binding motif. Results Here, we report a method for clarifying the DNA-binding motif ambiguity, MOCCS. Given ChIP-Seq data of any TF, MOCCS comprehensively analyzes and describes every k-mer to which that TF binds. Analysis of simulated datasets revealed that MOCCS is applicable to various ChIP-Seq datasets, requiring only a few minutes per dataset. Application to the ENCODE ChIP-Seq datasets proved that MOCCS directly evaluates whether a given TF binds to each DNA-binding motif, even if known position weight matrix models do not provide sufficient information on DNA-binding motif ambiguity. Furthermore, users are not required to provide numerous parameters or background genomic sequence models that are typically unavailable. MOCCS is implemented in Perl and R and is freely available via Conclusions By complementing existing motif-discovery software, MOCCS will contribute to the basic understanding of how the genome controls diverse cellular processes via DNA–protein interactions.

      PubDate: 2016-08-07T02:45:57Z
  • IFC Editorial Board
    • Abstract: Publication date: August 2016
      Source:Computational Biology and Chemistry, Volume 63

      PubDate: 2016-08-07T02:45:57Z
  • Gene expression variability in mammalian embryonic stem cells using single
           cell RNA-seq data
    • Abstract: Publication date: August 2016
      Source:Computational Biology and Chemistry, Volume 63
      Author(s): Anna Mantsoki, Guillaume Devailly, Anagha Joshi
      Background Gene expression heterogeneity contributes to development as well as disease progression. Due to technological limitations, most studies to date have focused on differences in mean expression across experimental conditions, rather than differences in gene expression variance. The advent of single cell RNA sequencing has now made it feasible to study gene expression heterogeneity and to characterise genes based on their coefficient of variation. Methods We collected single cell gene expression profiles for 32 human and 39 mouse embryonic stem cells and studied correlation between diverse characteristics such as network connectivity and coefficient of variation (CV) across single cells. We further systematically characterised properties unique to High CV genes. Results Highly expressed genes tended to have a low CV and were enriched for cell cycle genes. In contrast, High CV genes were co-expressed with other High CV genes, were enriched for bivalent (H3K4me3 and H3K27me3) marked promoters and showed enrichment for response to DNA damage and DNA repair. Conclusions Taken together, this analysis demonstrates the divergent characteristics of genes based on their CV. High CV genes tend to form co-expression clusters and they explain bivalency at least in part.

      PubDate: 2016-08-07T02:45:57Z
  • SUMONA: A supervised method for optimizing network alignment
    • Abstract: Publication date: August 2016
      Source:Computational Biology and Chemistry, Volume 63
      Author(s): Erhun Giray Tuncay, Tolga Can
      This study focuses on improving the multi-objective memetic algorithm for protein–protein interaction (PPI) network alignment, Optimizing Network Aligner – OptNetAlign, via integration with other existing network alignment methods such as SPINAL, NETAL and HubAlign. The output of this algorithm is an elite set of aligned networks all of which are optimal with respect to multiple user-defined criteria. However, OptNetAlign is an unsupervised genetic algorithm that initiates its search with completely random solutions and it requires substantial running times to generate an elite set of solutions that have high scores with respect to the given criteria. In order to improve running time, the search space of the algorithm can be narrowed down by focusing on remarkably qualified alignments and trying to optimize the most desired criteria on a more limited set of solutions. The method presented in this study improves OptNetAlign in a supervised fashion by utilizing the alignment results of different network alignment algorithms with varying parameters that depend upon user preferences. Therefore, the user can prioritize certain objectives upon others and achieve better running time performance while optimizing the secondary objectives.
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      PubDate: 2016-08-07T02:45:57Z
  • Multi-instance multi-label distance metric learning for genome-wide
           protein function prediction
    • Abstract: Publication date: August 2016
      Source:Computational Biology and Chemistry, Volume 63
      Author(s): Yonghui Xu, Huaqing Min, Hengjie Song, Qingyao Wu
      Multi-instance multi-label (MIML) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with not only multiple instances but also multiple class labels. To find an appropriate MIML learning method for genome-wide protein function prediction, many studies in the literature attempted to optimize objective functions in which dissimilarity between instances is measured using the Euclidean distance. But in many real applications, Euclidean distance may be unable to capture the intrinsic similarity/dissimilarity in feature space and label space. Unlike other previous approaches, in this paper, we propose to learn a multi-instance multi-label distance metric learning framework (MIMLDML) for genome-wide protein function prediction. Specifically, we learn a Mahalanobis distance to preserve and utilize the intrinsic geometric information of both feature space and label space for MIML learning. In addition, we try to deal with the sparsely labeled data by giving weight to the labeled data. Extensive experiments on seven real-world organisms covering the biological three-domain system (i.e., archaea, bacteria, and eukaryote; Woese et al., 1990) show that the MIMLDML algorithm is superior to most state-of-the-art MIML learning algorithms.

      PubDate: 2016-08-07T02:45:57Z
  • Protein inference: A protein quantification perspective
    • Abstract: Publication date: August 2016
      Source:Computational Biology and Chemistry, Volume 63
      Author(s): Zengyou He, Ting Huang, Xiaoqing Liu, Peijun Zhu, Ben Teng, Shengchun Deng
      In mass spectrometry-based shotgun proteomics, protein quantification and protein identification are two major computational problems. To quantify the protein abundance, a list of proteins must be firstly inferred from the raw data. Then the relative or absolute protein abundance is estimated with quantification methods, such as spectral counting. Until now, most researchers have been dealing with these two processes separately. In fact, the protein inference problem can be regarded as a special protein quantification problem in the sense that truly present proteins are those proteins whose abundance values are not zero. Some recent published papers have conceptually discussed this possibility. However, there is still a lack of rigorous experimental studies to test this hypothesis. In this paper, we investigate the feasibility of using protein quantification methods to solve the protein inference problem. Protein inference methods aim to determine whether each candidate protein is present in the sample or not. Protein quantification methods estimate the abundance value of each inferred protein. Naturally, the abundance value of an absent protein should be zero. Thus, we argue that the protein inference problem can be viewed as a special protein quantification problem in which one protein is considered to be present if its abundance is not zero. Based on this idea, our paper tries to use three simple protein quantification methods to solve the protein inference problem effectively. The experimental results on six data sets show that these three methods are competitive with previous protein inference algorithms. This demonstrates that it is plausible to model the protein inference problem as a special protein quantification task, which opens the door of devising more effective protein inference algorithms from a quantification perspective. The source codes of our methods are available at:
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      PubDate: 2016-08-07T02:45:57Z
  • Copy number variants calling for single cell sequencing data by
           multi-constrained optimization
    • Abstract: Publication date: August 2016
      Source:Computational Biology and Chemistry, Volume 63
      Author(s): Bo Xu, Hongmin Cai, Changsheng Zhang, Xi Yang, Guoqiang Han
      Variations in DNA copy number carry important information on genome evolution and regulation of DNA replication in cancer cells. The rapid development of single-cell sequencing technology allows one to explore gene expression heterogeneity among single-cells, thus providing important cancer cell evolution information. Single-cell DNA/RNA sequencing data usually have low genome coverage, which requires an extra step of amplification to accumulate enough samples. However, such amplification will introduce large bias and makes bioinformatics analysis challenging. Accurately modeling the distribution of sequencing data and effectively suppressing the bias influence is the key to success variations analysis. Recent advances demonstrate the technical noises by amplification are more likely to follow negative binomial distribution, a special case of Poisson distribution. Thus, we tackle the problem CNV detection by formulating it into a quadratic optimization problem involving two constraints, in which the underling signals are corrupted by Poisson distributed noises. By imposing the constraints of sparsity and smoothness, the reconstructed read depth signals from single-cell sequencing data are anticipated to fit the CNVs patterns more accurately. An efficient numerical solution based on the classical alternating direction minimization method (ADMM) is tailored to solve the proposed model. We demonstrate the advantages of the proposed method using both synthetic and empirical single-cell sequencing data. Our experimental results demonstrate that the proposed method achieves excellent performance and high promise of success with single-cell sequencing data.

      PubDate: 2016-08-07T02:45:57Z
  • Title page
    • Abstract: Publication date: August 2016
      Source:Computational Biology and Chemistry, Volume 63

      PubDate: 2016-08-07T02:45:57Z
  • The modularity and dynamicity of miRNA–mRNA interactions in high-grade
           serous ovarian carcinomas and the prognostic implication
    • Abstract: Publication date: August 2016
      Source:Computational Biology and Chemistry, Volume 63
      Author(s): Wensheng Zhang, Andrea Edwards, Wei Fan, Erik K. Flemington, Kun Zhang
      Ovarian carcinoma is the fifth-leading cause of cancer death among women in the United States. Major reasons for this persistent mortality include the poor understanding of the underlying biology and a lack of reliable biomarkers. Previous studies have shown that aberrantly expressed MicroRNAs (miRNAs) are involved in carcinogenesis and tumor progression by post-transcriptionally regulating gene expression. However, the interference of miRNAs in tumorigenesis is quite complicated and far from being fully understood. In this work, by an integrative analysis of mRNA expression, miRNA expression and clinical data published by The Cancer Genome Atlas (TCGA), we studied the modularity and dynamicity of miRNA–mRNA interactions and the prognostic implications in high-grade serous ovarian carcinomas. With the top transcriptional correlations (Bonferroni-adjusted p-value<0.01) as inputs, we identified five miRNA–mRNA module pairs (MPs), each of which included one positive-connection (correlation) module and one negative-connection (correlation) module. The number of miRNAs or mRNAs in each module varied from 3 to 7 or from 2 to 873. Among the four major negative-connection modules, three fit well with the widely accepted miRNA-mediated post-transcriptional regulation theory. These modules were enriched with the genes relevant to cell cycle and immune response. Moreover, we proposed two novel algorithms to reveal the group or sample specific dynamic regulations between these two RNA classes. The obtained miRNA–mRNA dynamic network contains 3350 interactions captured across different cancer progression stages or tumor grades. We found that those dynamic interactions tended to concentrate on a few miRNAs (e.g. miRNA-936), and were more likely present on the miRNA–mRNA pairs outside the discovered modules. In addition, we also pinpointed a robust prognostic signature consisting of 56 modular protein-coding genes, whose co-expression patterns were predictive for the survival time of ovarian cancer patients in multiple independent cohorts.
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      PubDate: 2016-08-07T02:45:57Z
  • Assembly of ligands interaction models for glutathione-S-transferases from
           Plasmodium falciparum, human and mouse using enzyme kinetics and molecular
    • Abstract: Publication date: Available online 25 July 2016
      Source:Computational Biology and Chemistry
      Author(s): Mohammed Nooraldeen Al-Qattan, Mohd Nizam Mordi, Sharif Mahsofi Mansor
      Background Glutathione-s-transferases (GSTs) are enzymes that principally catalyze the conjugation of electrophilic compounds to the endogenous nucleophilic glutathione substrate, besides, they have other non-catalytic functions. The Plasmodium falciparum genome encodes a single isoform of GST (PfGST) which is involved in buffering the toxic heme, thus considered a potential anti-malarial target. In mammals several classes of GSTs are available, each of various isoforms. The human (human GST Pi-1 or hGSTP1) and mouse (murine GST Mu-1 or mGSTM1) GST isoforms control cellular apoptosis by interaction with signaling proteins, thus considered as potential anti-cancer targets. In the course of GSTs inhibitors development, the models of ligands interactions with GSTs are used to guide rational molecular modification. In the absence of X-ray crystallographic data, enzyme kinetics and molecular docking experiments can aid in addressing ligands binding modes to the enzymes. Methods Kinetic studies were used to investigate the interactions between the three GSTs and each of glutathione, 1-chloro-2,4-dinitrobenzene, cibacron blue, ethacrynic acid, S-hexyl glutathione, hemin and protoporphyrin IX. Since hemin displacement is intended for PfGST inhibitors, the interactions between hemin and other ligands at PfGST binding sites were studied kinetically. Computationally determined binding modes and energies were interlinked with the kinetic results to resolve enzymes-ligands interaction models at atomic level. Results The results showed that hemin and cibacron blue have different binding modes in the three GSTs. Hemin has two binding sites (A and B) with two binding modes at site-A depending on presence of GSH. None of the ligands were able to compete hemin binding to PfGST except ethacrynic acid. Besides bind differently in GSTs, the isolated anthraquinone moiety of cibacron blue is not maintaining sufficient interactions with GSTs to be used as a lead. Similarly, the ethacrynic acid uses water bridges to mediate interactions with GSTs and at least the conjugated form of EA is the true hemin inhibitor, thus EA may not be a suitable lead. Conclusions Glutathione analogues with bulky substitution at thiol of cysteine moiety or at γ-amino group of γ-glutamine moiety may be the most suitable to provide GST inhibitors with hemin competition.

      PubDate: 2016-07-28T18:33:42Z
  • Evolution of camel CYP2E1 and its associated power of binding toxic
           industrial chemicals and drugs
    • Abstract: Publication date: Available online 26 July 2016
      Source:Computational Biology and Chemistry
      Author(s): Mahmoud Kandeel, Abdullah Altaher, Yukio Kitade, Magdi Abdelaziz, Mohamed Alnazawi, Kamal Elshazli
      Camels are raised in harsh desert environment for hundreds of years ago. By modernization of live and the growing industrial revolution in camels rearing areas, camels are exposed to considerable amount of chemicals, industrial waste, environmental pollutions and drugs. Furthermore, camels have unique gene evolution of some genes to withstand living in harsh environments. In this work, the camel cytochrome P450 2E1 (CYP2E1) is compromised to detect its evolution rate and its power to bind with various chemicals, protoxins, procarcinogens, industrial toxins and drugs. In comparison with human CYP2E1, camel CYP2E1 more efficiently binds to small toxins as aniline, benzene, catechol, amides, butadiene, toluene and acrylamide. Larger compounds were more preferentially bound to the human CYP2E1 in comparison with camel CYP2E1. The binding of inhalant anesthetics was almost similar in both camel and human CYP2E1 coinciding with similar anesthetic effect as well as toxicity profiles. Furthermore, evolutionary analysis indicated the high evolution rate of camel CYP2E1 in comparison with human, farm and companion animals. The evolution rate of camel CYP2E1 was among the highest evolution rate in a subset of 57 different organisms. These results indicate rapid evolution and potent toxin binding power of camel CYP2E1.
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      PubDate: 2016-07-28T18:33:42Z
  • Functional and Structural Insights into Novel DREB1A Transcription Factors
           in Common Wheat (Triticum aestivum L.): A Molecular Modeling Approach
    • Abstract: Publication date: Available online 19 July 2016
      Source:Computational Biology and Chemistry
      Author(s): Anuj Kumar, Sanjay Kumar, Upendra Kumar, Prashanth Suravajhala, M.N.V. Prasad Gajula
      Triticum aestivum L. known as common wheat is one of the most important cereal crops feeding a large and growing population. Various environmental stress factors including drought, high salinity and heat etc. adversely affect wheat production in a significant manner. Dehydration-responsive element-binding (DREB1A) factors, a class of transcription factors (TF) play an important role in combating drought stress. It is known that DREB1A specifically interacts with the dehydration responsive elements (DRE/CRT) inducing expression of genes involved in environmental stress tolerance in plants. Despite its critical interplay in plants, the structural and functional aspects of DREB1A TF in wheat remain unresolved. Previous studies showed that wheat DREBs (DREB1 and DREB2) were isolated using various methods including yeast two-hybrid screens but no extensive structural models were reported. In this study, we made an extensive in silico study to gain insight into DREB1A TF and reported the location of novel DREB1A in wheat chromosomes. We inferred the three-dimensional structural model of DREB1A using homology modelling and further evaluated them using molecular dynamics(MD) simulations yielding refined modelled structures. Our biochemical function predictions suggested that the wheat DREB1A orthologs have similar biochemical functions and pathways to that of AtDREB1A. In conclusion, the current study presents a structural perspective of wheat DREB1A and helps in understanding the molecular basis for the mechanism of DREB1A in response to environmental stress.

      PubDate: 2016-07-24T18:28:20Z
  • In-silico structural analysis of E509K mutation in LARGE and T192M
           mutation in Alpha Dystroglycan in the inhibition of glycosylation of Alpha
           Dystroglycan by LARGE
    • Abstract: Publication date: Available online 16 July 2016
      Source:Computational Biology and Chemistry
      Author(s): Simanti Bhattacharya, Amit Das, Angshuman Bagchi
      Impaired glycosylation of cellular receptor Alpha Dystroglycan (α-DG) leads to dystroglycanopathy. Glycoprotein α-DG is the receptor protein in the Dystrophin Associated Protein Complex (DAPC), a macromolecular gathering on muscle cell membrane to form a bridge between extracellular matrix (ECM) and cellular actin cytoskeleton. Proper glycosylation of α-DG is mediated by the glycosylating enzyme LARGE. Mutations either in α-DG or in LARGE lead to improper glycosylations of α-DG thereby hampering the formation of final Laminin binding form α-DG resulting in dystroglycanopathy. In our current work, we explored the structural changes associated with the presence of mutations in α-DG as well as in the enzyme LARGE. We further extended our research to understand the effect of the mutations onto protein-enzyme interactions. Moreover, since LARGE transfers the sugar moiety (glucuronic acid; GlcA) onto α-DG, we tried to analyze what effect the mutation in LARGE confers on this enzyme ligand interaction. This work for the first time addressed the molecular changes occurring in the structures α-DG, LARGE and their interactions and shed lights on the as yet poorly understood mechanism behind the dystroglycanopathy onset.
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      PubDate: 2016-07-19T18:23:15Z
  • An integrative approach predicted co-expression sub-networks regulating
           properties of stem cells and differentiation
    • Abstract: Publication date: Available online 18 July 2016
      Source:Computational Biology and Chemistry
      Author(s): Mousumi Sahu, Bibekanand Mallick
      The differentiation of human Embryonic Stem Cells (hESCs) is accompanied by the formation of different intermediary cells, gradually losing its stemness and acquiring differentiation. The precise mechanisms underlying hESCs integrity and its differentiation into fibroblast (Fib) are still elusive. Here, we aimed to assess important genes and co-expression sub-networks responsible for stemness, early differentiation of hESCs into embryoid bodies (EBs) and its lineage specification into Fibs. To achieve this, we compared transcriptional profiles of hESCs-EBs and EBs-Fibs and obtained differentially expressed genes (DEGs) exclusive to hESCs-EBs (early differentiation), EBs-Fibs (late differentiation) and common DEGs in hESCs-EBs and EBs-Fibs. Then, we performed gene set enrichment analysis (GSEA) followed by overrepresentation study and identified key genes for each gene category. The regulations of these genes were studied by integrating ChIP-Seq data of core transcription factors (TFs) and histone methylation marks in hESCs. Finally, we identified co-expression sub-networks from key genes of each gene category using k-clique sub-network extraction method. Our study predicted seven genes edicting core stemness properties forming a co-expression network. From the pathway analysis of sub-networks of hESCs-EBs, we hypothesize that FGF2 is contributing to pluripotent transcription network of hESCs in association with DNMT3B and JARID2 thereby facilitating cell proliferation. On the contrary, FGF2 is found to promote cell migration in Fibs along with DDR2, CAV1, DAB2, and PARVA. Moreover, our study identified three k-clique sub-networks regulating TGF-β signaling pathway thereby promoting EBs to Fibs differentiation by: (i) modulating extracellular matrix involving ITGB1, TGFB1I1 and GBP1, (ii) regulating cell cycle remodeling involving CDKN1A, JUNB and DUSP1 and (iii) helping in epithelial to mesenchymal transition (EMT) involving THBS1, INHBA and LOX. This study put forward the unexplored genes and co-expression sub-networks regulating stemness and different stages of differentiation of hESCs which will undoubtedly add to the comprehensive understanding of hESCs biology.
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      PubDate: 2016-07-19T18:23:15Z
  • Perceptron Ensemble of Graph-based Positive-Unlabeled Learning for Disease
           Gene Identification
    • Abstract: Publication date: Available online 12 July 2016
      Source:Computational Biology and Chemistry
      Author(s): Gholam-Hossein Jowkar, Eghbal G. Mansoori
      Identification of disease genes, using computational methods, is an important issue in biomedical and bioinformatics research. According to observations that diseases with the same or similar phenotype have the same biological characteristics, researchers have tried to identify genes by using machine learning tools. In recent attempts, some semi-supervised learning methods, called positive-unlabeled learning, is used for disease gene identification. In this paper, we present a Perceptron ensemble of graph-based positive-unlabeled learning (PEGPUL) on three types of biological attributes: gene ontologies, protein domains and protein-protein interaction networks. In our method, a reliable set of positive and negative genes are extracted using co-training schema. Then, the similarity graph of genes is built using metric learning by concentrating on multi-rank-walk method to perform inference from labeled genes. At last, a Perceptron ensemble is learned from three weighted classifiers: multilevel support vector machine, k-nearest neighbor and decision tree. The main contributions of this paper are: (i) incorporating the statistical properties of gene data through choosing proper metrics, (ii) statistical evaluation of biological features, and (iii) noise robustness characteristic of PEGPUL via using multilevel schema. In order to assess PEGPUL, we have applied it on 12950 disease genes with 949 positive genes from six class of diseases and 12001 unlabeled genes. Compared with some popular disease gene identification methods, the experimental results show that PEGPUL has reasonable performance.
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      PubDate: 2016-07-15T18:13:11Z
  • The binding mode of picrotoxinin in GABAA-ρ receptors: Insight into the
           subunit’s selectivity in the transmembrane domain
    • Abstract: Publication date: Available online 7 July 2016
      Source:Computational Biology and Chemistry
      Author(s): Moawiah M. Naffaa, Abdul Samad
      The channel blocker picrotoxinin has been studied with GABAA-ρ1 and GABAA-ρ2 homology models based on the GluCl crystal structure. Picrotoxinin is tenfold more potent for GABAA-ρ2 than for GABAA-ρ1 homomeric channels. This intra-subunit selectivity arises from the unconserved residues at the 2′ sites, which are the essential molecular basis for both the binding and potency of picrotoxinin. The serine residues at the 2′ positions of the ρ2 channel are predicted to form multiple hydrogen bonds and hydrophobic interactions with picrotoxinin, whereas the proline residues in the 2′ positions of ρ1 channels are predicted to form only hydrophobic contacts with picrotoxinin. However, although the studied ρ1 P2′G, A, and V models form no hydrogen bonds with picrotoxinin, they may participate in several hydrophobic interactions, and the ligand may have distinctive binding modes with GABAA-ρ mutant channels. Picrotoxinin has a lower Emodel value with ρ2 than ρ1 homomeric models (−47Kcal/mol and −36Kcal/mol, respectively), suggesting that picrotoxin blocks the pores of the ρ2 channels more effectively.
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      PubDate: 2016-07-11T17:59:25Z
  • Synthesis, spectroscopic and computational studies of
           2-(thiophen-2-yl)-2,3-dihydro-1H-perimidine: An enzymes inhibition study
    • Abstract: Publication date: Available online 24 June 2016
      Source:Computational Biology and Chemistry
      Author(s): Mahboob Alam, Dong-Ung Lee
      The biologically relevant molecule; 2-(thiophen-2-yl)-2,3-dihydro-1H-perimidine was synthesized and characterized by FT-IR, UV, 1H and 13C NMR, MS, CHN microanalysis, X-ray crystallography as well as by theoretical, B3LYP/6–311++G(d,p), calculations. The vibrational bands appearing in the FT-IR were assigned with great accuracy using animated modes. Molecular properties like HOMO–LUMO analysis, chemical reactivity descriptors, MEP mapping, dipole moment and natural charges have been presented at the same level of theory. The theoretical results are found in good correlation with the experimental data obtained from the various spectral techniques. Moreover, the Hirshfeld analysis was performed to explore the secondary interactions and associated 2D fingerprint plots. Perimidine molecule displayed promising inhibitory activity against acetylcholinesterase (AChE) as compared to the reference drug, tacrine. Molecular docking was carried out to ascertain the synthesized molecule into the X-ray crystal structures of acetylcholinesterase at the active site to find out the probable binding mode. The results of molecular docking admitted that perimidine may reveal enzyme inhibitor activity.
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      PubDate: 2016-07-07T17:36:17Z
  • Mechanistic insights into mode of action of rice allene oxide synthase on
           hydroxyperoxides: An intermediate step in herbivory-induced jasmonate
    • Abstract: Publication date: Available online 2 July 2016
      Source:Computational Biology and Chemistry
      Author(s): Chetna Tyagi, Archana Singh, Indrakant Kumar Singh
      Various types of oxygenated fatty acids termed ‘oxylipins’ are involved in plant response to herbivory. Oxylipins like jasmonic acid (JA) and green leafy volatiles (GLVs) are formed by the action of enzymes like allene oxide synthase (AOS) and hydroxyperoxide lyase (HPL) respectively. In this study, we focus on AOS of Oryza sativa sb. Japonica, that interact with 9- and 13- hydroxyperoxides to produce intermediates of jasmonate pathway and compare it with rice HPL that yields GLVs. We attempt to elucidate the interaction pattern by computational docking protocols keeping the Arabidopsis AOS system as the reference model system. Both 9-hydroxyperoxide and 13-hydroxyperoxide fit into the active site of AOS completely with Phe347, Phe92, Ile463, Val345, and Asn278 being the common interacting residues. Phe347 and Phe92 were mutated with Leucine and docked again with the hydroxyperoxides. The Phe347→Leu347 mutant showed a different mode of action than AOS-hydroxyperoxide complex with Trp413 in direct bonding with the OOH group of 9-hydroxyperoxide. The loss of Lys88-OOH interaction in 13-hydroxyperoxide and loss-of-interaction of Leu347 indicated the importance of Phe347 residue in hydroxyperoxide catalysis. The second mutant Phe92→Leu92 also shows a very different interaction pattern with 13-hydroxyperoxide but not with 9-hydroxyperoxide.Therefore, it can be concluded that Phe347 is more crucial for AOS functionality than Phe92. The aromatic ring of a Phenylalanine residue is important for catalysis and its mutation affects the binding of the two ligands. Another important residue is Asn278 which is an important part of the AOS catalytic site for maintaining stability and can be compared with the Arabidopsis AOS residue Asn321. Lastly, the interaction of HPL with these two derivatives involves Leu363 residue instead of Phe347 and thus, validating the importance of Phe→Leu substitution to be the reason of different modes of action that result in completely different products from same substrates.
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      PubDate: 2016-07-07T17:36:17Z
  • Revisiting the Structural Basis and Energetic Landscape of Susceptibility
           Difference between HLA Isotypes to Allergic Rhinitis
    • Abstract: Publication date: Available online 6 July 2016
      Source:Computational Biology and Chemistry
      Author(s): Xin-Li Mao, Feng Zhu, Zhao-Hu Pan, Guo-Min Wu, Hong-Yuan Zhu
      The human leukocyte antigen class II (HLA II) molecules are implicated in the immunopathogenesis of allergic rhinitis (AR). The HLA II contains three allelic isotypes HLA-DR, −DQ, and −QP that exhibit considerably different susceptibility to AR. Here, we investigated the structural basis and energetic landscape of the susceptibility difference between the three HLA II isotypes to AR by combining computational analysis and experimental assay. Multiple sequence alignment revealed a low conservation among the three subtypes with sequence identity of ∼10% between them, suggesting that the peptide repertoires presented by HLA-DR, −DP and −DQ are not overlapped to each other, and they may be involved in different immune functions and dysfunctions. Structural analysis imparted that the antigenic peptides are rooted on the peptide-binding groove of HLA molecules and hold in a PPII-like helical conformation. Subsequently, the interaction behavior of 17 AR allergen-derived peptides with HLA-DR, −DP and −DQ was investigated using a statistics-based quantitative structure-activity relationship (QSAR) predictor. It was found a significant difference between the binding capabilities of these antigenic peptides to HLA-DR and to HLA-DP/-DQ; the former showed a generally higher affinity than the latter with p-value of 0.02 obtained from 2-tailed student's t-test. The computational findings were then confirmed by HLA II–peptide stability assay, which demonstrated that the AR allergen-derived peptides have a high in vitro selectivity for HLA-DR over HLA-DP/-DQ. Thus, the HLA-DR isotype, rather than HLA-DP and −DQ, is expected to associate with the pathological process of AR.
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      PubDate: 2016-07-07T17:36:17Z
  • Alphavirus protease inhibitors from natural sources: A homology modeling
           and molecular docking investigation
    • Abstract: Publication date: October 2016
      Source:Computational Biology and Chemistry, Volume 64
      Author(s): Kendall G. Byler, Jasmine T. Collins, Ifedayo Victor Ogungbe, William N. Setzer
      Alphaviruses such as Chikungunya virus (CHIKV), O’Nyong–Nyong virus (ONNV), Ross River virus (RRV), Eastern equine encephalitis virus (EEEV), Venezuelan equine encephalitis virus (VEEV), and Western equine encephalitis virus (WEEV), are mosquito-transmitted viruses that can cause fevers, rash, and rheumatic diseases (CHIKV, ONNV, RRV) or potentially fatal encephalitis (EEEV, VEEV, WEEV) in humans. These diseases are considered neglected tropical diseases for which there are no current antiviral therapies or vaccines available. The alphavirus non-structural protein 2 (nsP2) contains a papain-like protease, which is considered to be a promising target for antiviral drug discovery. In this work, molecular docking analyses have been carried out on a library of 2174 plant-derived natural products (290 alkaloids, 664 terpenoids, 1060 polyphenolics, and 160 miscellaneous phytochemicals) with the nsP2 proteases of CHIKV, ONNV, RRV, EEEV, VEEV, WEEV, as well as Aura virus (AURV), Barmah Forest Virus (BFV), Semliki Forest virus (SFV), and Sindbis virus (SINV) in order to identity structural scaffolds for inhibitor design or discovery. Of the 2174 phytochemicals examined, a total of 127 showed promising docking affinities and poses to one or more of the nsP2 proteases, and this knowledge can be used to guide experimental investigation of potential inhibitors.
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      PubDate: 2016-07-07T17:36:17Z
  • Identification of miRNAs and their targets involved in the secondary
           metabolic pathways of Mentha spp.
    • Abstract: Publication date: Available online 17 June 2016
      Source:Computational Biology and Chemistry
      Author(s): Noopur Singh, Swati Srivastava, Ajit K. Shasany, Ashok Sharma
      The endogenous, small and non-coding functional microRNAs govern the regulatory system of gene expression and control the growth and development of the plants. Mentha spp. are well known herbs for its flavor, fragrance and medicinal properties. In the present study, we used a computational approach to identify miRNAs and their targets involved in different secondary metabolic pathways of Mentha spp. Additionally, phylogenetic and conservation analysis were also done for the predicted miRNAs. Eleven miRNAs families were identified from Mentha spp., out of which five miRNA families were reported for the first time from Lamiaceae. Overall, 130 distinct target transcripts were predicted for eight miRNAs families. All the predicted targets regulated by predicted miRNAs control the reproduction, signaling, stimulus response, developmental and different metabolic process. miRNA mediated gene regulatory network was also constructed on the basis of hybridized minimum free energy of identified miRNAs and their targets. The study revealed that the gene regulatory system of essential oil biosynthesis may be governed by miR156, miR414 and miR5021 in mint family. Furthermore, three miRNA candidates (miR156, miR5021, and miR5015b) were observed to be involved in trichome development also. This is the first in-silico study describing miRNAs and their role in the regulation of secondary metabolic pathways in Mentha spp.
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      PubDate: 2016-06-17T18:10:05Z
  • A model for the clustered distribution of SNPs in the human genome
    • 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
  • Comprehensive structural analysis of the open and closed conformations of
           Theileria annulata enolase by molecular modelling and docking
    • Abstract: Publication date: Available online 9 June 2016
      Source:Computational Biology and Chemistry
      Author(s): Ozal Mutlu, Sinem Yakarsonmez, Emrah Sariyer, Ozkan Danis, Basak Yuce-Dursun, Murat Topuzogullari, Ekrem Akbulut, Dilek Turgut-Balik
      Theileria annulata is an apicomplexan parasite which is responsible for tropical theileriosis in cattle. Due to resistance of T. annulata against commonly used antitheilerial drug, new drug candidates should be identified urgently. Enolase might be a druggable protein candidate which has an important role in glycolysis, and could also be related to several cellular functions as a moonlight protein. In this study; we have described three-dimensional models of open and closed conformations of T. annulata enolase by homology modeling method for the first time with the comprehensive domain, active site and docking analyses. Our results show that the enolase has similar folding patterns within enolase superfamily with conserved catalytic loops and active site residues. We have described specific insertions, possible plasminogen binding sites, electrostatic potential surfaces and positively charged pockets as druggable regions in T. annulata enolase.
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      PubDate: 2016-06-13T10:05:05Z
    • Abstract: Publication date: Available online 8 June 2016
      Source:Computational Biology and Chemistry
      Author(s): Arijit Pal, Anusri Tripathi
      Bacterial Oxacillinases (OXAs), genetically being extremely diverse and highly versatile in hydrolyzing antibiotics of different classes, holds utmost significant clinical importance. Hence, to analyze functional evolution of this enzyme, plausible changes in drug profile, affinity and binding stability of different subclasses of OXA with their preferred drugs, viz. penicillin, ceftazidime, imipenem/meropenem were investigated. Maximum-Likelihood dendrogram was constructed and based on tree topology, the least and most divergent variants of each clade were selected. Pocket characterization, enzyme structural stability and mutational effect were analyzed in silico. Modes of interaction of selected OXA variants with respective antibiotics were analyzed by Autodock4.0 and LIGPLOT. Comparative mobility profiling and subsequent ΔG ° and Km calculations of representative OXA variants revealed that after RSBL evolution, perhaps, two competitive strategies evolved among the OXA variants. Either loops flanking helix5 gets stabilized or it becomes more flexible. Therefore, while OXA variants (e.g. OXA-2, OXA-32, OXA-23, OXA-133, OXA-24, OXA-25, OXA-51 and OXA-75) with highly stabilized loops flanking helix5 exhibited improved binding stability and affinity towards carbapenems, especially meropenem, OXA variants (e.g. OXA-10, OXA-251, OXA-48 and OXA-247) possessing highly flexibile loops flanking helix5 revealed their catalytic proficiency towards ceftazidime. Moreover, LIGPLOT and PROMALS3D jointly identified ten consensuses/conserved residues, viz. P68, A69, F72, K73, W105, V120, W164, L169, K216 and G218 to be critical for drug hydrolysis. Hence, novel inhibitors could be designed to target these sites.
      Graphical abstract image

      PubDate: 2016-06-13T10:05:05Z
  • Systematic Profiling of Chemotherapeutic Drug Response to EGFR Gatekeeper
           Mutation in Non-small Cell Lung Cancer
    • Abstract: Publication date: Available online 4 June 2016
      Source:Computational Biology and Chemistry
      Author(s): Jun Yao, Xiaojuan Zhao, Xi Ding
      The epidermal growth factor receptor (EGFR) targeted therapy has been established as a routine strategy for treating non-small cell lung cancer (NSCLC). However, the gatekeeper mutation T790M in EGFR active site can confer generic resistance to tyrosine kinase inhibitors (TKIs), largely limiting the clinical applications of chemotherapeutic drugs in NSCLC. Here, a combined method of computational analysis and growth inhibition assay was described to systematically investigate the molecular response profile of wild-type–sparing and mutant-resistant inhibitors to the EGFR T790M mutation. The profile is highly consistent with previous clinical observations; three first-line chemotherapeutic drugs Gefitinib, Erlotinib and Lapatinib are established with acquired resistance upon the mutation. In addition, it was found that the alkaloid compound K252a, a Staurosporine analog isolated from Nocardiopisis sp., can selectively target the EGFR T790M mutant over wild-type kinase (23-fold selectivity), suggesting that the compound is good lead candidate for development of T790M mutant-selective inhibitors. Structural analysis revealed that the mutation-resulting Met790 residue does not induce steric hindrance to the EGFR T790M–K252a complex system, while a number of hydrophobic forces, van der Waals contacts and S⋯π interactions are observed between the aromatic rings of K252a and the sulfhydryl group of Met790, contributing considerable stabilization energy to the system.
      Graphical abstract image

      PubDate: 2016-06-07T09:58:56Z
  • A theoretical study on the electronic structures and equilibrium constants
           evaluation of Deferasirox iron complexes
    • 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.
      Graphical abstract image

      PubDate: 2016-06-02T09:52:28Z
  • Chemical Reaction Optimization for solving Shortest Common Supersequence
    • 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.
      Graphical abstract image Highlights

      PubDate: 2016-06-02T09:52:28Z
  • Increasing thermal stability and catalytic activity of glutamate
           decarboxylase in E. coli: An in silico study
    • 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.
      Graphical abstract image

      PubDate: 2016-06-02T09:52:28Z
  • Molecular dynamics and high throughput binding free energy calculation of
           anti-actin anticancer drugs—New insights for better design
    • 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.
      Graphical abstract image

      PubDate: 2016-05-28T09:34:06Z
  • Conformational Difference between Two Subunits in Flavin Mononucleotide
           Binding Protein Dimers from Desulfobivrio vugaris (MF): Molecular Dynamics
    • Abstract: Publication date: Available online 27 May 2016
      Source:Computational Biology and Chemistry
      Author(s): Nadtanet Nunthaboot, Kiattisak Lugsanangarm, Somsak Pianwanit, Sirirat Kokpol, Fumio Tanaka, Takeshi Nakanishi, Masaya Kitamura
      The structural and dynamical properties of five FMN binding protein (FBP) dimers, WT (wild type), E13K (Glu13 replaced by Lys), E13R (Glu13 replaced by Arg), E13T (Glu13 replaced by Thr) and E13Q (Glu13 replaced by Gln), were investigated using a method of molecular dynamics simulation (MDS). In crystal structures, subunit A (Sub A) and subunit B (Sub B) were almost completely equivalent in all of the five FBP dimers. However, the predicted MDS structures of the two subunits were not equivalent in solution, revealed by the distances and inter-planar angles between isoalloxazine (Iso) and aromatic amino acids (Trp32, Tyr35 and Trp106) as well as the hydrogen bonding pairs between Iso and nearby amino acids. Residue root of mean square fluctuations (RMSF) also displayed considerable differences between Sub A and Sub B and in the five FBP dimers. The dynamics of the whole protein structures were examined with the distance (RNN) between the peptide N atom of the N terminal (Met1) and the peptide N atom of the C terminal (Leu122). Water molecules were rarely accessible to Iso in all FBP dimers which are in contrast with other flavoenzymes.
      Graphical abstract image

      PubDate: 2016-05-28T09:34:06Z
  • Designing Peptide Inhibitor of Insulin Receptor to Induce Diabetes
           Mellitus Type 2 in Animal Model Mus musculus
    • 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.
      Graphical abstract image

      PubDate: 2016-05-28T09:34:06Z
  • IFC Editorial Board
    • Abstract: Publication date: June 2016
      Source:Computational Biology and Chemistry, Volume 62

      PubDate: 2016-05-28T09:34:06Z
  • Title page
    • Abstract: Publication date: June 2016
      Source:Computational Biology and Chemistry, Volume 62

      PubDate: 2016-05-28T09:34:06Z
  • 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
    • 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.
      Graphical abstract image

      PubDate: 2016-05-23T09:19:10Z
  • Molecular cloning, computational analysis and expression pattern of
           forkhead box l2 (Foxl2) gene in Catfish
    • 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
  • Small molecule ligand docking to genotype specific bundle structures of
           hepatitis C virus (HCV) p7 protein
    • 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.
      Graphical abstract image

      PubDate: 2016-05-23T09:19:10Z
    • 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
  • Hidden Heterogeneity of Transcription Factor Binding Sites: A Case Study
           of SF-1
    • 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
    • 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
  • Guest Editorial for the 14th Asia Pacific Bioinformatics Conference
    • Abstract: Publication date: Available online 13 April 2016
      Source:Computational Biology and Chemistry
      Author(s): Lu Tian, Jijun Tang, Yi-Ping Phoebe Chen

      PubDate: 2016-04-17T07:59:36Z
  • Using propensity score adjustment method in genetic association studies
    • Abstract: Publication date: Available online 3 March 2016
      Source:Computational Biology and Chemistry
      Author(s): Amrita Sengupta Chattopadhyay, Ying-Chao Lin, Ai-Ru Hsieh, Chien-Ching Chang, Ie-Bin Lian, Cathy S.J. Fann
      Background The statistical tests for single locus disease association are mostly under-powered. If a disease associated causal single nucleotide polymorphism (SNP) operates essentially through a complex mechanism that involves multiple SNPs or possible environmental factors, its effect might be missed if the causal SNP is studied in isolation without accounting for these unknown genetic influences. In this study, we attempt to address the issue of reduced power that is inherent in single point association studies by accounting for genetic influences that negatively impact the detection of causal variant in single point association analysis. In our method we use Propensity Score (PS) to adjust for the effect of SNPs that influence the marginal association of a candidate marker. These SNPs might be in linkage disequilibrium (LD) and/or epistatic with the target-SNP and have a joint interactive influence on the disease under study. We therefore propose a Propensity Score Adjustment Method (PSAM) as a tool for dimension reduction to improve the power for single locus studies through an estimated PS to adjust for influence from these SNPs while regressing disease status on the target-genetic locus. The degree of freedom of such a test is therefore always restricted to 1. Results We assess PSAM under the null hypothesis of no disease association to affirm that it correctly controls for the type-I-error rate (<0.05). PSAM displays reasonable power (>70%) and shows an average of 15% improvement in power as compared with commonly-used logistic regression method and PLINK under most simulated scenarios. Using the open-access Multifactor Dimensionality Reduction dataset, PSAM displays improved significance for all disease loci. Through a whole genome study, PSAM was able to identify 21 less significant SNPs from the GAW16 NARAC dataset by reducing the original trend-test p-values from within 0.001 and 0.05 to less than 0.0009, and among which 6 SNPs were further found to be associated with immunity and inflammation. Conclusions PSAM improves the significance of single-locus association of causal SNPs which have had marginal single point association by adjusting for influence from other SNPs in a dataset. This would explain part of the missing heritability without increasing the complexity of the model due to huge multiple testing scenarios. The newly reported SNPs from GAW16 data would provide evidences for further research to elucidate the etiology of Rheumatoid Arthritis. PSAM is proposed as an exploratory tool that would be complementary to other existing methods. A downloadable user friendly program, PSAM, written in SAS, is available for public use.
      Graphical abstract image

      PubDate: 2016-03-08T03:38:24Z
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