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  Subjects -> ENGINEERING (Total: 2269 journals)
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CHEMICAL ENGINEERING (190 journals)                     

Showing 1 - 0 of 0 Journals sorted alphabetically
AATCC Journal of Research     Full-text available via subscription   (Followers: 5)
ACS Sustainable Chemistry & Engineering     Hybrid Journal   (Followers: 2)
Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials     Hybrid Journal   (Followers: 4)
Acta Polymerica     Hybrid Journal   (Followers: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 20)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 6)
Advanced Chemical Engineering Research     Open Access   (Followers: 29)
Advanced Powder Technology     Hybrid Journal   (Followers: 15)
Advances in Applied Ceramics     Hybrid Journal   (Followers: 4)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 24)
Advances in Chemical Engineering and Science     Open Access   (Followers: 52)
Advances in Polymer Technology     Hybrid Journal   (Followers: 12)
African Journal of Pure and Applied Chemistry     Open Access   (Followers: 7)
Annual Review of Analytical Chemistry     Full-text available via subscription   (Followers: 9)
Annual Review of Chemical and Biomolecular Engineering     Full-text available via subscription   (Followers: 12)
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: 4)
Biomass Conversion and Biorefinery     Partially Free   (Followers: 10)
Brazilian Journal of Chemical Engineering     Open Access   (Followers: 3)
Bulletin of Chemical Reaction Engineering & Catalysis     Open Access   (Followers: 2)
Bulletin of the Chemical Society of Ethiopia     Open Access   (Followers: 3)
Carbohydrate Polymers     Hybrid Journal   (Followers: 8)
Catalysts     Open Access   (Followers: 6)
ChemBioEng Reviews     Full-text available via subscription   (Followers: 1)
Chemical and Engineering News     Free   (Followers: 10)
Chemical and Materials Engineering     Open Access   (Followers: 8)
Chemical and Petroleum Engineering     Hybrid Journal   (Followers: 10)
Chemical and Process Engineering     Open Access   (Followers: 22)
Chemical and Process Engineering Research     Open Access   (Followers: 19)
Chemical Engineering & Technology     Hybrid Journal   (Followers: 32)
Chemical Engineering and Processing: Process Intensification     Hybrid Journal   (Followers: 17)
Chemical Engineering and Science     Open Access   (Followers: 14)
Chemical Engineering Communications     Hybrid Journal   (Followers: 13)
Chemical Engineering Education     Full-text available via subscription  
Chemical Engineering Journal     Hybrid Journal   (Followers: 31)
Chemical Engineering Research and Design     Hybrid Journal   (Followers: 22)
Chemical Engineering Research Bulletin     Open Access   (Followers: 9)
Chemical Engineering Science     Hybrid Journal   (Followers: 23)
Chemical Geology     Hybrid Journal   (Followers: 15)
Chemical Papers     Hybrid Journal   (Followers: 2)
Chemical Product and Process Modeling     Hybrid Journal   (Followers: 3)
Chemical Reviews     Full-text available via subscription   (Followers: 146)
Chemical Society Reviews     Full-text available via subscription   (Followers: 39)
Chemical Technology     Open Access   (Followers: 12)
ChemInform     Hybrid Journal   (Followers: 7)
Chemistry & Industry     Hybrid Journal   (Followers: 4)
Chemistry Central Journal     Open Access   (Followers: 4)
Chemistry of Materials     Full-text available via subscription   (Followers: 158)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
Chinese Chemical Letters     Full-text available via subscription   (Followers: 3)
Chinese Journal of Chemical Engineering     Full-text available via subscription   (Followers: 4)
Chinese Journal of Chemical Physics     Hybrid Journal   (Followers: 1)
Coke and Chemistry     Hybrid Journal   (Followers: 1)
Coloration Technology     Hybrid Journal  
Computational Biology and Chemistry     Hybrid Journal   (Followers: 11)
Computer Aided Chemical Engineering     Full-text available via subscription   (Followers: 1)
Computers & Chemical Engineering     Hybrid Journal   (Followers: 10)
CORROSION     Full-text available via subscription   (Followers: 18)
Corrosion Engineering, Science and Technology     Hybrid Journal   (Followers: 33)
Corrosion Reviews     Hybrid Journal   (Followers: 3)
Crystal Research and Technology     Hybrid Journal   (Followers: 5)
Current Opinion in Chemical Engineering     Open Access   (Followers: 8)
Education for Chemical Engineers     Hybrid Journal   (Followers: 5)
Eksergi     Open Access  
Emerging Trends in Chemical Engineering     Full-text available via subscription   (Followers: 2)
European Polymer Journal     Hybrid Journal   (Followers: 41)
Fibers and Polymers     Full-text available via subscription   (Followers: 5)
Fluorescent Materials     Open Access   (Followers: 1)
Focusing on Modern Food Industry     Open Access   (Followers: 2)
Frontiers of Chemical Science and Engineering     Hybrid Journal   (Followers: 2)
Gels     Open Access  
Geochemistry International     Hybrid Journal   (Followers: 2)
Handbook of Powder Technology     Full-text available via subscription   (Followers: 5)
Heat Exchangers     Open Access   (Followers: 2)
High Performance Polymers     Hybrid Journal  
Hungarian Journal of Industry and Chemistry     Open Access  
Indian Chemical Engineer     Hybrid Journal   (Followers: 5)
Indian Journal of Chemical Technology (IJCT)     Open Access   (Followers: 9)
Indonesian Journal of Chemical Science     Open Access   (Followers: 1)
Industrial & Engineering Chemistry     Full-text available via subscription   (Followers: 10)
Industrial & Engineering Chemistry Research     Full-text available via subscription   (Followers: 21)
Industrial Chemistry Library     Full-text available via subscription   (Followers: 3)
Industrial Gases     Open Access  
Info Chimie Magazine     Full-text available via subscription   (Followers: 3)
International Journal of Chemical and Petroleum Sciences     Open Access   (Followers: 2)
International Journal of Chemical Engineering     Open Access   (Followers: 7)
International Journal of Chemical Reactor Engineering     Hybrid Journal   (Followers: 3)
International Journal of Chemical Technology     Open Access   (Followers: 5)
International Journal of Chemoinformatics and Chemical Engineering     Full-text available via subscription   (Followers: 2)
International Journal of Food Science     Open Access   (Followers: 3)
International Journal of Industrial Chemistry     Open Access   (Followers: 1)
International Journal of Polymeric Materials     Hybrid Journal   (Followers: 5)
International Journal of Science and Engineering     Open Access   (Followers: 4)
International Journal of Waste Resources     Open Access   (Followers: 3)
Journal of Chemical Engineering & Process Technology     Open Access   (Followers: 3)
Journal of Applied Crystallography     Hybrid Journal   (Followers: 5)
Journal of Applied Electrochemistry     Hybrid Journal   (Followers: 11)
Journal of Applied Polymer Science     Hybrid Journal   (Followers: 109)
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: 16)
Journal of Chemical Engineering and Materials Science     Open Access   (Followers: 2)
Journal of Chemical Science and Technology     Open Access   (Followers: 4)
Journal of Chemical Sciences     Partially Free   (Followers: 17)
Journal of Chemical Technology & Biotechnology     Hybrid Journal   (Followers: 10)
Journal of Chemical Theory and Computation     Full-text available via subscription   (Followers: 14)
Journal of CO2 Utilization     Hybrid Journal   (Followers: 2)
Journal of Crystallization Process and Technology     Open Access   (Followers: 7)
Journal of Environmental Chemical Engineering     Hybrid Journal   (Followers: 3)
Journal of Food Measurement and Characterization     Hybrid Journal  
Journal of Food Processing & Technology     Open Access   (Followers: 1)
Journal of Fuel Chemistry and Technology     Full-text available via subscription   (Followers: 4)
Journal of Geochemical Exploration     Hybrid Journal   (Followers: 1)
Journal of Industrial and Engineering Chemistry     Hybrid Journal   (Followers: 1)
Journal of Information Display     Hybrid Journal   (Followers: 1)
Journal of Inorganic and Organometallic Polymers and Materials     Partially Free   (Followers: 8)
Journal of Modern Chemistry & Chemical Technology     Full-text available via subscription   (Followers: 2)
Journal of Molecular Catalysis A: Chemical     Hybrid Journal   (Followers: 5)
Journal of Non-Crystalline Solids     Hybrid Journal   (Followers: 7)
Journal of Organic Semiconductors     Open Access   (Followers: 4)
Journal of Physics and Chemistry of Solids     Hybrid Journal   (Followers: 5)
Journal of Polymer and Biopolymer Physics Chemistry     Open Access   (Followers: 4)
Journal of Polymer Engineering     Hybrid Journal   (Followers: 8)
Journal of Polymer Research     Hybrid Journal   (Followers: 6)
Journal of Polymer Science Part C : Polymer Letters     Hybrid Journal   (Followers: 6)
Journal of Polymers     Open Access   (Followers: 2)
Journal of Polymers and the Environment     Hybrid Journal   (Followers: 1)
Journal of Pure and Applied Chemistry Research     Open Access   (Followers: 1)
Journal of the American Chemical Society     Full-text available via subscription   (Followers: 269)
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   (Followers: 1)
Jurnal Reaktor     Open Access  
Jurnal Teknologi Dan Industri Pangan     Open Access   (Followers: 1)
Konversi     Open Access  
Korean Journal of Chemical Engineering     Hybrid Journal   (Followers: 3)
Main Group Metal Chemistry     Hybrid Journal   (Followers: 1)
Materials Chemistry and Physics     Full-text available via subscription   (Followers: 14)
Materials Science and Applied Chemistry     Open Access  
Materials Sciences and Applied Chemistry     Full-text available via subscription  
Modern Chemistry & Applications     Open Access  
Molecular Imprinting     Open Access  
Nanocontainers     Open Access  
Nanofabrication     Open Access  
Noise Control Engineering Journal     Full-text available via subscription   (Followers: 2)
Ochrona Srodowiska i Zasobów Naturalnych : Environmental Protection and Natural Resources     Open Access  
Petroleum Chemistry     Hybrid Journal   (Followers: 1)
Physics and Chemistry of Glasses - European Journal of Glass Science and Technology Part B     Full-text available via subscription   (Followers: 4)
Plasma Processes and Polymers     Hybrid Journal   (Followers: 1)
Plasmas and Polymers     Hybrid Journal  
Polymer     Hybrid Journal   (Followers: 109)
Polymer Bulletin     Hybrid Journal   (Followers: 7)
Polymer Composites     Hybrid Journal   (Followers: 14)
Polyolefins Journal     Open Access  
Powder Technology     Hybrid Journal   (Followers: 14)
Recyclable Catalysis     Open Access   (Followers: 1)
Research on Chemical Intermediates     Hybrid Journal  
Reviews in Chemical Engineering     Hybrid Journal   (Followers: 5)
Revista Cubana de Química     Open Access  
Revista ION     Open Access  
Revista Mexicana de Ingeniería Química     Open Access  
Rubber Chemistry and Technology     Full-text available via subscription   (Followers: 2)
Russian Chemical Bulletin     Hybrid Journal   (Followers: 2)
Russian Journal of Applied Chemistry     Hybrid Journal   (Followers: 1)
Science and Engineering of Composite Materials     Hybrid Journal   (Followers: 58)
Solid Fuel Chemistry     Hybrid Journal  
South African Journal of Chemical Engineering     Open Access   (Followers: 2)
South African Journal of Chemistry     Open Access   (Followers: 2)
Surface Engineering and Applied Electrochemistry     Hybrid Journal   (Followers: 5)
Sustainable Chemical Processes     Open Access   (Followers: 2)
Synthesis Lectures on Chemical Engineering and Biochemical Engineering     Full-text available via subscription  
The Canadian Journal of Chemical Engineering     Hybrid Journal   (Followers: 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: 2)
Zeitschrift für Naturforschung B : A Journal of Chemical Sciences     Open Access   (Followers: 1)

           

Journal Cover Computational Biology and Chemistry
  [SJR: 0.491]   [H-I: 47]   [11 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1476-9271
   Published by Elsevier Homepage  [3040 journals]
  • Bipartite network analysis reveals metabolic gene expression profiles that
           are highly associated with the clinical outcomes of acute myeloid leukemia
           
    • Abstract: Publication date: Available online 6 January 2017
      Source:Computational Biology and Chemistry
      Author(s): Fanfan Xie, Mingxiong He, Li He, Keqin Liu, Menglong Li, Guoquan Hu, Zhining Wen
      Dysregulated and reprogrammed metabolism is one of the most important characteristics of cancer, and exploiting cancer cell metabolism can aid in understanding the diverse clinical outcomes for patients. To investigate the differences in metabolic pathways among patients with acute myeloid leukemia (AML) and differential survival outcomes, we systematically conducted microarray data analysis of the metabolic gene expression profiles from 384 patients available from the Gene Expression Omnibus and Cancer Genome Atlas databases. Pathway enrichment analysis of differentially expressed genes (DEGs) showed that the metabolic differences between low-risk and high-risk patients mainly existed in two pathways: biosynthesis of unsaturated fatty acids and oxidative phosphorylation. Using the gene-pathway bipartite network, 62 metabolic genes were identified from 272 DEGs involved in 88 metabolic pathways. Based on the expression patterns of the 62 genes, patients with shorter overall survival (OS) durations in the training set (hazard ratio (HR)=1.58, p =0.038) and in two test sets (HR=1.69 and 1.56 and p =0.089 and 0.029, respectively) were well discriminated by hierarchical clustering analysis. Notably, the expression profiles of ALAS2, BCAT1, BLVRB, and HK3 showed distinct differences between the low-risk and high-risk patients. In addition, models for predicting the OS outcome of AML from the 62 gene signatures achieved improved performance compared with previous studies. In conclusion, our findings reveal significant differences in metabolic processes of patients with AML with diverse survival durations and provide valuable information for clinical translation.
      Graphical abstract image

      PubDate: 2017-01-10T14:49:24Z
       
  • Targeting eukaryotic-like Serine/Threonine Protein Kinase of Mycobacterium
           tuberculosis, PknB with phytomolecules
    • Abstract: Publication date: Available online 9 January 2017
      Source:Computational Biology and Chemistry
      Author(s): Sandeep Appunni, P.M. Rajisha, Muni Rubens, Sangoju Chandana, Himanshu Narayan Singh, Vishnu Swarup
      Tuberculosis (TB), caused by Mycobacterium tuberculosis is one of the most lethal communicable disease globally. As per the WHO Global TB Report (2015), 9.6 million cases were reported in year 2014 alone. The receptor-like protein kinase, PknB is crucial for sustained mycobacterial growth. Therefore, PknB can be a potential target to develop anti-tuberculosis drugs. In present study, we performed a comparative study to investigate binding efficacies of three phytomolecules namely, Demethylcalabaxanthone, Cryptolepine hydrochloride and Ermanin. 3D structures of PknB and phytomolecules were retrieved from Protein Data Bank (PDB ID: 2FUM) and PubChem Chemical Compound Database, respectively. PknB was set to be rigid and phytochemicals were kept free to rotate. All computational simulations were carried out using Autodock 4.0 on Windows platform. In-silico study demonstrated a strong complex formation (large binding constants and low ΔG) between phytomolecules and target protein PknB of Mycobacterium tuberculosis. However, Demethylcalabaxanthone was able to bind PknB more strongly (Kb =6.8×105 M−1, ΔG=−8.06kcal/mol) than Cryptolepine hydrochloride (Kb =3.06×105 M−1, ΔG=−7.58kcal/mol) and Ermanin (Kb =9.8×104 M−1, ΔG=−6.9kcal/mol). These in silico analysis indicate that phytomolecules are capable to target PknB protein efficiently which is vital for mycobacterial survival and therefore can be excellent alternatives to conventional anti-tuberculosis drugs.
      Graphical abstract image

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

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

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

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

      PubDate: 2017-01-10T14:49:24Z
       
  • Disruption of redox catalytic functions of peroxiredoxin-thioredoxin
           complex in Mycobacterium tuberculosis H37Rv using small interface binding
           molecules
    • Abstract: Publication date: April 2017
      Source:Computational Biology and Chemistry, Volume 67
      Author(s): Arun Bahadur Gurung, Amit Kumar Das, Atanu Bhattacharjee
      Mycobacterium tuberculosis has distinctive ability to detoxify various microbicidal superoxides and hydroperoxides via a redox catalytic cycle involving thiol reductants of peroxiredoxin (Prx) and thioredoxin (Trx) systems which has conferred on it resistance against oxidative killing and survivability within host. We have used computational approach to disrupt catalytic functions of Prx-Trx complex which can possibly render the pathogen vulnerable to oxidative killing in the host. Using protein–protein docking method, we have successfully constructed the Prx-Trx complex. Statistics of interface region revealed contact area of each monomer less than 1500Å2 and enriched in polar amino acids indicating transient interaction between Prx and Trx. We have identified ZINC40139449 as a potent interface binding molecule through virtual screening of drug-like compounds from ZINC database. Molecular dynamics (MD) simulation studies showed differences in structural properties of Prx-Trx complex both in apo and ligand bound states with regard to root mean square deviation (RMSD), radius of gyration (Rg), root mean square fluctuations (RMSF), solvent accessible surface area (SASA) and number of hydrogen bonds (NHBs). Interestingly, we found stability of two conserved catalytic residues Cys61 and Cys174 of Prx and conserved catalytic motif, WCXXC of Trx upon binding of ZINC40139449. The time dependent displacement study reveals that the compound is quite stable in the interface binding region till 30ns of MD simulation. The structural properties were further validated by principal component analysis (PCA). We report ZINC40139449 as promising lead which can be further evaluated by in vitro or in vivo enzyme inhibition assays.
      Graphical abstract image

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

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

      PubDate: 2017-01-10T14:49:24Z
       
  • Predicting protein subcellular localization based on information content
           of gene ontology terms
    • Authors: Shu-Bo Zhang; Qiang-Rong Tang
      Pages: 1 - 7
      Abstract: Publication date: Available online 14 September 2016
      Source:Computational Biology and Chemistry
      Author(s): Shu-Bo Zhang, Qiang-Rong Tang
      Predicting the location where a protein resides within a cell is important in cell biology. Computational approaches to this issue have attracted more and more attentions from the community of biomedicine. Among the protein features used to predict the subcellular localization of proteins, the feature derived from Gene Ontology (GO) has been shown to be superior to others. However, most of the sights in this field are set on the presence or absence of some predefined GO terms. We proposed a method to derive information from the intrinsic structure of the GO graph. The feature vector was constructed with each element in it representing the information content of the GO term annotating to a protein investigated, and the support vector machines was used as classifier to test our extracted features. Evaluation experiments were conducted on three protein datasets and the results show that our method can enhance eukaryotic and human subcellular location prediction accuracy by up to 1.1% better than previous studies that also used GO-based features. Especially in the scenario where the cellular component annotation is absent, our method can achieved satisfied results with an overall accuracy of more than 87%.
      Graphical abstract image

      PubDate: 2016-09-19T04:30:37Z
      DOI: 10.1016/j.compbiolchem.2016.09.009
      Issue No: Vol. 65 (2016)
       
  • 1,3-oxazole derivatives as potential anticancer agents: computer modeling
           and experimental study
    • Authors: Ivan Semenyuta; Vasyl Kovalishyn; Vsevolod Tanchuk; Stepan Pilyo; Vladimir Zyabrev; Volodymyr Blagodatnyy; Olena Trokhimenko; Volodymyr Brovarets; Larysa Metelytsia
      Pages: 8 - 15
      Abstract: Publication date: Available online 21 September 2016
      Source:Computational Biology and Chemistry
      Author(s): Ivan Semenyuta, Vasyl Kovalishyn, Vsevolod Tanchuk, Stepan Pilyo, Vladimir Zyabrev, Volodymyr Blagodatnyy, Olena Trokhimenko, Volodymyr Brovarets, Larysa Metelytsia
      Microtubules play a significant role in cell growth and functioning. Therefore inhibition of the microtubule assemblies has emerged as one of the most promising cancer treatment strategies. Predictive QSAR models were built on a series of selective inhibitors of the tubulin were performed by using Associative Neural Networks (ANN). To overcome the problem of data overfitting due to the descriptor selection, a 5-fold cross-validation with variable selection in each step of the analysis was used. All developed QSAR models showed excellent statistics on the training (total accuracy: 0.96–0.97) and test sets (total accuracy: 0.95–97). The models were further validated by 11 synthesized 1,3-oxazole derivatives and all of them showed inhibitory effect on the Hep-2 cancer cell line. The most promising compound showed inhibitory activity IC50 =60.2 μM. In order to hypothesize their mechanism of action the top three compounds were docked in the colchicine binding site of tubulin and showed reasonable docking scores as well as favorable interactions with the protein.
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      PubDate: 2016-09-23T04:35:10Z
      DOI: 10.1016/j.compbiolchem.2016.09.012
      Issue No: Vol. 65 (2016)
       
  • Investigating ego modules involved in TGFβ3-induced chondrogenesis in
           mesenchymal stem cells based on ego network
    • Authors: Jing-Guo Wu; Qing-Wei Jia; Yong Li; Fei-Fei Cao; Xi-Shan Zhang; Cong Liu
      Pages: 16 - 20
      Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65
      Author(s): Jing-Guo Wu, Qing-Wei Jia, Yong Li, Fei-Fei Cao, Xi-Shan Zhang, Cong Liu
      Objective This paper aimed to investigate ego modules for TGFβ3-induced chondrogenesis in mesenchymal stem cells (MSCs) using ego network algorithm. Methods The ego network algorithm comprised three parts, extracting differential expression network (DEN) based on gene expression data and protein-protein interaction (PPI) data; exploring ego genes by reweighting DEN; and searching ego modules by ego gene expansions. Subsequently, permutation test was carried out to evaluate the statistical significance of the ego modules. Finally, pathway enrichment analysis was conducted to investigate ego pathways enriched by the ego modules. Results A total of 15 ego genes were obtained from the DEN, such as PSMA4, HNRNPM and WDR77. Starting with each ego genes, 15 candidate modules were gained. When setting the thresholds of the area under the receiver operating characteristics curve (AUC) ≥0.9 and gene size ≥4, three ego modules (Module 3, Module 8 and Module 14) were identified, and all of them had statistical significances between normal and TGFβ3-induced chondrogenesis in MSCs. By mapping module genes to confirmed pathway database, their ego pathways were detected, Cdc20:Phospho-APC/C mediated degradation of Cyclin A for Module 3, Mitotic G1-G1/S phases for Module 8, and mRNA Splicing for Module 14. Conclusions We have successfully identified three ego modules, evaluated their statistical significances and investigated their functional enriched ego pathways. The findings might provide potential biomarkers and give great insights to reveal molecular mechanism underlying this process.
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      PubDate: 2016-10-02T17:51:38Z
      DOI: 10.1016/j.compbiolchem.2016.09.017
      Issue No: Vol. 65 (2016)
       
  • Identification of potential inhibitor and enzyme-inhibitor complex on
           trypanothione reductase to control Chagas disease
    • Authors: Mohammad Uzzal Hossain; Arafat Rahman Oany; Shah Adil Ishtiyaq Ahmad; Md. Anayet Hasan; Md. Arif Khan; Md Al Ahad Siddikey
      Pages: 29 - 36
      Abstract: Publication date: Available online 7 October 2016
      Source:Computational Biology and Chemistry
      Author(s): Mohammad Uzzal Hossain, Arafat Rahman Oany, Shah Adil Ishtiyaq Ahmad, Md. Anayet Hasan, Md. Arif Khan, Md Al Ahad Siddikey
      Chagas is a parasitic disease with major threat to public health due to its resistance against commonly available drugs. Trypanothione reductase (TryR) is the key enzyme to develop this disease. Though this enzyme is well thought-out as potential drug target, the accurate structure of enzyme-inhibitor complex is required to design a potential inhibitor which is less available for TryR. In this research, we aimed to investigate the advanced drug over the available existing drugs by designing inhibitors as well as to identify a new enzyme-inhibitor complex that may act as a template for drug design. A set of analogues were designed from a known inhibitor Quinacrine Mustard (QUM) to identify the effective inhibitor against this enzyme. Further, the pharmacoinformatics elucidation and structural properties of designed inhibitor proposed effective drug candidates against Chagas disease. Molecular docking study suggests that a designed inhibitor has higher binding affinity in both crystal and modeled TryR and also poses similar interacting residues as of crystal TryR-QUM complex structure. The comparative studies based on in silico prediction proposed an enzyme-inhibitor complex which could be effective to control the disease activity. So our in silico analysis based on TryR built model, Pharmacophore and docking analysis might play an important role for the development of novel therapy for Chagas disease. But both animal model experiments and clinical trials must be done to confirm the efficacy of the therapy.

      PubDate: 2016-10-09T18:58:02Z
      DOI: 10.1016/j.compbiolchem.2016.10.002
      Issue No: Vol. 65 (2016)
       
  • A comparative QSAR analysis and molecular docking studies of phenyl
           piperidine derivatives as potent dual NK1R antagonists/serotonin
           transporter (SERT) inhibitors
    • Abstract: Publication date: Available online 23 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Somayeh Zare, Masood Fereidoonnezhad, Davoud Afshar, Zahra Ramezani
      Depression is a critical mood disorder that affects millions of patients. Available therapeutic antidepressant agents are associated with several undesirable side effects. Recently, it has been shown that Neurokinin 1 receptor (NK1R) antagonists can potentiate the antidepressant effects of serotonin-selective reuptake inhibitors (SSRIs). In this study, a series of phenyl piperidine derivatives as potent dual NK1R antagonists/serotonin transporter (SERT) inhibitors were applied to quantitative structure–activity relationship (QSAR) analysis. A collection of chemometrics methods such as multiple linear regression (MLR), factor analysis–based multiple linear regression (FA-MLR), principal component regression (PCR), and partial least squared combined with genetic algorithm for variable selection (GA-PLS) were applied to make relations between structural characteristics and NK1R antagonism/SERT inhibitory of these compounds. The best multiple linear regression equation was obtained from GA-PLS and MLR for NK1R and SERT, respectively. Based on the resulted model, an in silico-screening study was also conducted and new potent lead compounds based on new structural patterns were designed for both targets. Molecular docking studies of these compounds on both targets were also conducted and encouraging results were acquired. There was a good correlation between QSAR and docking results. The results obtained from validated docking studies indicate that the important amino acids inside the active site of the cavity that are responsible for essential interactions are Glu33, Asp395 and Arg26 for SERT and Ala30, Lys7, Asp31, Phe5 and Tyr82 for NK1R receptors.
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      PubDate: 2016-12-30T14:40:09Z
       
  • Role of computational efficiency indices and pose clustering in effective
           decision making: an example of annulated furanones in Pf-DHFR space
    • Abstract: Publication date: Available online 23 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Manoj Kumar, Tanpreet Kaur, Anuj Sharma
      In the present report, the role of computationally estimated efficiency indices and pose clustering has been demonstrated in effective decision making, resource management and chemical prioritization. As an example, 720 annulated furanones from six different scaffold classes were computationally docked against Pf-DHFR active site using AutoDock 4.2. Many trends were established by navigating efficiency indices (BEI and SEI) in 2D planes. These trends were then explained by comparing interaction profiles of docked poses with that of known actives/inhibitors. Cases where trends emerged from efficiency plotsresonated well with the pattern of a particular cluster diagram were considered as guidelines for optimization purpose. These kind of guidelines could help medicinal chemists in prioritization their work and in effective management of time, energy and chemical resources.
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      PubDate: 2016-12-30T14:40:09Z
       
  • In silico approach to identify non-synonymous SNPs in human obesity
           related gene, MC3R (melanocortin-3-receptor)
    • Abstract: Publication date: Available online 29 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Rajan Kumar Singh, Kulandaivelu Mahalingam
      The melanocortin-3-receptor (MC3R) is a novel gene candidate for human obesity, which involved in controlling the energy homeostasis and food intake behavior. The main aim behind this work is to investigate the potentially deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in obesity related gene MC3R by using six computational tools viz., PolyPhen, I-Mutant, PROVEAN, SIFT, PANTHER and PhD-SNP. In our study, we predicted eight nsSNPs i.e., rs74315393 (Ile146Asn), rs368205448 (Asp121Tyr), rs143321797 (Phe45Ser), rs17847261 (Cys274Ser), rs144166442 (Pro257His), rs370533946 (Leu224Pro), rs371354428 (Pro72Leu) and rs373708098 (Gly249Ser) found to be potentially deleterious. The functional impact of three nsSNPs i.e., rs74315393, rs368205448 and rs143321797 have already been validated experimentally in the context of human obesity. Moreover, Homology modeling and structural analysis were carried out for already experimentally validated nsSNPs i.e., rs74315393, rs368205448 and rs143321797 to check the stability of predicted models. The mutant models showed higher energy and RMSD (Root mean square deviation) values. In addition, FTSite server predicted one nsSNP i.e., rs368205448 (Asp121Tyr) out of eight identified nsSNPs found in the MC3R protein binding site. Thus, the present computational study may suggest that predicted nsSNPs possibly be a better drug target and contribute to the treatment and better understanding of human obesity.
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      PubDate: 2016-12-30T14:40:09Z
       
  • Comparative QSAR studies using HQSAR, CoMFA, and CoMSIA methods on cyclic
           sulfone hydroxyethylamines as BACE1 inhibitors
    • Abstract: Publication date: Available online 23 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Shuqun Zhang, Zichun Lin, Yinglan Pu, Yunqin Zhang, Li Zhang, Zhili Zuo
      The inhibition of β-secretase (BACE1) is currently the main pharmacological strategy available for Alzheimer’s disease (AD). 2D QSAR and 3D QSAR analysis on some cyclic sulfone hydroxyethylamines inhibitors against β-secretase (IC50: 0.002 to 2.75μM) were carried out using hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods. The best model based on the training set was generated with a HQSAR q2 value of 0.693 and r2 value of 0.981; a CoMFA q2 value of 0.534 and r2 value of 0.913; and a CoMSIA q2 value of 0.512 and r2 value of 0.973. In order to gain further understand of the vital interactions between cyclic sulfone hydroxyethylamines and the protease, the analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the BACE1. The final QSAR models could be helpful in the design and development of novel active BACE1 inhibitors.
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      PubDate: 2016-12-30T14:40:09Z
       
  • An efficient perturbation method to predict the functionally key sites of
           glutamine binding protein
    • Abstract: Publication date: Available online 29 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Dashuai Lv, Cunxin Wang, Chunhua Li, Jianjun Tan, Xiaoyi Zhang
      Glutamine-Binding Protein (GlnBP) of Escherichia coli, an important member of the periplasmic binding protein family, is responsible for the first step in the active transport of glutamine across the cytoplasmic membrane. In this work, the functionally key regulation sites of GlnBP were identified by utilizing a perturbation method proposed by our group, in which the residues whose perturbations markedly change the binding free energy between GlnBP and glutamine are considered to be functionally key residues. The results show that besides the substrate binding sites, some other residues distant from the binding pocket, including the ones in the hinge regions between the two domains, the front- and back- door channels and the exposed region, are important for the function of glutamine binding and transport. The predicted results are well consistent with the theoretical and experimental data, which indicates that our method is an effective approach to identify the key residues important for both ligand binding and long-range allosteric signal transmission. This work can provide some insights into the function performance of GlnBP and the physical mechanism of its allosteric regulation.
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      PubDate: 2016-12-30T14:40:09Z
       
  • In silico Study of Porphyrin-Anthraquinone Hybrids as CDK2 Inhibitor
    • Abstract: Publication date: Available online 19 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Muhammad Arba, Sunandar Iksan, La Ode Ahmad Nur Ramadhan, Daryono Hadi Tjahjono
      Cyclin-Dependent Kinases (CDKs) are known to play crucial roles in controlling cell cycle progression of eukaryotic cell and inhibition of their activity has long been considered as potential strategy in anti-cancer drug research. In the present work, a series of porphyrin-anthraquinone hybrids bearing meso-substituents, i.e. either pyridine or pyrazole rings were designed and computationally evaluated for their Cyclin Dependent Kinase-2 (CDK2) inhibitory activity using molecular docking, molecular dynamics simulation, and binding free energy calculation. The molecular docking simulation revealed that all six porphyrin hybrids were able to bind to ATP-binding site of CDK2 and interacted with key residues constituted the active cavity of CDK2, while molecular dynamics simulation indicated that all porphyrins bound to CDK2 were stable for 6ns. The binding free energies predicted by MM-PBSA method showed that most compounds exhibited higher affinity than that of native ligand (4-anilinoquinazoline, DTQ) and the affinity of mono-H2PyP-AQ was about three times better than that of DTQ, indicating its potential to be advanced as a new CDK2 inhibitor.
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      PubDate: 2016-12-23T14:35:32Z
       
  • Title page
    • Abstract: Publication date: February 2017
      Source:Computational Biology and Chemistry, Volume 66


      PubDate: 2016-12-23T14:35:32Z
       
  • Structure and expression of dna methyltransferase genes from apomictic and
           sexual Boechera species
    • Abstract: Publication date: Available online 20 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Kemal Melik Taşkin, Aslıhan Özbilen, Fatih Sezer, Kaan Hürkan, Şebnem Güneş
      In this study, we determined the structure of DNA methyltransferase (DNMT) genes in apomict and sexual Boechera species and investigated the expression levels during seed development. Protein and DNA sequences of diploid sexual Boechera stricta DNMT genes obtained from Phytozome 10.3 were used to identify the homologues in apomicts, Boechera holboellii and Boechera divaricarpa. Geneious R8 software was used to map the short-paired reads library of B. holboellii whole genome or B. divaricarpa transcriptome reads to the reference gene sequences. We determined three DNMT genes; for Boechera spp. METHYLTRANSFERASE1 (MET1), CHROMOMETHYLASE 3 (CMT3) and DOMAINS REARRANGED METHYLTRANSFERASE 1/2 (DRM2). We examined the structure of these genes with bioinformatic tools and compared with other DNMT genes in plants. We also examined the levels of expression in silique tissues after fertilization by semi-quantitative PCR. The structure of DNMT proteins in apomict and sexual Boechera species share common features. However, the expression levels of DNMT genes were different in apomict and sexual Boechera species. We found that DRM2 was upregulated in apomictic Boechera species after fertilization. Phylogenetic trees showed that three genes are conserved among green algae, monocotyledons and dicotyledons. Our results indicated a deregulation of DNA methylation machinery during seed development in apomicts.
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      PubDate: 2016-12-23T14:35:32Z
       
  • IFC Editorial Board
    • Abstract: Publication date: February 2017
      Source:Computational Biology and Chemistry, Volume 66


      PubDate: 2016-12-23T14:35:32Z
       
  • Exploring the resistance mechanism of imipenem in carbapenem hydrolysing
           class D beta-lactamases OXA-143 and its variant OXA-231 (D224A) expressing
           Acinetobacter baumannii: An in-silico approach
    • Abstract: Publication date: Available online 7 December 2016
      Source:Computational Biology and Chemistry
      Author(s): Kullappan Malathi, Anand Anbarasu, Sudha Ramaiah
      Acinetobacter baumannii (A. baumannii), is a Gram negative, coccobacilli and is associated with nosocomial infections. The bacterium has developed resistance to all known classes of antibiotics. Multi-drug resistant A. baumannii infections have been treated with the carbapenem group of antibiotics like imipenem and meropenem. Recent reports indicate that A. baumannii has acquired resistance to imipenem due to the secretion of carbapenem hydrolysing class D beta-lactamases (CHDLs). Such CHDLs found in carbapenem resistant A. baumannii belongs to OXA-143 and its variant OXA-231, which has Alanine (A) in place of Aspartic acid (D) at sequence position 224. The mutation of the OXA-231 CHDL alters the catalytic activity of the enzyme. Hence, the present study was carried out to find the probable mechanism of imipenem resistance in OXA-143 and OXA-231 (D224A) CHDLs expressing A. baumannii by employing molecular docking and dynamics. Methods Our study reveals that OXA-143 CHDL-imipenem complex has more binding affinity than OXA-231 (D224A) CHDL-imipenem complex. Our results indicate that there is a strong binding affinity of OXA-143 with imipenem when compared with OXA-243 and this mechanism might be the probable reason for imipenem resistance in OXA-143 expressing A. baumannii strains.
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      PubDate: 2016-12-09T13:21:14Z
       
  • Structure-based Optimization of Salt-bridge Network across the Complex
           Interface of PTPN4 PDZ Domain with Its Peptide Ligands in Neuroglioma
    • Abstract: Publication date: Available online 30 November 2016
      Source:Computational Biology and Chemistry
      Author(s): Xian Xiao, Qiang-Hua He, Li-Yan Yu, Song-Qing Wang, Yang Li, Hua Yang, Ai-Hua Zhang, Xiao-Hong Ma, Yu-Jie Peng, Bing Chen
      The PTP non-receptor type 4 (PTPN4) is an important regulator protein in learning, spatial memory and cerebellar synaptic plasticity; targeting the PDZ domain of PTPN4 has become as attractive therapeutic strategy for human neuroglioma. Here, we systematically examined the complex crystal structures of PTPN4 PDZ domain with its known peptide ligands; a number of charged amino acid residues were identified in these ligands and in the peptide-binding pocket of PDZ domain, which can constitute a complicated salt-bridge network across the complex interface. Molecular dynamics (MD) simulations, binding free energy calculations and continuum model analysis revealed that the electrostatic effect plays a predominant role in domain–peptide binding, while other noncovalent interactions such as hydrogen bonds and hydrophobic forces are also responsible for the binding. The computational findings were then used to guide structure-based optimization of the interfacial salt-bridge network. Consequently, five peptides were rationally designed using the high-affinity binder Cyto8-RETEV (RETEV−COOH) as template, including four single-point mutants (i.e. Cyto8-mtxe0: RETE E −COOH, Cyto8-mtxd-1: RET D V−COOH, Cyto8-mtxd-3: R D TEV−COOH and Cyto8-mtxk-4: K ETEV−COOH) and one double-point mutant (i.e. Cyto8-mtxd-1k-4: K ET D V−COOH). Binding assays confirmed that three (Cyto8-mtxd-1, Cyto8-mtxk-4 and Cyto8-mtxd-1k-4) out of the five designed peptides exhibit moderately or considerably increased affinity as compared to the native peptide Cyto8-RETEV.
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      PubDate: 2016-12-03T21:53:35Z
       
  • PrAS: Prediction of amidation sites using multiple feature extraction
    • Abstract: Publication date: February 2017
      Source:Computational Biology and Chemistry, Volume 66
      Author(s): Tong Wang, Wei Zheng, Qiqige Wuyun, Zhenfeng Wu, Jishou Ruan, Gang Hu, Jianzhao Gao
      Amidation plays an important role in a variety of pathological processes and serious diseases like neural dysfunction and hypertension. However, identification of protein amidation sites through traditional experimental methods is time consuming and expensive. In this paper, we proposed a novel predictor for Prediction of Amidation Sites (PrAS), which is the first software package for academic users. The method incorporated four representative feature types, which are position-based features, physicochemical and biochemical properties features, predicted structure-based features and evolutionary information features. A novel feature selection method, positive contribution feature selection was proposed to optimize features. PrAS achieved AUC of 0.96, accuracy of 92.1%, sensitivity of 81.2%, specificity of 94.9% and MCC of 0.76 on the independent test set. PrAS is freely available at https://sourceforge.net/p/praspkg.
      Graphical abstract image

      PubDate: 2016-12-03T21:53:35Z
       
  • BS-RNA: An efficient mapping and annotation tool for RNA bisulfite
           sequencing data
    • Abstract: Publication date: December 2016
      Source:Computational Biology and Chemistry, Volume 65
      Author(s): Fang Liang, Lili Hao, Jinyue Wang, Shuo Shi, Jingfa Xiao, Rujiao Li
      Cytosine methylation is one of the most important RNA epigenetic modifications. With the development of experimental technology, scientists attach more importance to RNA cytosine methylation and find bisulfite sequencing is an effective experimental method for RNA cytosine methylation study. However, there are only a few tools can directly deal with RNA bisulfite sequencing data efficiently. Herein, we developed a specialized tool BS-RNA, which can analyze cytosine methylation of RNA based on bisulfite sequencing data and support both paired-end and single-end sequencing reads from directional bisulfite libraries. For paired-end reads, simply removing the biased positions from the 5′ end may result in “dovetailing” reads, where one or both reads seem to extend past the start of the mate read. BS-RNA could map “dovetailing” reads successfully. The annotation result of BS-RNA is exported in BED (.bed) format, including locations, sequence context types (CG/CHG/CHH, H=A,T, or C), reference sequencing depths, cytosine sequencing depths, and methylation levels of covered cytosine sites on both Watson and Crick strands. BS-RNA is an efficient, specialized and highly automated mapping and annotation tool for RNA bisulfite sequencing data. It performs better than the existing program in terms of accuracy and efficiency. BS-RNA is developed by Perl language and the source code of this tool is freely available from the website: http://bs-rna.big.ac.cn.

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

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


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


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

      PubDate: 2016-11-26T20:09:57Z
       
  • Modeling of the catalytic core of Arabidopsis thaliana Dicer-like 4
           protein and its complex with double-stranded RNA
    • Abstract: Publication date: Available online 17 November 2016
      Source:Computational Biology and Chemistry
      Author(s): Agnieszka Mickiewicz, Joanna Sarzyńska, Maciej Miłostan, Anna Kurzyńska-Kokorniak, Agnieszka Rybarczyk, Piotr Łukasiak, Tadeusz Kuliński, Marek Figlerowicz, Jacek Błażewicz
      Plant Dicer-like proteins (DCLs) belong to the Ribonuclease III (RNase III) enzyme family. They are involved in the regulation of gene expression and antiviral defense through RNA interference pathways. A model plant, Arabidopsis thaliana encodes four DCL proteins (AtDCL1-4) that produce different classes of small regulatory RNAs. Our studies focus on AtDCL4 that processes double-stranded RNAs (dsRNAs) into 21 nucleotide trans-acting small interfering RNAs. So far, little is known about the structures of plant DCLs and the complexes they form with dsRNA. In this work, we present models of the catalytic core of AtDCL4 and AtDCL4-dsRNA complex constructed by computational methods. We built a homology model of the catalytic core of AtDCL4 comprising Platform, PAZ, Connector helix and two RNase III domains. To assemble the AtDCL4-dsRNA complex two modeling approaches were used. In the first method, to establish conformations that allow building a consistent model of the complex, we used Normal Mode Analysis for both dsRNA and AtDCL4. The second strategy involved template-based approach for positioning of the PAZ domain and manual arrangement of the Connector helix. Our results suggest that the spatial orientation of the Connector helix, Platform and PAZ relative to the RNase III domains is crucial for measuring dsRNA of defined length. The modeled complexes provide information about interactions that may contribute to the relative orientations of these domains and to dsRNA binding. All these information can be helpful for understanding the mechanism of AtDCL4-mediated dsRNA recognition and binding, to produce small RNA of specific size.
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      PubDate: 2016-11-21T20:01:17Z
       
  • Insights into structure and function of 30S Ribosomal Protein S2 (30S2) in
           Chlamydophila Pneumoniae: A potent target of Pneumonia
    • Abstract: Publication date: Available online 9 November 2016
      Source:Computational Biology and Chemistry
      Author(s): G. Koteswara Reddy, K. Nagamalleswara Rao, Kiran Yarrakula
      The gene 30S ribosomal protein S2 (30S2) is identified as a potential drug and vaccine target for Pneumonia. Its structural characterization is an important to understand the mechanism of action for identifying its receptor and/or other binding partners. The comparative genomics and proteomics studies are useful for structural characterization of 30S2 in C. Pneumoniae using different bioinformatics tools and web servers. In this study, the protein 30S2 structure was modelled and validated by Ramachandran plot. It is found that the modelled protein under most favoured “core” region was 88.7% and overall G-factor statistics with average score was −0.20. However, seven sequential motifs have been identified for 30S2 with reference codes (PR0095, PF0038, TIGR01012, PTHR11489, SSF52313 and PTHR11489). In addition, seven structural highly conserved residues have been identified in the large cleft are Lys160, Gly161and Arg162 with volume 1288.83Å3 and average depth of the cleft was 10.75Å. Moreover, biological functions, biochemical process and structural constituents of ribosome are also explored. The study will be helped us to understand the sequential, structural, functional and evolutionary clues of unknown proteins available in C. Pneumoniae.
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      PubDate: 2016-11-15T13:00:54Z
       
  • Identification of miRNA from Bouteloua gracilis, a drought tolerant grass,
           by deep sequencing and their in silico analysis
    • Abstract: Publication date: Available online 9 November 2016
      Source:Computational Biology and Chemistry
      Author(s): Perla Lucía Ordóñez-Baquera, Everardo González-Rodríguez, Gerardo Armando Aguado-Santacruz, Quintín Rascón-Cruz, Ana Conesa, Verónica Moreno-Brito, Raquel Echavarria, Joel Dominguez-Viveros
      Background MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate signal transduction, development, metabolism, and stress responses in plants through post-transcriptional degradation and/or translational repression of target mRNAs. Several studies have addressed the role of miRNAs in model plant species, but miRNA expression and function in economically important forage crops, such as Bouteloua gracilis (Poaceae), a high-quality and drought-resistant grass distributed in semiarid regions of the United States and northern Mexico remain unknown. Results We applied high-throughput sequencing technology and bioinformatics analysis and identified 31 conserved miRNA families and 53 novel putative miRNAs with different abundance of reads in chlorophyllic cell cultures derived from B. gracilis. Some conserved miRNA families were highly abundant and possessed predicted targets involved in metabolism, plant growth and development, and stress responses. We also predicted additional identified novel miRNAs with specific targets, including B. gracilis ESTs, which were detected under drought stress conditions. Conclusions Here we report 31 conserved miRNA families and 53 putative novel miRNAs in B. gracilis. Our results suggested the presence of regulatory miRNAs involved in modulating physiological and stress responses in this grass species.
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      PubDate: 2016-11-15T13:00:54Z
       
  • Investigating dysregulated pathways in Staphylococcus aureus (SA) exposed
           macrophages based on pathway interaction network
    • Abstract: Publication date: Available online 13 November 2016
      Source:Computational Biology and Chemistry
      Author(s): Wei Zhou, Yan Zhang, Yue-Hua Li, Shuang Wang, Jing-Jing Zhang, Cui-Xia Zhang, Zhi-Sheng Zhang
      Objective This work aimed to identify dysregulated pathways for Staphylococcus aureus (SA) exposed macrophages based on pathway interaction network (PIN). Methods The inference of dysregulated pathways was comprised of four steps: preparing gene expression data, protein-protein interaction (PPI) data and pathway data; constructing a PIN dependent on the data and Pearson correlation coefficient (PCC); selecting seed pathway from PIN by computing activity score for each pathway according to principal component analysis (PCA) method; and investigating dysregulated pathways in a minimum set of pathways (MSP) utilizing seed pathway and the area under the receiver operating characteristics curve (AUC) index implemented in support vector machines (SVM) model. Results A total of 20,545 genes, 449,833 interactions and 1,189 pathways were obtained in the gene expression data, PPI data and pathway data, respectively. The PIN was consisted of 8,388 interactions and 1,189 nodes, and Respiratory electron transport, ATP synthesis by chemiosmotic coupling, and heat production by uncoupling proteins was identified as the seed pathway. Finally, 15 dysregulated pathways in MSP (AUC=0.999) were obtained for SA infected samples, such as Respiratory electron transport and DNA Replication. Conclusions We have identified 15 dysregulated pathways for SA infected macrophages based on PIN. The findings might provide potential biomarkers for early detection and therapy of SA infection, and give insights to reveal the molecular mechanism underlying SA infections. However, how these dysregulated pathways worked together still needs to be studied.
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      PubDate: 2016-11-15T13:00:54Z
       
  • Computational analysis, structural modeling and ligand binding site
           prediction of Plasmodium falciparum 1-deoxy-d-xylulose-5-phosphate
           synthase
    • Abstract: Publication date: Available online 5 November 2016
      Source:Computational Biology and Chemistry
      Author(s): Achintya Mohan Goswami
      Malaria remains one of the most serious infectious diseases in the world. Though there are four species of Plasmodium genus, but the most responsible and virulent among them is Plasmodium falciparum. The unique biochemical processes that exist in Plasmodium falciparum provide a useful way to develop novel inhibitors. One such biochemical pathway is the methyl erythritol phosphate pathway (MEP), required to synthesize isoprenoid precursors. In the present study, a detailed computational analysis has been performed for 1-deoxy-d-xylulose-5-phosphate synthase, a key enzyme in MEP. The protein is found to be stable and residues from 825 to 971 are highly conserved across species. The homology model of the enzyme is developed using three web-based servers and Modeller software. It has twelve disordered regions indicating its druggability. Virtual screening of ZINC database identifies ten potential compounds in thiamine diphosphate binding region of the enzyme.
      Graphical abstract image

      PubDate: 2016-11-09T12:45:46Z
       
  • Development of a sugar-binding residue prediction system from protein
           sequences using support vector machine
    • Abstract: Publication date: Available online 9 November 2016
      Source:Computational Biology and Chemistry
      Author(s): Masaki Banno, Yusuke Komiyama, Wei Cao, Yuya Oku, Kokoro Ueki, Kazuya Sumikoshi, Shugo Nakamura, Tohru Terada, Kentaro Shimizu
      Several methods have been proposed for protein–sugar binding site prediction using machine learning algorithms. However, they are not effective to learn various properties of binding site residues caused by various interactions between proteins and sugars. In this study, we classified sugars into acidic and nonacidic sugars and showed that their binding sites have different amino acid occurrence frequencies. By using this result, we developed sugar-binding residue predictors dedicated to the two classes of sugars: an acid sugar binding predictor and a nonacidic sugar binding predictor. We also developed a combination predictor which combines the results of the two predictors. We showed that when a sugar is known to be an acidic sugar, the acidic sugar binding predictor achieves the best performance, and showed that when a sugar is known to be a nonacidic sugar or is not known to be either of the two classes, the combination predictor achieves the best performance. Our method uses only amino acid sequences for prediction. Support vector machine was used as a machine learning algorithm and the position-specific scoring matrix created by the position-specific iterative basic local alignment search tool was used as the feature vector. We evaluated the performance of the predictors using five-fold cross-validation. We have launched our system, as an open source freeware tool on the GitHub repository (http://doi.org/10.5281/zenodo.61513).
      Graphical abstract image Highlights

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

      PubDate: 2016-11-03T12:31:54Z
       
  • Small Family, Big Impact: In silico analysis of DREB2 transcription factor
           family in rice
    • Abstract: Publication date: Available online 29 October 2016
      Source:Computational Biology and Chemistry
      Author(s): Venura Herath
      Dehydration-responsive element- (DREB) proteins are considered as the master regulators of plant abiotic stress responses including drought, salinity and cold. They are also involved in other developmental processes such as embryo and endosperm development. DREB family of transcription factors consist of two sub families namely CBF1/DREB1 and DREB2. In this study, a genome-wide in silico analysis was carried out to dissect the structure and function of DREB2 family transcription factors in the rice genome. Using Arabidopsis DREB2 sequences a total of five rice DREB2 homologs were identified and they were distributed among four chromosomes. All OsDREBs contained the AP2 domain and unique [K/R]GKKGPxN motif characteristic to DREB2 family. During rice growth and development, three OsDREB2s namely OsDREB2A, OsDREB2B and OsABI4 were expressed and their expression was confined to embryo and endosperm tissues. OsDREB2A, OsDREB2B and OsDREB2C were expressed under abiotic stress conditions. OsDREB2B was expressed under drought, salinity and cold stress conditions while OsDREB2A and OsDREB2C were expressed only under drought and salinity conditions. The putative promoter regions of OsDREB2s were enriched with elements related to cellular development, hormonal regulation and stress response validating the observed expression dynamics. Co-expression analysis revealed that embryo development and stress related genes were expressed together with OsDREB2s. Predicted post-translational modifications indicated the fine regulation of OsDREB2s. These findings may shed light in uncovering the complex abiotic stress signaling networks and future genomics studies targeting the development of climate ready crops.
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      PubDate: 2016-11-03T12:31:54Z
       
  • Solvent Effect on Hydrogen Bonded Tyr⋯Asp⋯Arg Triads:
           Enzymatic Catalyzed Model System
    • Abstract: Publication date: Available online 2 November 2016
      Source:Computational Biology and Chemistry
      Author(s): Shihai Yan, Lishan Yao, Baotao Kang, Jin Yong Lee
      The hydrogen bond plays a vital role in structural arrangement, intermediate state stabilization, materials function, and biological activity of certain enzymatic reactions. The solvent and electronic effects on hydrogen bonds are illustrated employing the polarizable contimuum model at B3LYP/6–311++G(d,p) level. Geometry optimizations reflect the significant solvent and electronic effect. The proton departs spontaneously upon oxidation from the hydroxyl group of tyrosyl in hydrogen bonded Tyr⋯Asp⋯Arg triads in both gas phase and solvents. The electron transfer isomers are observed for anionic triads, no matter what the solvent is. The difference of distance between two hydrogen bonds is enlarged in solvent as compared to that in gas phase. The electronic effect on IR spectra is distinctive. The tyrosyl fragment tends to be oxidized and the arginine moiety is easier to bind an excess electron. The variations of chemical shift and spin-spin coupling constant are more significant upon electron transfer than upon solvent dielectric constant. The augmentation of solvent dielectric constant stabilizes the system, enhances the difference of isomers, and increases the vertical ionization potential and vertical electron affinity values.
      Graphical abstract image

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

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

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

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

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

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

      PubDate: 2016-10-16T11:30:26Z
       
  • Weighted Edge Based Clustering to Identify Protein Complexes in Protein
           Protein Interaction Networks incorporating Gene Expression Profile
    • Authors: Seketoulie Keretsu; Rosy Sarmah
      Abstract: Publication date: Available online 8 October 2016
      Source:Computational Biology and Chemistry
      Author(s): Seketoulie Keretsu, Rosy Sarmah
      Protein complex detection from Protein Protein Interaction (PPI) network has received a lot of focus in recent years. A number of methods identify protein complexes as dense sub-graphs using network information while several other methods detect protein complexes based on topological information. While the methods based on identifying dense sub-graphs are more effective in identifying protein complexes, not all protein complexes have high density. Moreover, existing methods focus more on static PPI networks and usually overlook the dynamic nature of protein complexes. Here, we propose a new method, Weighted Edge based Clustering (WEC), to identify protein complexes based on the weight of the edge between two interacting proteins, where the weight is defined by the edge clustering coefficient and the gene expression correlation between the interacting proteins. Our WEC method is capable of detecting highly inter-connected and co-expressed protein complexes. The experimental results of WEC on three real life data shows that our method can detect protein complexes effectively in comparison with other highly cited existing methods. Availability: The WEC tool is available at http://agnigarh.tezu.ernet.in/~rosy8/shared.html

      PubDate: 2016-10-09T18:58:02Z
      DOI: 10.1016/j.compbiolchem.2016.10.001
       
  • Protein-protein interaction and molecular dynamics analysis for
           identification of novel inhibitors in Burkholderia cepacia GG4
    • Authors: Money Gupta; Rashi Chauhan; Yamuna Prasad; Gulshan Wadhwa; Chakresh Kumar Jain
      Abstract: Publication date: Available online 8 October 2016
      Source:Computational Biology and Chemistry
      Author(s): Money Gupta, Rashi Chauhan, Yamuna Prasad, Gulshan Wadhwa, Chakresh Kumar Jain
      The lack of complete treatments and appearance of multiple drug-resistance strains of Burkholderia cepacia complex (Bcc) are causing an increased risk of lung infections in cystic fibrosis patients. Bcc infection is a big risk to human health and demands an urgent need to identify new therapeutics against these bacteria. Network biology has emerged as one of the prospective hope in identifying novel drug targets and hits. We have applied protein-protein interaction methodology to identify new drug-target candidates (orthologs) in Burkhloderia cepacia GG4, which is an important strain for studying the quorum-sensing phenomena. An evolutionary based ortholog mapping approach has been applied for generating the large scale protein-protein interactions in B. Cepacia. As a case study, one of the identified drug targets; GEM_3202, a NH (3)-dependent NAD synthetase protein has been studied and the potential ligand molecules were screened using the ZINC database. The three dimensional structure (NH (3)-dependent NAD synthetase protein) has been predicted from MODELLERv9.11 tool using multiple PDB templates such as 3DPI, 2PZ8 and 1NSY with sequence identity of 76%, 50% and 50% respectively. The structure has been validated with Ramachandaran plot having 100% residues of NadE in allowed region and overall quality factor of 81.75 using ERRAT tool. High throughput screening and Vina resulted in two potential hits against NadE such as ZINC83103551 and ZINC38008121. These molecules showed lowest binding energy of −5.7kcal/mol and high stability in the binding pockets during molecular dynamics simulation analysis. The similar approach for target identification could be applied for clinical strains of other pathogenic microbes.
      Graphical abstract image

      PubDate: 2016-10-09T18:58:02Z
      DOI: 10.1016/j.compbiolchem.2016.10.003
       
  • Building and analysis of protein-protein interactions related to diabetes
           mellitus using support vector machine, biomedical text mining and network
           analysis
    • Authors: Renu Vyas; Sanket Bapat Esha Jain Muthukumarasamy Karthikeyan Sanjeev Tambe
      Abstract: Publication date: Available online 30 September 2016
      Source:Computational Biology and Chemistry
      Author(s): Renu Vyas, Sanket Bapat, Esha Jain, Muthukumarasamy Karthikeyan, Sanjeev Tambe, Bhaskar D. Kulkarni
      In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein–protein interactions therefore assume significance. 1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies.
      Graphical abstract image

      PubDate: 2016-10-02T17:51:38Z
       
  • A Post-Decoding Re-Ranking Algorithm for Predicting Interacting Residues
           in Proteins with Hidden Markov Models Incorporating Long-Distance
           Information
    • Authors: Colin Kern; Li Liao
      Abstract: Publication date: Available online 29 September 2016
      Source:Computational Biology and Chemistry
      Author(s): Colin Kern, Li Liao
      Protein-protein interactions play a central role in the biological processes of cells. Accurate prediction of the interacting residues in protein-protein interactions enhances understanding of the interaction mechanisms and enables in silico mutagenesis, which can help facilitate drug design and deepen our understanding of the inner workings of cells. Correlations have been found among interacting residues as a result of selection pressure to retain the interaction during evolution. In previous work, incorporation of such correlations in the interaction profile hidden Markov models with a special decoding algorithm (ETB-Viterbi) has led to improvement in prediction accuracy. In this work, we first demonstrated the sub-optimality of the ETB-Viterbi algorithm, and then reformulated the optimality of decoding paths to include correlations between interacting residues. To identify optimal decoding paths, we propose a post-decoding re-ranking algorithm based on a genetic algorithm with simulated annealing and show that the new method gains an increase of near 14% in prediction accuracy over the ETB-Viterbi algorithm.

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