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  Subjects -> ENGINEERING (Total: 2291 journals)
    - CHEMICAL ENGINEERING (192 journals)
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    - ENGINEERING (1209 journals)
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CHEMICAL ENGINEERING (192 journals)                     

Showing 1 - 192 of 192 Journals sorted alphabetically
AATCC Journal of Research     Full-text available via subscription   (Followers: 7)
ACS Sustainable Chemistry & Engineering     Hybrid Journal   (Followers: 5)
Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials     Hybrid Journal   (Followers: 5)
Acta Polymerica     Hybrid Journal   (Followers: 9)
Additives for Polymers     Full-text available via subscription   (Followers: 21)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 8)
Advanced Chemical Engineering Research     Open Access   (Followers: 32)
Advanced Powder Technology     Hybrid Journal   (Followers: 17)
Advances in Applied Ceramics     Hybrid Journal   (Followers: 5)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 26)
Advances in Chemical Engineering and Science     Open Access   (Followers: 55)
Advances in Polymer Technology     Hybrid Journal   (Followers: 13)
African Journal of Pure and Applied Chemistry     Open Access   (Followers: 7)
Annual Review of Analytical Chemistry     Full-text available via subscription   (Followers: 11)
Annual Review of Chemical and Biomolecular Engineering     Full-text available via subscription   (Followers: 12)
Anti-Corrosion Methods and Materials     Hybrid Journal   (Followers: 10)
Applied Petrochemical Research     Open Access   (Followers: 2)
Asia-Pacific Journal of Chemical Engineering     Hybrid Journal   (Followers: 8)
Biochemical Engineering Journal     Hybrid Journal   (Followers: 14)
Biofuel Research Journal     Open Access   (Followers: 4)
Biomass Conversion and Biorefinery     Partially Free   (Followers: 10)
Brazilian Journal of Chemical Engineering     Open Access   (Followers: 3)
Bulletin of Chemical Reaction Engineering & Catalysis     Open Access   (Followers: 2)
Bulletin of the Chemical Society of Ethiopia     Open Access   (Followers: 2)
Carbohydrate Polymers     Hybrid Journal   (Followers: 8)
Catalysts     Open Access   (Followers: 8)
ChemBioEng Reviews     Full-text available via subscription   (Followers: 1)
Chemical and Engineering News     Free   (Followers: 13)
Chemical and Materials Engineering     Open Access   (Followers: 13)
Chemical and Petroleum Engineering     Hybrid Journal   (Followers: 13)
Chemical and Process Engineering     Open Access   (Followers: 27)
Chemical and Process Engineering Research     Open Access   (Followers: 24)
Chemical Engineering & Technology     Hybrid Journal   (Followers: 32)
Chemical Engineering and Processing: Process Intensification     Hybrid Journal   (Followers: 17)
Chemical Engineering and Science     Open Access   (Followers: 19)
Chemical Engineering Communications     Hybrid Journal   (Followers: 14)
Chemical Engineering Education     Full-text available via subscription  
Chemical Engineering Journal     Hybrid Journal   (Followers: 45)
Chemical Engineering Research and Design     Hybrid Journal   (Followers: 23)
Chemical Engineering Research Bulletin     Open Access   (Followers: 12)
Chemical Engineering Science     Hybrid Journal   (Followers: 27)
Chemical Geology     Hybrid Journal   (Followers: 20)
Chemical Papers     Hybrid Journal   (Followers: 2)
Chemical Product and Process Modeling     Hybrid Journal   (Followers: 4)
Chemical Reviews     Full-text available via subscription   (Followers: 186)
Chemical Society Reviews     Full-text available via subscription   (Followers: 41)
Chemical Technology     Open Access   (Followers: 16)
ChemInform     Hybrid Journal   (Followers: 8)
Chemistry & Industry     Hybrid Journal   (Followers: 5)
Chemistry Central Journal     Open Access   (Followers: 4)
Chemistry of Materials     Full-text available via subscription   (Followers: 258)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
Chinese Chemical Letters     Full-text available via subscription   (Followers: 2)
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   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 12)
Computer Aided Chemical Engineering     Full-text available via subscription   (Followers: 1)
Computers & Chemical Engineering     Hybrid Journal   (Followers: 9)
CORROSION     Full-text available via subscription   (Followers: 21)
Corrosion Engineering, Science and Technology     Hybrid Journal   (Followers: 37)
Corrosion Reviews     Hybrid Journal   (Followers: 6)
Crystal Research and Technology     Hybrid Journal   (Followers: 6)
Current Opinion in Chemical Engineering     Open Access   (Followers: 7)
Designed Monomers and Polymers     Open Access   (Followers: 2)
Education for Chemical Engineers     Hybrid Journal   (Followers: 5)
Eksergi     Open Access  
Emerging Trends in Chemical Engineering     Full-text available via subscription   (Followers: 3)
European Polymer Journal     Hybrid Journal   (Followers: 41)
Fibers and Polymers     Full-text available via subscription   (Followers: 6)
Fluorescent Materials     Open Access   (Followers: 1)
Focusing on Modern Food Industry     Open Access   (Followers: 2)
Frontiers of Chemical Science and Engineering     Hybrid Journal   (Followers: 2)
Gels     Open Access  
Geochemistry International     Hybrid Journal   (Followers: 2)
Handbook of Powder Technology     Full-text available via subscription   (Followers: 6)
Heat Exchangers     Open Access   (Followers: 3)
High Performance Polymers     Hybrid Journal   (Followers: 1)
Hungarian Journal of Industry and Chemistry     Open Access  
Indian Chemical Engineer     Hybrid Journal   (Followers: 5)
Indian Journal of Chemical Technology (IJCT)     Open Access   (Followers: 10)
Indonesian Journal of Chemical Science     Open Access   (Followers: 1)
Industrial & Engineering Chemistry     Full-text available via subscription   (Followers: 11)
Industrial & Engineering Chemistry Research     Full-text available via subscription   (Followers: 21)
Industrial Chemistry Library     Full-text available via subscription   (Followers: 3)
Industrial Gases     Open Access  
Info Chimie Magazine     Full-text available via subscription   (Followers: 3)
International Journal of Chemical and Petroleum Sciences     Open Access   (Followers: 3)
International Journal of Chemical Engineering     Open Access   (Followers: 7)
International Journal of Chemical Reactor Engineering     Hybrid Journal   (Followers: 3)
International Journal of Chemical Technology     Open Access   (Followers: 5)
International Journal of Chemoinformatics and Chemical Engineering     Full-text available via subscription   (Followers: 2)
International Journal of Food Science     Open Access   (Followers: 3)
International Journal of Industrial Chemistry     Open Access   (Followers: 1)
International Journal of Polymeric Materials     Hybrid Journal   (Followers: 6)
International Journal of Waste Resources     Open Access   (Followers: 4)
Journal of Chemical Engineering & Process Technology     Open Access   (Followers: 5)
Journal of Applied Crystallography     Hybrid Journal   (Followers: 6)
Journal of Applied Electrochemistry     Hybrid Journal   (Followers: 14)
Journal of Applied Polymer Science     Hybrid Journal   (Followers: 134)
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: 11)
Journal of Chemical and Biological Interfaces     Full-text available via subscription   (Followers: 1)
Journal of Chemical Ecology     Hybrid Journal   (Followers: 7)
Journal of Chemical Engineering     Open Access   (Followers: 20)
Journal of Chemical Engineering and Materials Science     Open Access   (Followers: 2)
Journal of Chemical Science and Technology     Open Access   (Followers: 5)
Journal of Chemical Sciences     Partially Free   (Followers: 22)
Journal of Chemical Technology & Biotechnology     Hybrid Journal   (Followers: 10)
Journal of Chemical Theory and Computation     Full-text available via subscription   (Followers: 15)
Journal of CO2 Utilization     Hybrid Journal   (Followers: 2)
Journal of Combinatorial Chemistry     Full-text available via subscription   (Followers: 1)
Journal of Crystallization Process and Technology     Open Access   (Followers: 8)
Journal of Environmental Chemical Engineering     Hybrid Journal   (Followers: 7)
Journal of Food Measurement and Characterization     Hybrid Journal  
Journal of Food Processing & Technology     Open Access   (Followers: 1)
Journal of Fuel Chemistry and Technology     Full-text available via subscription   (Followers: 4)
Journal of Geochemical Exploration     Hybrid Journal   (Followers: 1)
Journal of Industrial and Engineering Chemistry     Hybrid Journal   (Followers: 1)
Journal of Information Display     Hybrid Journal   (Followers: 1)
Journal of Inorganic and Organometallic Polymers and Materials     Partially Free   (Followers: 9)
Journal of Modern Chemistry & Chemical Technology     Full-text available via subscription   (Followers: 2)
Journal of Molecular Catalysis A: Chemical     Hybrid Journal   (Followers: 6)
Journal of Non-Crystalline Solids     Hybrid Journal   (Followers: 8)
Journal of Organic Semiconductors     Open Access   (Followers: 5)
Journal of Physics and Chemistry of Solids     Hybrid Journal   (Followers: 5)
Journal of Polymer and Biopolymer Physics Chemistry     Open Access   (Followers: 6)
Journal of Polymer Engineering     Hybrid Journal   (Followers: 9)
Journal of Polymer Research     Hybrid Journal   (Followers: 6)
Journal of Polymer Science Part C : Polymer Letters     Hybrid Journal   (Followers: 6)
Journal of Polymers     Open Access   (Followers: 6)
Journal of Polymers and the Environment     Hybrid Journal   (Followers: 1)
Journal of Pure and Applied Chemistry Research     Open Access   (Followers: 2)
Journal of the American Chemical Society     Full-text available via subscription   (Followers: 308)
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: 2)
Journal of the Pakistan Institute of Chemical Engineers     Open Access   (Followers: 1)
Journal of the Taiwan Institute of Chemical Engineers     Hybrid Journal   (Followers: 2)
Journal of Water Chemistry and Technology     Hybrid Journal   (Followers: 9)
Jurnal Bahan Alam Terbarukan     Open Access  
Jurnal Inovasi Pendidikan Kimia     Open Access   (Followers: 5)
Jurnal Reaktor     Open Access  
Jurnal Rekayasa Kimia & Lingkungan     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: 2)
Materials Chemistry and Physics     Full-text available via subscription   (Followers: 16)
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  
Nanochemistry Research     Open Access  
Nanocontainers     Open Access   (Followers: 1)
Nanofabrication     Open Access  
Noise Control Engineering Journal     Full-text available via subscription   (Followers: 4)
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: 3)
Plasmas and Polymers     Hybrid Journal  
Polymer     Hybrid Journal   (Followers: 155)
Polymer Bulletin     Hybrid Journal   (Followers: 7)
Polymer Composites     Hybrid Journal   (Followers: 16)
Polyolefins Journal     Open Access  
Powder Technology     Hybrid Journal   (Followers: 13)
Recyclable Catalysis     Open Access   (Followers: 1)
Research on Chemical Intermediates     Hybrid Journal  
Reviews in Chemical Engineering     Hybrid Journal   (Followers: 5)
Revista ION     Open Access  
Revista Mexicana de Ingeniería Química     Open Access  
Rubber Chemistry and Technology     Full-text available via subscription   (Followers: 2)
Russian Chemical Bulletin     Hybrid Journal   (Followers: 2)
Russian Journal of Applied Chemistry     Hybrid Journal   (Followers: 1)
Science and Engineering of Composite Materials     Hybrid Journal   (Followers: 61)
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: 6)
Sustainable Chemical Processes     Open Access   (Followers: 2)
Synthesis Lectures on Chemical Engineering and Biochemical Engineering     Full-text available via subscription  
The Canadian Journal of Chemical Engineering     Hybrid Journal   (Followers: 4)
The Chemical Record     Hybrid Journal   (Followers: 1)
Theoretical Foundations of Chemical Engineering     Hybrid Journal   (Followers: 2)
Transition Metal Chemistry     Hybrid Journal   (Followers: 4)
Transylvanian Review of Systematical and Ecological Research     Open Access  
Visegrad Journal on Bioeconomy and Sustainable Development     Open Access   (Followers: 2)
Zeitschrift für Naturforschung B : A Journal of Chemical Sciences     Open Access   (Followers: 1)

           

Journal Cover Computational Biology and Chemistry
  [SJR: 0.491]   [H-I: 47]   [12 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1476-9271
   Published by Elsevier Homepage  [3043 journals]
  • Predicting lysine glycation sites using bi-profile bayes feature
           extraction
    • Abstract: Publication date: Available online 12 October 2017
      Source:Computational Biology and Chemistry
      Author(s): Zhe Ju, Juhe Sun, Yanjie Li, Li Wang
      Glycation is a nonenzymatic post-translational modification which has been found to be involved in various biological processes and closely associated with many metabolic diseases. The accurate identification of glycation sites is important to understand the underlying molecular mechanisms of glycation. As the traditional experimental methods are often labor-intensive and time-consuming, it is desired to develop computational methods to predict glycation sites. In this study, a novel predictor named BPB_GlySite is proposed to predict lysine glycation sites by using bi-profile bayes feature extraction and support vector machine algorithm. As illustrated by 10-fold cross-validation, BPB_GlySite achieves a satisfactory performance with a Sensitivity of 63.68%, a Specificity of 72.60%, an Accuracy of 69.63% and a Matthew’s correlation coefficient of 0.3499. Experimental results also indicate that BPB_GlySite significantly outperforms three existing glycation sites predictors: NetGlycate, PreGly and Gly-PseAAC. Therefore, BPB_GlySite can be a useful bioinformatics tool for the prediction of glycation sites. A user-friendly web-server for BPB_GlySite is established at 123.206.31.171/BPB_GlySite/.
      Graphical abstract image

      PubDate: 2017-10-14T13:23:45Z
       
  • Ligand-Based Computational Modelling of Platelet-Derived Growth Factor
           Beta Receptor Leading to New Angiogenesis Inhibitory Leads
    • Abstract: Publication date: Available online 10 October 2017
      Source:Computational Biology and Chemistry
      Author(s): Rua’a A Al-Aqtash, Malek A. Zihlif, Hana Hammad, Zeyad D. Nassar, Jehad Al Meliti, Mutasem O. Taha
      Platelet derived growth factor beta receptor (PDGFR- β) plays an important role in angiogenesis. PDGFR-β expression is correlated with increased vascularity and maturation of blood vessels in cancer. Pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis were combined to explore the structural requirements for ligand-PDGFR-β recognition using 107 known PDGFR-β inhibitors. Genetic function algorithm (GFA) coupled to k nearest neighbor (kNN) and multiple linear regression (MLR) analysis were employed to generate predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. The successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. The QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of new angiogenesis inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. Two hits illustrated low micromolar IC50 values in two distinct anti-angiogenesis bioassays.
      Graphical abstract image

      PubDate: 2017-10-11T13:19:28Z
       
  • 3D-QSAR studies on indole and 7-azoindole derivatives as ROCK-2
           inhibitors: An Integrative Computational Approach
    • Abstract: Publication date: Available online 9 October 2017
      Source:Computational Biology and Chemistry
      Author(s): Santhosh Kumar Nagarajan, Sathya Babu, Honglae Sohn, Thirumurthy Madhavan
      Rho Kinases (ROCK) has been found to regulate a wide range of fundamental cell functions such as contraction, motility, proliferation, and apoptosis. Recent experiments have defined new functions of ROCKs in cells, including centrosome positioning and cell-size regulation, which might contribute to various physiological and pathological states. In this study, we have performed pharmacophore modeling and 3D QSAR studies on a series of 36 indoles and 7-azoindoles derivatives as ROCK2 inhibitors to elucidate the structural variations with their inhibitory activities. Ligand based CoMFA and CoMSIA models were generated based on three different alignment methods such as systematic search, simulated annealing and pharmacophore. A total of 15 CoMFA models and 27 CoMSIA were generated using different alignments. One model from each alignment is selected based on the statistical values. Contour maps of the selected models were compared, analyzed and reported. The 3D QSAR study revealed that electro positive group linked to the methoxy-benzene ring position of the structure will enhance the biological activity and bulkier substitutions are preferred in the methyl dihydroindole region. Also, it is found that the hydrogen bond donor substituted at the R1 position enhances the inhibitory activity. In future, this study would give proper guidelines to further enhance the activity of novel inhibitors for ROCK2.
      Graphical abstract image

      PubDate: 2017-10-11T13:19:28Z
       
  • HashGO: Hashing Gene Ontology for protein function prediction
    • Abstract: Publication date: Available online 4 October 2017
      Source:Computational Biology and Chemistry
      Author(s): Guoxian Yu, Yingwen Zhao, Chang Lu, Jun Wang
      Gene Ontology (GO) is a standardized and controlled vocabulary of terms that describe the molecular functions, biological roles and cellular locations of proteins. GO terms and GO hierarchy are regularly updated as the accumulated biological knowledge. More than 50,000 terms are included in GO and each protein is annotated with several or dozens of these terms. Therefore, accurately predicting the association between proteins and massive GO terms is rather challenging. To accurately predict the association between massive GO terms and proteins, we proposed a method called Hashing GO for protein function prediction (HashGO in short). HashGO firstly adopts a protein-term association matrix to store available GO annotations of proteins. Then, it tailors a graph hashing method to explore the underlying structure between GO terms and to obtain a series of hash functions to compress the high-dimensional protein-term association matrix into a low-dimensional one. Next, HashGO computes the semantic similarity between proteins based on Hamming distance on that low-dimensional matrix. After that, it predicts missing annotations of a protein based on the annotations of its semantic neighbors. Experimental results on archived GO annotations of two model species (Yeast and Human) show that HashGO not only more accurately predicts functions than other related approaches, but also runs faster than them.

      PubDate: 2017-10-11T13:19:28Z
       
  • IFC Editorial Board
    • Abstract: Publication date: October 2017
      Source:Computational Biology and Chemistry, Volume 70


      PubDate: 2017-10-02T13:11:51Z
       
  • Title page
    • Abstract: Publication date: October 2017
      Source:Computational Biology and Chemistry, Volume 70


      PubDate: 2017-10-02T13:11:51Z
       
  • Predicting microRNA Biological Functions Based on Genes Discriminant
           Analysis
    • Abstract: Publication date: Available online 29 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Tao Ding, Junhua Xu, Mengmeng Sun, Shanshan Zhu, Jie Gao
      Although thousands of microRNAs (miRNAs) have been identified in recent experimental efforts, it remains a challenge to explore their specific biological functions through molecular biological experiments. Since those members from same family share same or similar biological functions, classifying new miRNAs into their corresponding families will be helpful for their further functional analysis. In this study, we initially built a vector space by characterizing the features from miRNA sequences and structures according to their miRBase family organizations. Then we further assigned miRNAs into its specific miRNA families by developing a novel genes discriminant analysis (GDA) approach in this study. As can be seen from the results of new families from GDA, in each of these new families, there was a high degree of similarity among all members of nucleotide sequences. At the same time, we employed 10-fold cross-validation machine learning to achieve the accuracy rates of 68.68%, 80.74%, and 83.65% respectively for the original miRNA families with no less than two, three, and four members. The encouraging results suggested that the proposed GDA could not only provide a support in identifying new miRNAs’ families, but also contributing to predicting their biological functions.

      PubDate: 2017-10-02T13:11:51Z
       
  • Assessment of in vivo organ-uptake and in silico prediction of CYP
           mediated metabolism of DA-Phen, a new dopaminergic agent.
    • Abstract: Publication date: Available online 28 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Flavia Maria Sutera, Libero Italo Giannola, Denise Murgia, Viviana De Caro
      The drug development process strives to predict metabolic fate of a drug candidate, together with its uptake in major organs, whether they act as target, deposit or metabolism sites, to the aim of establish a relationship between the pharmacodynamics and the pharmacokinetics and highlight the potential toxicity of the drug candidate. The present study was aimed at evaluating the in vivo uptake of 2-Amino-N-[2-(3,4-dihydroxy-phenyl)-ethyl]-3-phenyl-propionamide (DA-Phen) − a new dopaminergic neurotransmission modulator, in target and non-target organs of animal subjects and integrating these data with SMARTCyp results, an in silico method that predicts the sites of cytochrome P450-mediated metabolism of drug-like molecules. Wistar rats, subjected to two different behavioural studies in which DA-Phen was intraperitoneally administrated at a dose equal to 0.03mmol/kg, were sacrificed after the experimental protocols and their major organs were analysed to quantify the drug uptake. The data obtained were integrated with in silico prediction of potential metabolites of DA-Phen using the SmartCYP predictive tool. DA-Phen reached quantitatively the Central Nervous System and the results showed that the amide bond of the DA-Phen is scarcely hydrolysed as it was found intact in analyzed organs. As a consequence, it is possible to assume that DA-Phen acts as dopaminergic modulator per se and not as a Dopamine prodrug, thus avoiding peripheral release and toxic side effects due to the endogenous neurotransmitter. Furthermore the identification of potential metabolites related to biotransformation of the drug candidate leads to a more careful evaluation of the appropriate route of administration for future intended therapeutic aims and potential translation into clinical studies.
      Graphical abstract image

      PubDate: 2017-10-02T13:11:51Z
       
  • Solving probability reasoning based on DNA strand displacement and
           probability modules
    • Abstract: Publication date: Available online 28 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Qiang Zhang, Xiaobiao Wang, Xiaojun Wang, Changjun Zhou
      In computation biology, DNA strand displacement technology is used to simulate the computation process and has shown strong computing ability. Most researchers use it to solve logic problems, but it is only rarely used in probabilistic reasoning. To process probabilistic reasoning, a conditional probability derivation model and total probability model based on DNA strand displacement were established in this paper. The models were assessed through the game “read your mind.” It has been shown to enable the application of probabilistic reasoning in genetic diagnosis.

      PubDate: 2017-10-02T13:11:51Z
       
  • Prediction of new chromene-based inhibitors of tubulin using
           structure-based virtual screening and molecular dynamics simulation
           methods
    • Abstract: Publication date: Available online 27 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Hassan Aryapour, Maryam Dehdab, Farzin Sohraby, Afshar Bargahi
      Multidrug resistance (MDR) is one of the serious problems in cancer research that causes failure in chemotherapy. Chromene-based compounds have been proven to be the novel anti-MDR agents for inhibiting proliferation of tumor cells through tubulin polymerization inhibition of by binding at the colchicine binding site. In this study, we screened a chromene-based database of small molecules using physicochemical, ADMET properties and molecular docking to identify potential hit compounds. In order to validate our hit compounds, molecular dynamics simulations and related analysis were carried out and the results suggest that our hit compounds (PubChem CIDs: 16814409, 17594471, 57367244 and 69899719) can prove to be potential inhibitors of tubulin. The in silico results show that the present hits, like colchicine, effectively suppressed the dynamic instability of microtubules and induced microtubule-depolymerization and cell cycle arrest.
      Graphical abstract image

      PubDate: 2017-10-02T13:11:51Z
       
  • Signaling pathway impact analysis by incorporating the importance and
           specificity of genes (SPIA-IS)
    • Abstract: Publication date: Available online 27 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Hongyuan Fang, Xianbin Li, Xiangzhen Zan, Liangzhong Shen, Runnian Ma, Wenbin Liu
      rlying biology of differentially expressed genes and proteins. Although various approaches have been proposed to identify cancer-related pathways, most of them only partially consider the influence of those differentially expressed genes, such as the gene numbers, their perturbation in the signaling transduction, and the interaction between genes. Signaling-pathway impact analysis (SPIA) provides a convenient framework which considers both the classical enrichment analysis and the actual perturbation on a given pathway. In this study, we extended previous proposed SPIA by incorporating the importance and specificity of genes (SPIA-IS). We applied this approach to six datasets for colorectal cancer, lung cancer, and pancreatic cancer. Results from these datasets showed that the proposed SPIA-IS could effectively improve the performance of the original SPIA in identifying cancer-related pathways.

      PubDate: 2017-10-02T13:11:51Z
       
  • QSAR study of pyrazolo[4,3-e][1,2,4]triazine sulfonamides against
           tumor-associated human carbonic anhydrase isoforms IX and XII
    • Abstract: Publication date: Available online 21 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Joanna Matysiak, Alicja Skrzypek, Paweł Tarasiuk, Mariusz Mojzych
      The QSAR models for a set of pyrazolo[4,3-e][1,2,4]triazines incorporating benzenesulfonamide moiety combined directly with the heterocyclic ring or by NH linkage were generated. The inhibitory potency of compounds against human carbonic anhydrase isoforms IX and XII and antiproliferative activity against human MCF-7 cells were used as the dependent variables. The Codessa pro software was used for the descriptors calculation and the Best Multi-Linear Regression (BMLR) algorithm was employed to build the QSAR models. It was found that quantum descriptors are critical of the compounds activities. The selected models have good predictive accuracy confirmed by a set of the statistical quantities recommended by OECD.
      Graphical abstract image

      PubDate: 2017-09-25T19:33:45Z
       
  • In silico investigation of propofol binding sites in human serum albumin
           using explicit and implicit solvation models
    • Abstract: Publication date: October 2017
      Source:Computational Biology and Chemistry, Volume 70
      Author(s): Sergey Shityakov, Norbert Roewer, Carola Förster, Jens-Albert Broscheit
      All-atom molecular dynamics (MD) simulations are presented on general anesthetic propofol bound to human serum albumin (HSA) due to the drug pharmacokinetics and pharmacodynamics in the circulatory system. We implemented the explicit and implicit solvation models to compare the binding affinity of propofol at the different binding sites (PR1 and PR2) in the HSA protein. Only the implicit solvation models provided the evidence in accordance with the experimental data indicating that the HSA-ligand interactions are dominanted by hydrophobic forces due to the higher drug affinity at the PR1 position with a ΔGMM-PB/SA value of −23.44kcalmol−1. Overall, this study provides important information on the accuracy of explicit and implicit solvation models to characterize the propofol interaction with different HSA binding sites.
      Graphical abstract image

      PubDate: 2017-09-19T19:23:52Z
       
  • Factors analysis of protein O-glycosylation site prediction
    • Abstract: Publication date: Available online 18 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Xuemei Yang, Henry Han
      To improve the prediction accuracy of O-glycosylation sites, and analyze the structure of the O-glycosylation sites, factor analysis based prediction is proposed in this study. Our studies show that factor analysis strongly boosts machine learning algorithms’ performance in glycosylation site prediction besides demonstrates advantages compared to principal component analysis and nonnegative matrix factorization. In addition, we have found that factor analysis based linear discriminant analysis seem to be a desirable method in O-glycosylation site prediction for its advantage in both accuracy and time complexity than other machine learning methods. To the best of our knowledge, it is the first work to employ factor analysis in glycosylation site prediction and will inspire more future work in this topic.

      PubDate: 2017-09-19T19:23:52Z
       
  • In silico 3-D structure prediction and molecular docking studies of
           Inosine monophosphate dehydrogenase from Plasmodium falciparum
    • Abstract: Publication date: Available online 15 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Muslim Raza, Zahid Khan, Aftab Ahmad, Saleem Raza, Ajab Khan, Imdad Ullah Mohammadzai, Shah Zada
      Growing resistance in malarial parasites, particularly in Plasmodium falciparum needs a serious search for the discovery of novel drug targets. Inosine monophosphate dehydrogenase (IMPDH) is an important target for antimalarial drug discovery process in P. falciparum for the treatment of malaria. In the absence of x-ray crystal structure of this enzyme, homology modeling proved to be a reasonable alternate to study substrate binding mechanisms of this enzyme. In this study, a 3-D homology model for P. falciparum IMPDH was constructed taking human IMPDH (PDB code 1NF7) as template. Furthermore, an in-silico combinatorial library of ribavirin (RVP) derivatives (1347 molecules) was designed and virtually screened for ligands having selectively greater binding affinity with Plasmodium falciparum IMPDH relative to human IMPDH II. A total of five Ribavirin derivatives were identified as having greater binding affinity (-126 to −108 Kcal/mol and −9.4 to −8.6 Kcal/mol) with Plasmodium falciparum IMPDH. These five inhibitors should be used as selective and potent for Plasmodium falciparum IMPDH. Such type of study will provide information to synthetic medicinal chemist to enhance the potential of compounds (RVP derivatives) as chemotherapeutic agents to fight against the increasing burden of malarial infections.
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      PubDate: 2017-09-19T19:23:52Z
       
  • Identification and functional prediction of stress responsive AP2/ERF
           transcription factors in Brassica napus by genome-wide analysis
    • Abstract: Publication date: Available online 14 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Hajar Owji, Ali Hajiebrahimi, Hassan Seradj, Shiva Hemmati
      Using homology and domain authentication, 321 putative AP2/ERF transcription factors were identified in Brassica napus, called BnAP2/ERF TFs. BnAP2/ERF TFs were classified into five major subfamilies, including DREB, ERF, AP2, RAV, and BnSoloist. This classification is based on phylogenetic analysis, motif identification, gene structure analysis, and physiochemical characterization. These TFs were annotated based on phylogenetic relationship with Brassica rapa. BnAP2/ERF TFs were located on 19 chromosomes of B. napus. Orthologs and paralogs were identified using synteny-based methods Ks calculation within B. napus genome and between B. napus with other species such as B. rapa, Brassica oleracea, and Arabidopsis thaliana indicated that BnAP2/ERF TFs were formed through duplication events occurred before B. napus formation. Kn/Ks values were between 0-1, suggesting the purifying selection among BnAP2/ERF TFs. Gene ontology annotation, cis-regulatory elements and functional interaction networks suggested that BnAP2/ERF TFs participate in response to stressors, including drought, high salinity, heat and cold as well as developmental processes particularly organ specification and embryogenesis. The identified cis-regulatory elements in the upstream of BnAP2/ERF TFs were responsive to abscisic acid. Analysis of the expression data derived from Illumina Hiseq 2000 RNA sequencing revealed that BnAP2/ERF genes were highly expressed in the roots comparing to flower buds, leaves, and stems. Also, the ERF subfamily was over-expressed under salt and fungal treatments. BnERF039 and BnERF245 are candidates for salt-tolerant B. napus. BnERF253-256 and BnERF260-277 are potential cytokinin response factors. BnERF227, BnERF228, BnERF234, BnERF134, BnERF132, BnERF176, and BnERF235 were suggested for resistance against Leptosphaeria maculan and Leptosphaeria biglobosa.
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      PubDate: 2017-09-19T19:23:52Z
       
  • An investigation of novel traditional Chinese medicine formula for
           management of acute skin inflammation in silico
    • Abstract: Publication date: Available online 14 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Hsin-Chieh Tang, Hung-Jin Huang, Cheng-Chun Lee, Calvin Yu Chian Chen
      Matrix metalloproteinase-9 (MMP-9) appears to play an important role in acute skin inflammation. Subantimicrobial dose of tetracycline has been demonstrated to inhibit the activity of MMP-9 protein. However, long-term use tetracycline will induce side effect. The catalytic site of MMP-9 is located at zinc-binding amino acids, His401, His405 and His411. We attempted to search novel medicine formula as MMP-9 inhibitors from traditional Chinese medicine (TCM) database by using in silico studies. We utilized high-throughput virtual screening to find which natural compounds could bind to the zinc-binding site. The quantitative structure-activity relationship (QSAR) models, which constructed by scaffold of MMP-9 inhibitors and its activities, were employed to predict the bio-activity of the natural compounds for MMP-9. The results showed that Celacinnine, Lobelanidine and Celallocinnine were qualified to interact with zinc-binding site and displayed well predictive activity. We found that Celallocinnine was the best TCM compound for zinc binging sites of MMP-9 because the stable interactions were observed under dynamic condition. In addition, Celacinnine and Lobelanidine could interact with MMP-9 related protein that identified by drug-target interaction network analysis. Thus, we suggested the herbs Hypericum patulum, Sedum acre, and Tripterygium wilfordii that containing Celallocinnine, Celacinnine and Lobelanidine might be a novel medicine formula to avoid the side effect of tetracycline and increase the efficacy of treatment.
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      PubDate: 2017-09-19T19:23:52Z
       
  • NFκB pathway analysis: an approach to analyze gene co-expression networks
           employing feedback cycles
    • Abstract: Publication date: Available online 14 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Fabiane Cristine Dillenburg, Alfeu Zanotto-Filho, José Cláudio Fonseca Moreira, Leila Ribeiro, Luigi Carro
      The genes of the NFκB pathway are involved in the control of a plethora of biological processes ranking from inhibition of apoptosis to metastasis in cancer. It has been described that Gliobastoma multiforme (GBM) patients carry aberrant NFκB activation, but the molecular mechanisms are not completely understood. Here, we present a NFκB pathway analysis in tumor specimens of GBM compared to non-neoplasic brain tissues, based on the different kind of cycles found among genes of a gene co-expression network constructed using quantized data obtained from the microarrays. A cycle is a closed walk with all vertices distinct (except the first and last). Thanks to this way of finding relations among genes, a more robust interpretation of gene correlations is possible, because the cycles are associated with feedback mechanisms, that are very common in biological networks. In GBM samples, we could conclude that the stoichiometric relationship between genes involved in NFκB pathway regulation is unbalanced. This can be measured and explained by the identification of a cycle. This conclusion helps to understand more about the biology of this type of tumor.

      PubDate: 2017-09-19T19:23:52Z
       
  • Pharmacophore modeling, virtual screening and molecular docking of ATPase
           inhibitors of HSP70
    • Abstract: Publication date: October 2017
      Source:Computational Biology and Chemistry, Volume 70
      Author(s): K. Sangeetha, R.P. Sasikala, K.S. Meena
      Heat shock protein 70 is an effective anticancer target as it influences many signaling pathways. Hence the study investigated the important pharmacophore feature required for ATPase inhibitors of HSP70 by generating a ligand based pharmacophore model followed by virtual based screening and subsequent validation by molecular docking in Discovery studio V4.0. The most extrapolative pharmacophore model (hypotheses 8) consisted of four hydrogen bond acceptors. Further validation by external test set prediction identified 200 hits from Mini Maybridge, Drug Diverse, SCPDB compounds and Phytochemicals. Consequently, the screened compounds were refined by rule of five, ADMET and molecular docking to retain the best competitive hits. Finally Phytochemical compounds Muricatetrocin B, Diacetylphiladelphicalactone C, Eleutheroside B and 5-(3-{[1-(benzylsulfonyl)piperidin-4-yl]amino}phenyl)- 4-bromo-3-(carboxymethoxy)thiophene-2-carboxylic acid were obtained as leads to inhibit the ATPase activity of HSP70 in our findings and thus can be proposed for further in vitro and in vivo evaluation.
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      PubDate: 2017-09-13T21:10:45Z
       
  • Identification of Effective DNA Barcodes for Triticum Plants through
           Chloroplast Genome-wide Analysis
    • Abstract: Publication date: Available online 12 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Mohamed Awad, Ragab M. Fahmy, Kareem A. Mosa, Mohamed Helmy, Fawzy A. El-Feky
      The Egyptian flora is rich with a large number of Triticum plants, which are very difficult to discriminate between in the early developmental stages. This study assesses the significance of using two DNA Barcoding loci (matK and rbcL) in distinguishing between 18 different Triticum accessions in Egypt. We isolated and sequenced 15 rbcL and six matK fragments, but our analysis of the resultant sequences demonstrated a limited ability of matK and rbcL in distinguishing between Triticum accessions. Therefore, we pursued a bioinformatics approach to determine the most useful loci which may be used as DNA barcodes for the Triticum spp. We obtained the 10 available chloroplast genomes of the 10 Triticum species and sub-species from NCBI, and performed chloroplast genome-wide analysis to find the potential barcode loci. A total of 134 chloroplast genes, gene combinations, intergenic regions and intergenic region combinations were tested using a Tree-based method. We were unable to discriminate between Triticum species by using chloroplast genes, gene combinations and intergenic regions. However, a combination of the intergenic region (trnfM−trnT) with either (trnD−psbM), (petN−trnC), (matK−rps16) or (rbcL−psaI) demonstrated a very high discrimination capacity, suggesting their utilization as DNA barcodes for the Triticum plants. Furthermore, our novel DNA barcodes demonstrated high discrimination capacity for other Poaceae members.
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      PubDate: 2017-09-13T21:10:45Z
       
  • A physicochemical descriptor based method for effective and rapid
           screening of dual inhibitors against BACE-1 and GSK-3β as targets for
           Alzheimer’s disease.
    • Abstract: Publication date: Available online 8 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Akhil Kumar, Gaurava Srivastava, Ashok Sharma
      Due to multifactorial nature of Alzheimer’s disease one target-one ligand hypothesis often looks insufficient. BACE-1 and GSK-3β are well established therapeutic drug targets and interaction between BACE-1 and GSK-3β pathways has also been established. Thus, designing of dual inhibitor for these two targets seems rational and may provide effective therapeutic strategies against AD. Recent studies revealed that only two scaffolds i.e. triazinone and curcumin act as a dual inhibitor against BACE-1 and GSK-3β. Thus, this discovery set the path to screen new chemical entities from a vast chemical space (∼1060 compounds) that inhibit both the targets. However, small part of the large chemical space will only show biological activity for specific targets. Virtual screening of large libraries is impractical and computational expensive especially in case of dual inhibitor design. In the case of dual or multi target inhibitor designing, we screened the database for each target that further increases time and resources. In this study we have done physicochemical descriptor based profiling to know the biological relevant chemical space for BACE-1 and GSK-3β inhibitors and proposed the suitable range of important physicochemical properties, occurrence of functional groups. We generated scaffolds tree of known inhibitors of BACE-1 and GSK-3β suggesting the common structure/fragment that can be used to design dual inhibitors. This approach can filter the potential dual inhibitor candidates of BACE-1 and GSK-3β from non inhibitors.
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      PubDate: 2017-09-13T21:10:45Z
       
  • Development of an epitope-based vaccine inhibiting immune cells rolling
           and migration against atherosclerosis using in silico approaches
    • Abstract: Publication date: Available online 1 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Mehdi Tourani, Ahmad Karkhah, Ali Najafi
      Atherosclerosis is a chronic inflammatory disease characterized by formation of pro-oxidative lipids in large and medium-sized vessels. Over the years, many treatments and drugs have entered the market to improve atherosclerosis and autoantigen-mediated active immunization is currently considered as a beneficial method. Therefore, this study was conducted to design a novel epitope-based vaccine against atherosclerosis employing CD99, CD81 and CD99L2 antigens. In this way, structural vaccinology approaches were used to design a novel multi-epitope vaccine against atherosclerosis. Six epitopes were predicted from CD99, CD81 and CD99L2 proteins. In addition, helper epitopes selected from Tetanus toxin fragment C (TTFrC)ion were applied to induce CD4+ helper T lymphocytes (HTLs) responses. Moreover, cholera toxin B (CTB) was employed as an adjuvant. Finally, EAAAK AND GPGPG sequences as linkers were considered to make a linkage between favorite peptide sequences. A multi-epitope construction was designed based on the predicted epitopes which was 270 residues in length. Further immunoinformatic analyses were carried out to assess physicochemical properties, secondary and tertiary structures, stability, intrinsic protein disorder, solubility, and allergenicity of this chimeric protein. Based on the obtained results, a soluble, and non-allergic protein with a molecular weight of 28.7kDa was designed. Further analyses revealed that the chimeric protein is a stable protein and the predicted epitopes indicated strong potential to induce B-cell and T-cell mediated immune response. Our immunoinformatic analyses revealed that the modeled multi-epitope vaccine had appropriate properties,which can properly stimulate the immune responses of both T and B cells.
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      PubDate: 2017-09-01T20:47:19Z
       
  • Virtual Screening of B-Raf Kinase Inhibitors: A Combination of
           Pharmacophore Modelling, Molecular Docking, 3D-QSAR Model and Binding Free
           Energy Calculation Studies
    • Abstract: Publication date: Available online 31 August 2017
      Source:Computational Biology and Chemistry
      Author(s): Wen Zhang, Kai-Xiong Qiu, Fang Yu, Xiao-Guang Xie, Shu-Qun Zhang, Ya-Juan Chen, Hui-Ding Xie
      B-Raf kinase has been identified as an important target in recent cancer treatment. In order to discover structurally diverse and novel B-Raf inhibitors (BRIs), a virtual screening of BRIs against ZINC database was performed by using a combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy (ΔG bind) calculation studies in this work. After the virtual screening, six promising hit compounds were obtained, which were then tested for inhibitory activities of A375 cell lines. In the result, five hit compounds show good biological activities (IC50 <50μM). The present method of virtual screening can be applied to find structurally diverse inhibitors, and the obtained five structurally diverse compounds are expected to develop novel BRIs.
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      PubDate: 2017-09-01T20:47:19Z
       
  • Enzyme Classification using Multiclass Support Vector Machine and Feature
           Subset Selection
    • Abstract: Publication date: Available online 31 August 2017
      Source:Computational Biology and Chemistry
      Author(s): Debasmita Pradhan, Sudarsan Padhy, Biswajit Sahoo
      Proteins are the macromolecules responsible for almost all biological processes in a cell. With the availability of large number of protein sequences from different sequencing projects, the challenge with the scientist is to characterize their functions. As the wet lab methods are time consuming and expensive, many computational methods such as FASTA, PSI-BLAST, DNA microarray clustering, and Nearest Neighborhood classification on protein-protein interaction network have been proposed. Support vector machine is one such method that has been used successfully for several problems such as protein fold recognition, protein structure prediction etc. Cai et al. in 2003 has used SVM for classifying proteins into different functional classes and to predict their function. They used the physico-chemical properties of proteins to represent the protein sequences. In this paper a model comprising of feature subset selection followed by multiclass Support Vector Machine is proposed to determine the functional class of a newly generated protein sequence. To train and test the model for its performance, 32 physico-chemical properties of enzymes from 6 enzyme classes are considered. To determine the features that contribute significantly for functional classification, Sequential Forward Floating Selection (SFFS), Orthogonal Forward Selection (OFS) and SVM Recursive Feature Elimination (SVM-RFE) algorithms are used and it is observed that out of 32 properties considered initially, only 20 features are sufficient to classify the proteins into its functional classes with an accuracy ranging from 91% to 94%. On comparison it is seen that, OFS followed by SVM performs better than other methods. Our model generalizes the existing model to include multiclass classification and to identify most significant features affecting the protein function.
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      PubDate: 2017-09-01T20:47:19Z
       
  • Computational model for vitamin D deficiency using hair mineral analysis
    • Abstract: Publication date: Available online 30 August 2017
      Source:Computational Biology and Chemistry
      Author(s): Aboul Ella Hassanien, Alaa Tharwat, Hala S. Own
      Vitamin D deficiency is prevalent in the Arabian Gulf region, especially among women. Recent studies show that the vitamin D deficiency is associated with a mineral status of a patient. Therefore, it is important to assess the mineral status of the patient to reveal the hidden mineral imbalance associated with vitamin D deficiency. A well-known test such as the red blood cells is fairly expensive, invasive, and less informative. On the other hand, a hair mineral analysis can be considered an accurate, excellent, highly informative tool to measure mineral imbalance associated with vitamin D deficiency. In this study, 118 apparently healthy Kuwaiti women were assessed for their mineral levels and vitamin D status by a Hair Mineral Analysis (HMA). This information was used to build a computerized model that would predict vitamin D deficiency based on its association with the levels and ratios of minerals. The first phase of the proposed model introduces a novel hybrid optimization algorithm, which can be considered as an improvement of Bat Algorithm (BA) to select the most discriminative features. The improvement includes using the mutation process of Genetic Algorithm (GA) to update the positions of bats with the aim of speeding up convergence; thus, making the algorithm more feasible for wider ranges of real-world applications. Due to the imbalanced class distribution in our dataset, in the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling, and Synthetic Minority Oversampling Technique are used to solve the problem of imbalanced datasets. In the third phase, an AdaBoost ensemble classifier is used to predicting the vitamin D deficiency. The results showed that the proposed model achieved good results to detect the deficiency in vitamin D.

      PubDate: 2017-09-01T20:47:19Z
       
  • Inhibition of TRAF6-Ubc13 interaction in NFkB inflammatory pathway by
           analyzing the hotspot amino acid residues and protein–protein
           interactions using molecular docking simulations
    • Abstract: Publication date: Available online 30 August 2017
      Source:Computational Biology and Chemistry
      Author(s): Ria Biswas, Angshuman Bagchi
      Protein-protein interactions (PPIs) are important in most of the biochemical processes. Hotspot amino acid residues in proteins are the most important contributors for proper protein-protein interactions. Hotspot amino acid residues have been looked down upon as important therapeutic targets in inhibiting PPIs. Interaction between TRAF6 and Ubc13 is a crucial point in the NFkB inflammatory pathway. Dysfunction of the NFkB pathway is associated with numerous human diseases including cancer and neurodenegeration disorders. Ubc13 also interacts specifically to TRAF6 and not with other proteins of the TRAF family and this makes the TRAF6-Ubc13 complex an important target for specific inhibition. Hence, interfering with the TRAF6-Ubc13 association may prove effective in suppressing the NFkB disease pathway. In the present study, we searched the TRAF6-Ubc13 interaction interface to analyze their binding hotspot amino acid residues using various computational techniques. Heterocyclic compounds are known for their medicinal properties. We screened for heterocyclic analogues to the known TRAF6 inhibitor PDTC, to predict a better inhibitor using in silico protein-ligand and protein-protein interaction studies. Our in silico prediction results suggest that tetrahydro-2-thiophenecarbothioamide (Chemspider ID 36027528) binds one of the major hot-spot residues of TRAF6-Ubc13 interface and can be a better alternative in suppressing TRA6-Ubc13 complex formation in chronic inflammation than PDTC.
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      PubDate: 2017-09-01T20:47:19Z
       
  • Toward a generalized computational workflow for exploiting transient
           pockets as new targets for small molecule stabilizers: application to the
           homogentisate 1,2-dioxygenase mutants at the base of rare disease
           Alkaptonuria
    • Abstract: Publication date: Available online 25 August 2017
      Source:Computational Biology and Chemistry
      Author(s): Andrea Bernini, Silvia Galderisi, Ottavia Spiga, Giulia Bernardini, Neri Niccolai, Fabrizio Manetti, Annalisa Santucci
      Alkaptonuria (AKU) is an inborn error of metabolism where mutation of homogentisate 1,2-dioxygenase (HGD) gene leads to a deleterious or misfolded product with subsequent loss of enzymatic degradation of homogentisic acid (HGA) whose accumulation in tissues causes ochronosis and degeneration. There is no licensed therapy for AKU. Many missense mutations have been individuated as responsible for quaternary structure disruption of the native hexameric HGD. A new approach to the treatment of AKU is here proposed aiming to totally or partially rescue enzyme activity by targeting of HGD with pharmacological chaperones, i.e. small molecules helping structural stability. Co-factor pockets from oligomeric proteins have already been successfully exploited as targets for such a strategy, but no similar sites are present at HGD surface; hence, transient pockets are here proposed as a target for pharmacological chaperones. Transient pockets are detected along the molecular dynamics trajectory of the protein and filtered down to a set of suitable sites for structural stabilization by mean of biochemical and pharmacological criteria. The result is a computational workflow relevant to other inborn errors of metabolism requiring rescue of oligomeric, misfolded enzymes.
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      PubDate: 2017-09-01T20:47:19Z
       
  • Protein β-sheet Prediction Using an Efficient Dynamic Programming
           Algorithm
    • Abstract: Publication date: Available online 24 August 2017
      Source:Computational Biology and Chemistry
      Author(s): Mostafa Sabzekar, Mahmoud Naghibzadeh, Mahdie Eghdami, Zafer Aydin
      Predicting the β-sheet structure of a protein is one of the most important intermediate steps towards the identification of its tertiary structure. However, it is regarded as the primary bottleneck due to the presence of non-local interactions between several discontinuous regions in β-sheets. To achieve reliable long-range interactions, a promising approach is to enumerate and rank all β-sheet conformations for a given protein and find the one with the highest score. The problem with this solution is that the search space of the problem grows exponentially with respect to the number of β-strands. Additionally, brute-force calculation in this conformational space leads to dealing with a combinatorial explosion problem with intractable computational complexity. The main contribution of this paper is to generate and search the space of the problem efficiently to reduce the time complexity of the problem. To achieve this, two tree structures, called sheet-tree and grouping-tree, are proposed. They model the search space by breaking it into sub-problems. Then, an advanced dynamic programming is proposed that stores the intermediate results, avoids repetitive calculation by repeatedly uses them efficiently in successive steps and reduces the space of the problem by removing those intermediate results that will no longer be required in later steps. As a consequence, the following contributions have been made. Firstly, more accurate β-sheet structures are found by searching all possible conformations, and secondly, the time complexity of the problem is reduced by searching the space of the problem efficiently which makes the proposed method applicable to predict β-sheet structures with high number of β-strands. Experimental results on the BetaSheet916 dataset showed significant improvements of the proposed method in both execution time and the prediction accuracy in comparison with the state-of-the-art β-sheet structure prediction methods Moreover, we investigate the effect of different contact map predictors on the performance of the proposed method using BetaSheet1452 dataset. The source code is available at http://www.conceptsgate.com/BetaTop.rar.
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      PubDate: 2017-09-01T20:47:19Z
       
  • A ligand-based comparative molecular field analysis (CoMFA) and homology
           model based molecular docking studies on 3′, 4′-dihydroxyflavones as
           rat 5-lipoxygenase inhibitors: Design of new inhibitors
    • Abstract: Publication date: Available online 24 August 2017
      Source:Computational Biology and Chemistry
      Author(s): T.K.Shameera Ahamed, K. Muraleedharan
      In this study, ligand based comparative molecular field analysis (CoMFA) with five principal components was performed on class of 3′, 4′-dihydroxyflavone derivatives for potent rat 5-LOX inhibitors. The percentage contributions in building of CoMFA model were 91.36% for steric field and 8.6% for electrostatic field. R2 values for training and test sets were found to be 0.9320 and 0.8259, respectively. In case of LOO, LTO and LMO cross validation test, q2 values were 0.6587, 0.6479 and 0.5547, respectively. These results indicate that the model has high statistical reliability and good predictive power. The extracted contour maps were used to identify the important regions where the modification was necessary to design a new molecule with improved activity. The study has developed a homology model for rat 5-LOX and recognized the key residues at the binding site. Docking of most active molecule to the binding site of 5-LOX confirmed the stability and rationality of CoMFA model. Based on molecular docking results and CoMFA contour plots, new inhibitors with higher activity with respect to the most active compound in data set were designed.
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      PubDate: 2017-09-01T20:47:19Z
       
  • Biocomputational identification and validation of novel microRNAs
           predicted from Bubaline Whole Genome Shotgun Sequences
    • Abstract: Publication date: Available online 14 August 2017
      Source:Computational Biology and Chemistry
      Author(s): H.K. Manku, J.K. Dhanoa, S. Kaur, J.S. Arora, C.S. Mukhopadhyay
      MicroRNAs (miRNAs) are small (19-25 base long), non-coding RNAs that regulate post-transcriptional gene expression by cleaving targeted mRNAs in several eukaryotes. The miRNAs play vital roles in multiple biological and metabolic processes, including developmental timing, signal transduction, cell maintenance and differentiation, diseases and cancers. Experimental identification of microRNAs is expensive and lab-intensive. Alternatively, computational approaches for predicting putative miRNAs from genomic or exomic sequences rely on features of miRNAs viz. secondary structures, sequence conservation, minimum free energy index (MFEI) etc. To date, not a single miRNA has been identified in bubaline (Bubalus bubalis), which is an economically important livestock. The present study aims at predicting the putative miRNAs of buffalo using comparative computational approach from buffalo whole genome shotgun sequencing data (INSDC: AWWX00000000.1). The sequences were blasted against the known mammalian miRNA. The obtained miRNAs were then passed through a series of filtration criteria to obtain the set of predicted (putative and novel) bubaline miRNA. Eight miRNAs were selected based on lowest E-value and validated by real time PCR (SYBR green chemistry) using RNU6 as endogenous control. The results from different trails of real time PCR shows that out of selected 8 miRNAs, only 2 (hsa-miR-1277-5p; bta-miR-2285b) are not expressed in bubaline PBMCs. The potential target genes based on their sequence complementarities were then predicted using miRanda. This work is the first report on prediction of bubaline miRNA from whole genome sequencing data followed by experimental validation. The finding could pave the way to future studies in economically important traits in buffalo.

      PubDate: 2017-08-22T02:45:10Z
       
  • Deciphering the catalytic amino acid residues of L-2-haloacid Dehalogenase
           (DehL) from Rhizobium Sp. RC1: In silico analysis
    • Abstract: Publication date: Available online 14 August 2017
      Source:Computational Biology and Chemistry
      Author(s): Aliyu Adamu, Roswanira Abdul Wahab, Mohd Shahir Shamsir, Firdausi Aliyu, Fahrul Huyop
      L-2-haloacid dehalogenases (EC 3.8.1.2) are enzymes that catalyse the specific cleavage of carbon-halogen bond in L-isomers of halogenated organic acid compounds. These enzymes are gaining popularity among researchers for their potential applications in bioremediation and synthesis of various industrial products. To enhance the efficiency and utility of these enzymes, full details of their molecular reaction mechanism and identification of the important catalytic amino acid residues are required. Here, using ab initio fragment molecular orbital (FMO) calculations, classical molecular dynamic (MD) simulations and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations; we predict the catalytic amino acid residues of L-2-haloacid dehalogenase (DehL) from Rhizobium sp. RC1; and propose its molecular catalytic mechanism. We found that in silico replacements of ASP13, THR17, MET48, ARG51 and HIS184 with alanine significantly decreased the free binding energy of DehL-L-2CP complex, indicating their involvement in the catalytic mechanism. Further more, strong interfragment interacting energies (IFIEs) and stability in relative distances between ASP13 and L-2CP; and WT1 and HIS184 were observed; suggesting ASP13 acts as a nucleophile and HIS184 activates the catalytic water in DehL catalysis. The results obtained here will play a crucial role in identifying the potential molecular engineering targets in DehL for efficiency enhancement.
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      PubDate: 2017-08-22T02:45:10Z
       
  • Induction of senescence in cancer cells by 5′-Aza-2′-deoxycytidine:
           Bioinformatics and experimental insights to its targets
    • Abstract: Publication date: October 2017
      Source:Computational Biology and Chemistry, Volume 70
      Author(s): Jayarani F. Putri, Nashi Widodo, Kazuichi Sakamoto, Sunil C. Kaul, Renu Wadhwa
      5′-Aza-2′-deoxycytidine (5-Aza-dC) is a demethylating drug that causes genome-wide hypomethylation resulting in the expression of several tumor suppressor genes causing growth arrest of cancer cells. Cancer is well established as a multifactorial disease and requires multi-module therapeutics. Search for new drugs and their approval by FDA takes a long time. Keeping this in view, research on new functions of FDA-approved anticancer drugs is desired to expand the list of multi-module functioning drugs for cancer therapy. In this study, we conducted an analysis for new functions of 5-Aza-dC by applying bio-chemo-informatics approach. The potential of 5-Aza-dC bioactivity was analyzed by PASS online and Molinspiration. Target proteins were predicted by SuperPred. The protein networks and biological processes were analyzed by Biological Networks using Gene Ontology tool, BINGO, based on BIOGRID database. Interactions between 5-Aza-dC and targeted proteins were examined by Autodoc Vina integrated into pyrx software. Induction of p53 by 5-Aza-dC was tested in vitro using cancer cells. Bioinformatics analyses predicted that 5-Aza-dC functions as a p53 inducer, radiosensitizer, and inhibitor of some enzymes. It was predicted to target proteins including MDM2, POLA1, POLB, and CXCR4 that are involved in the induction of DNA damage response and p53-HDM2-p21 signaling. In this study, we provide experimental evidence showing HDM2 is one of the targets of 5-AZA-dC leading to activation of p53 pathway and growth arrest of cells. Furthermore, we found that the combinatorial treatment of 5-AZA-dC with three other drugs caused drug resistance. We discuss that 5-Aza-dC-induced senescence is a multi-module drug that controls cell proliferation phenotype not only by proteins but also by noncoding miRNAs. Further studies are warranted to dissect these mechanisms and establish 5-Aza-dC as an effective multi-module anticancer reagent.
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      PubDate: 2017-08-11T02:21:23Z
       
  • An in silico analysis of primary and secondary structure specificity
           determinants for human peptidylarginine deiminase types 2 and 4
    • Abstract: Publication date: Available online 9 August 2017
      Source:Computational Biology and Chemistry
      Author(s): Justin S. Olson, Joshua M. Lubner, Dylan J. Meyer, Jennifer E. Grant
      Human peptidylarginine deiminases (hPADs) are a family of five calcium-dependent enzymes that facilitate citrullination, which is the post-translational modification of peptidyl arginine to peptidyl citrulline. The isozymes hPAD2 and hPAD4 have been implicated in the development and progression of several autoimmune diseases, including rheumatoid arthritis and multiple sclerosis. To better characterize the primary and secondary structure determinants of citrullination specificity, we mined the literature for protein sequences susceptible to citrullination by hPAD2 or hPAD4. First, protein secondary structure classification (α-helix, β-sheet, or coil) was predicted using the PSIPRED software. Next, we used motif-x and pLogo to extract and visualize statistically significant motifs within each data set. Within the data sets of peptides predicted to lie in coil regions, both hPAD2 and hPAD4 appear to favor citrullination of glycine-containing motifs, while distinct hydrophobic motifs were identified for hPAD2 citrullination sites predicted to reside within α-helical and β-sheet regions. Additionally, we identified potential substrate overlap between coil region citrullination and arginine methylation. Together, these results confirm the importance and offer some insight into the role of secondary structure elements for citrullination specificity, and provide biological context for the existing hPAD specificity and arginine post-translational modification literature.
      Graphical abstract image

      PubDate: 2017-08-11T02:21:23Z
       
  • Molecular Dynamics-Assisted Pharmacophore Modeling of Caspase-3-Isatin
           Sulfonamide Complex: Recognizing Essential Intermolecular Contacts and
           Features of Sulfonamide Inhibitor Class for Caspase-3 Binding
    • Abstract: Publication date: Available online 9 August 2017
      Source:Computational Biology and Chemistry
      Author(s): Sivakumar Prasanth Kumar, Chirag N. Patel, Prakash C. Jha, Himanshu A. Pandya
      The identification of isatin sulfonamide as a potent small molecule inhibitor for caspase-3 had fuelled the synthesis and characterization of the numerous sulfonamide class of inhibitors to optimize for potency. Earlier works that relied on the ligand-based approaches have successfully shown the regions of optimizations for sulfonamide scaffold. We present here molecular dynamics-based pharmacophore modeling of caspase-3-isatin sulfonamide crystal structure to elucidate the essential non-covalent contacts and its associated pharmacophore features necessary to ensure caspase-3 binding. We performed 20ns long dynamics of this crystal structure to extract global conformation states which were rigorously validated using an exclusive focussed library of experimental actives and inactives of sulfonamide origin by Receiver Operating Characteristic (ROC) curves. Eighteen structure-based pharmacophore hypotheses were identified which constituted both better sensitivity and specificity (>0.6) and showed that Cys163 (S1 sub-site; required for covalent and H bonding with Michael acceptor), Gly122 (S1;H bond with carbonyl oxygen), His121(S1; π stack with bicyclic isatin moiety) and Tyr204 (S2; π stack with phenyl group of the isatin sulfonamide molecule) were stringent binding entities for enabling caspase-3 optimal binding. The introduction of spatially prioritized pharmacophores and scrutinized non-covalent interactions obtained from dynamics-based pharmacophore models in a suitable virtual screening strategy will be helpful to screen and optimize molecules belonging to sulfonamide class of caspase-3 inhibitors.
      Graphical abstract image

      PubDate: 2017-08-11T02:21:23Z
       
  • Functional contribution of coenzyme specificity-determining sites of
           7α-hydroxysteroid dehydrogenase from Clostridium absonum
    • Abstract: Publication date: Available online 9 August 2017
      Source:Computational Biology and Chemistry
      Author(s): Deshuai Lou, Yue Wang, Jun Tan, Liancai Zhu, Shunlin Ji, Bochu Wang
      Studies of the molecular determinants of coenzyme specificity help to reveal the structure-function relationship of enzymes, especially with regards to coenzyme specificity-determining sites (CSDSs) that usually mediate complex interactions. NADP(H)-dependent 7α-hydroxysteroid dehydrogenase from Clostridium absonum (CA 7α-HSDH), a member of the short-chain dehydrogenase/reductase superfamily (SDRs), possesses positively charged CSDSs that mainly contain T15, R16, R38, and R194, forming complicated polar interactions with the adenosine ribose C2 phosphate group of NADP(H). The R38 residue is crucial for coenzyme anchoring, but the influence of the other residues on coenzyme utilization is still not clear. Hence, we performed alanine scanning mutagenesis and molecular dynamic (MD) simulations. The results suggest that the natural CSDSs have the greatest NADP(H)-binding affinity, but not the best activity (k cat) toward NADP+. Compared with the wild type and other mutants, the mutant R194A showed the highest catalytic efficiency (k cat/K m), which was more than three-times that of the wild type. MD simulation and kinetics analysis suggested that the importance of the CSDSs of CA 7α-HSDH should be in accordance with the following order R38>T15>R16>R194, and S39 may have a supporting role in NADP(H) anchoring for mutants R16A/T194A and T15A/R16A/T194A.
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      PubDate: 2017-08-11T02:21:23Z
       
  • Exploration of cell cycle regulation and modulation of the DNA methylation
           mechanism of pelargonidin: insights from the molecular modeling approach
    • Abstract: Publication date: Available online 8 August 2017
      Source:Computational Biology and Chemistry
      Author(s): Natesan Karthi, Arumugasamy Karthiga, Thangaraj Kalaiyarasu, Antony Stalin, Vaiyapuri Manju, Sanjeev Kumar Singh, Ravi Cyril, Sang-Myeong Lee
      Pelargonidin is an anthocyanidin isolated from plant resources. It shows strong cytotoxicity toward various cancer cell lines, even though the carcinogenesis-modulating pathway of pelargonidin is not yet known. One of our previous reports showed that pelargonidin arrests the cell cycle and induces apoptosis in HT29 cells. Flowcytometry and immunoblot analysis confirmed that pelargonidin specifically inhibits the activation of CDK1 and blocks the G2-M transition of the cell cycle. In addition, DNA fragmentation was observed along with induction of cytochrome c release-mediated apoptosis. Hence, the aim of the present study was to investigate the molecular mechanism of pelargonidin’s action on cell cycle regulators CDK1, CDK4, and CDK6 as well as the substrate-binding domain of DNMT1 and DNMT3A, which regulate the epigenetic signals related to DNA methylation. The results of docking analysis, binding free energy calculation, and molecular dynamics simulation correlated with the experimental results, and pelargonidin showed a specific interaction with CDK1. In this context, pelargonidin may also inhibit the recognition of DNA and catalytic binding by DNMT1 and DNMT3A. The HOMO-LUMO analysis mapped the functional groups of pelargonidin. Prediction of pharmacological descriptors suggested that pelargonidin can serve as a multitarget inhibitor for cancer treatment.
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      PubDate: 2017-08-11T02:21:23Z
       
  • Insights into the immune manipulation mechanisms of pollen allergens by
           protein domain profiling
    • Abstract: Publication date: Available online 29 July 2017
      Source:Computational Biology and Chemistry
      Author(s): Seema Patel, Aruna Rani, Arun Goyal
      Plant pollens are airborne allergens, as their inhalation causes immune activation, leading to rhinitis, conjunctivitis, sinusitis and oral allergy syndrome. A myriad of pollen proteins belonging to profilin, expansin, polygalacturonase, glucan endoglucosidase, pectin esterase, and lipid transfer protein class have been identified. In the present in silico study, the protein domains of fifteen pollen sequences were extracted from the UniProt database and submitted to the interactive web tool SMART (Simple Modular Architecture Research Tool), for finding the protein domain profiles. Analysis of the data based on custom-made scripts revealed the conservation of pathogenic domains such as OmpH, PROF, PreSET, Bet_v_1, Cpl-7 and GAS2. Further, the retention of critical domains like CHASE2, Galanin, Dak2, DALR_1, HAMP, PWI, EFh, Excalibur, CT, PbH1, HELICc, and Kelch in pollen proteins, much like cockroach allergens and lethal viruses (such as HIV, HCV, Ebola, Dengue and Zika) was observed. Based on the shared motifs in proteins of taxonomically dispersed organisms, it can be hypothesized that allergens and pathogens manipulate the human immune system in a similar manner. Allergens, being inanimate, cannot replicate in human body and are neutralized by immune system. But, when the allergens are unremitting, the immune system becomes persistently hyper-sensitized, creating an inflammatory milieu. This study is expected to contribute to the understanding of pollen allergenicity and pathogenicity.
      Graphical abstract image

      PubDate: 2017-07-31T23:13:58Z
       
  • Virtual Screening and Repositioning of Inconclusive Molecules of
           Beta-lactamase Bioassays—A Data Mining Approach
    • Abstract: Publication date: Available online 29 July 2017
      Source:Computational Biology and Chemistry
      Author(s): Akshata Gad, Andrew Titus Manuel, Jinuraj K. R., Lijo John, Sajeev R., Shanmuga Priya V. G., Abdul Jaleel U.C.
      This study focuses on the best possible way forward in utilizing inconclusive molecules of PubChem bioassays AID 1332, AID 434987 and AID 434955, which are related to beta-lactamase inhibitors of Mycobacterium tuberculosis (Mtb). The inadequacy in the experimental methods that were observed during the invitro screening resulted in an inconclusive dataset. This could be due to certain moieties present within the molecules. In order to reconsider such molecules, insilico methods can be suggested in place of invitro methods For instance, datamining and medicinal chemistry methods: have been adopted to prioritise the inconclusive dataset into active or inactive molecules. These include the Random Forest algorithm for dataminning, Lilly MedChem rules for virtually screening out the promiscuity, and Self Organizing Maps (SOM) for clustering the active molecules and enlisting them for repositioning through the use of artificial neural networks. These repositioned molecules could then be prioritized for downstream drug discovery analysis.
      Graphical abstract image

      PubDate: 2017-07-31T23:13:58Z
       
  • SRSF shape analysis for sequencing data reveal new differentiating
           patterns
    • Abstract: Publication date: Available online 27 July 2017
      Source:Computational Biology and Chemistry
      Author(s): Sergiusz Wesolowski, Daniel Vera, Wei Wu
      Motivation Sequencing-based methods to examine fundamental features of the genome, such as gene expression and chromatin structure, rely on inferences from the abundance and distribution of reads derived from Illumina sequencing. Drawing sound inferences from such experiments relies on appropriate mathematical methods to model the distribution of reads along the genome, which has been challenging due to the scale and nature of these data. Results We propose a new framework (SRSFseq) based on Square Root Slope Functions shape analysis to analyse Illumina sequencing data. In the new approach the basic unit of information is the density of mapped reads over region of interest located on the known reference genome. The densities are interpreted as shapes and a new shape analysis model is proposed. An equivalent of a Fisher test is used to quantify the significance of shape differences in read distribution patterns between groups of density functions in different experimental conditions. We evaluated the performance of this new framework to analyze RNA-seq data at the exon level, which enabled the detection of variation in read distributions and abundances between experimental conditions not detected by other methods. Thus, the method is a suitable supplement to the state of the are count based techniques. The variety of density representations and flexibility of mathematical design allow the model to be easily adapted to other data types or problems in which the distribution of reads is to be tested. The functional interpretation and SRSF phase-amplitude separation technique gives an efficient noise reduction procedure improving the sensitivity and specificity of the method.

      PubDate: 2017-07-31T23:13:58Z
       
  • In silico locating the immune-reactive segments of Lepidium draba
           peroxidase and designing a less immune-reactive enzyme derivative
    • Abstract: Publication date: October 2017
      Source:Computational Biology and Chemistry, Volume 70
      Author(s): Yaser Fattahian, Ali Riahi-Madvar, Reza Mirzaee, Gholamreza Asadikaram, Mohammad Reza Rahbar
      Peroxidases have broad applications in industry, environmental as well as pharmaceutical and diagnosis. Recently applicability of peroxidases in cancer therapy was mentioned. In the present study, a horseradish peroxidase homologue from Lepidium draba was subjected to in silico analyzes aiming at identifying and locating immune-reactive regions. A derivative sequence with decreased immunogenicity and increased stability also suggested. The tertiary structure of the enzyme was predicted. The functional and structural importance of residues was annotated as well as the conservatory status of each residue. The immune-dominant regions of protein were predicted with various software. N-terminal 4 residues, NFSHTGL (186–192), PRNGN (210–214), PLVRAYADGTQKFFN (261–275), and last 4 residues in C-terminal were predicted to be the consensus immunogenic segments of L. draba peroxidase. The modifications were applied to wild type sequence in order to mitigate its immune-reactiveness. The modifications were based on predicted energetic status of residues and naturally occurred amino acids in each position of the enzyme sequence, extracted from alignment file of 150 homologous peroxidases. The new enzyme derivative is predicted to be less immune-reactive and more stable. Thus the sequence is better suited to therapeutic applications.
      Graphical abstract image

      PubDate: 2017-07-23T22:59:03Z
       
  • IFC Editorial Board
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69


      PubDate: 2017-07-23T22:59:03Z
       
  • Title page
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69


      PubDate: 2017-07-23T22:59:03Z
       
  • Screening disrupted molecular functions and pathways associated with clear
           cell renal cell carcinoma using Gibbs sampling
    • Abstract: Publication date: Available online 13 July 2017
      Source:Computational Biology and Chemistry
      Author(s): Ning Nan, Qi Chen, Yu Wang, Xu Zhai, Chuan-Ce Yang, Bin Cao, Chong Tie
      Objective To explore the disturbed molecular functions and pathways in clear cell renal cell carcinoma (ccRCC) using Gibbs sampling. Methods Gene expression data of ccRCC samples and adjacent non-tumor renal tissues were recruited from public available database. Then, molecular functions of expression changed genes in ccRCC were classed to Gene Ontology (GO) project, and these molecular functions were converted into Markov chains. Markov chain Monte Carlo (MCMC) algorithm was implemented to perform posterior inference and identify probability distributions of molecular functions in Gibbs sampling. Differentially expressed molecular functions were selected under posterior value more than 0.95, and genes with the appeared times in differentially expressed molecular functions ≥5 were defined as pivotal genes. Functional analysis was employed to explore the pathways of pivotal genes and their strongly co-regulated genes. Results In this work, we obtained 396 molecular functions, and 13 of them were differentially expressed. Oxidoreductase activity showed the highest posterior value. Gene composition analysis identified 79 pivotal genes, and survival analysis indicated that these pivotal genes could be used as a strong independent predictor of poor prognosis in patients with ccRCC. Pathway analysis identified one pivotal pathway − oxidative phosphorylation. Conclusions We identified the differentially expressed molecular functions and pivotal pathway in ccRCC using Gibbs sampling. The results could be considered as potential signatures for early detection and therapy of ccRCC.
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      PubDate: 2017-07-13T22:57:59Z
       
  • Computational analysis for the determination of deleterious nsSNPs in
           human MTHFD1 gene
    • Abstract: Publication date: Available online 11 July 2017
      Source:Computational Biology and Chemistry
      Author(s): Mansi Desai, J.B. Chauhan
      Single nucleotide polymorphisms (SNPs) are the most common genetic polymorphisms and play a major role in many inherited diseases. Methylenetetrahydrofolate dehydrogenase 1 (MTHFD1) is one of the enzymes involved in folate metabolism. In the present study, the functional and structural consequences of nsSNPs of human MTHFD1 gene was analyzed using various computational tools like SIFT, PolyPhen2, PANTHER, PROVEAN, SNAP2, nsSNPAnalyzer, PhD-SNP, SNPs&GO, I-Mutant, MuPro, ConSurf, InterPro, NCBI Conserved Domain Search tool, ModPred, SPARKS-X, RAMPAGE, FT Site and PyMol. Out of 327 nsSNPs form human MTHFD1 gene, total 45 SNPs were predicted as functionally most significant SNPs, among which 17 were highly conserved and functional, 17 were highly conserved and structural residues. Among 45 most significant SNPs, 15 were predicted to be involved in post translational modifications. The p.Gly165Arg may interfere in homodimer interface formation. The p.Asn439Lys and p.Asp445Asn may interfere in binding interactions of MTHFD1 protein with cesium cation and potassium. The two SNPs (p.Asp562Gly and p.Gly637Cys) might interfere in interactions of MTHFD1 with ligand.
      Graphical abstract image

      PubDate: 2017-07-13T22:57:59Z
       
  • Genome-wide Identification, Functional and Evolutionary Analysis of
           Terpene Synthases in Pineapple
    • Abstract: Publication date: Available online 6 July 2017
      Source:Computational Biology and Chemistry
      Author(s): Xiaoe Chen, Wei Yang, Liqin Zhang, Xianmiao Wu, Tian Cheng, Guanglin Li
      Terpene synthases (TPSs) are vital for the biosynthesis of active terpenoids, which have important physiological, ecological and medicinal value. Although terpenoids have been reported in pineapple (Ananas comosus), genome-wide investigations of the TPS genes responsible for pineapple terpenoid synthesis are still lacking. By integrating pineapple genome and proteome data, twenty-one putative terpene synthase genes were found in pineapple and divided into five subfamilies. Tandem duplication is the cause of TPS gene family duplication. Furthermore, functional differentiation between each TPS subfamily may have occurred for several reasons. Sixty-two key amino acid sites were identified as being type-II functionally divergence between TPS-a and TPS-c subfamily. Finally, coevolution analysis indicated that multiple amino acid residues are involved in coevolutionary processes. In addition, the enzyme activity of two TPSs were tested. This genome-wide identification, functional and evolutionary analysis of pineapple TPS genes provide a new insight into understanding the roles of TPS family and lay the basis for further characterizing the function and evolution of TPS gene family.

      PubDate: 2017-07-13T22:57:59Z
       
  • Tetracyclines as a potential antiviral therapy against Crimean Congo
           Hemorrhagic Fever Virus: docking and molecular dynamic studies
    • Abstract: Publication date: Available online 1 July 2017
      Source:Computational Biology and Chemistry
      Author(s): Amirhossein Sharifi, Arash Amanlou, Faezeh Moosavi, Sahand Golestanian, Massoud Amanlou
      Crimean-Congo Hemorrhagic Fever Virus (CCHFV) is one of the deadliest human diseases with mortality rate near 50%. Special attention should be paid to this virus since there is no approved treatment for it. On the other hand, the recent outbreak of Ebola virus which is a member of hemorrhagic fever viruses shows this group of viruses can be extremely dangerous. Previous studies have indicated that nucleoprotein of CCHFV, a pivotal protein in virus replication, is an appropriate target for antiviral drug development. The aim of this study is finding inhibitor(s) of this protein. Herein, a virtual screening procedure employing docking followed by molecular dynamic was used to identify small molecule inhibitors of the nucleoprotein from FDA-approved drugs. Regarding CCHFV, using in-silico method is a safe way to achieve its inhibitor(s) since this virus is categorized as a World Health Organization (WHO) biosafety level 4 pathogen and therefore investigation in general laboratories is restricted. In conclusion, considering docking and molecular dynamic results alongside with bioavailability of FDA-approved drugs, doxycycline and minocycline are proposed as potential inhibitors of CCHFV nucleoprotein. There is hope, this study encourage other research groups for in-vitro and in-vivo studies about the efficacy of those two medicines in CCHFV treatment.
      Graphical abstract image

      PubDate: 2017-07-04T22:37:41Z
       
  • Subcellular localization based comparative study on radioresistant
           bacteria: A novel approach to mine proteins involve in radioresistance
    • Abstract: Publication date: Available online 10 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Divya Vishambra, Malay Srivastava, Kamal Dev, Varun Jaiswal
      Radioresistant bacteria (RRB) are among the most radioresistant organisms and has a unique role in evolution. Along with the evolutionary role, radioresistant organisms play important role in paper industries, bioremediation, vaccine development and possibility in anti-ageing and anti-cancer treatment. The study of radiation resistance in RRB was mainly focused on cytosolic mechanisms such as DNA repair mechanism, cell cleansing activity and high antioxidant activity. Although it was known that protein localized on outer areas of cell play role in resistance towards extreme condition but the mechanisms/proteins localized on the outer area of cells are not studied for radioresistance. Considering the fact that outer part of cell is more exposed to radiations and proteins present in outer area of the cell may have role in radioresistance. Localization based comparative study of proteome from RRB and non-radio resistant bacteria was carried out. In RRB 20 unique proteins have been identified. Further domain, structural, and pathway analysis of selected proteins were carried out. Out of 20 proteins, 8 proteins were direct involvement in radioresistance and literature study strengthens this, however, 1 proteins had assumed relation in radioresistance. Selected radioresistant proteins may be helpful for optimal use of RRB in industry and health care.

      PubDate: 2017-05-14T15:31:41Z
       
  • 3D-QSAR studies of some reversible Acetyl cholinesterase inhibitors based
           on CoMFA and ligand protein interaction fingerprints using PC-LS-SVM and
           PLS-LS-SVM
    • Abstract: Publication date: Available online 10 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Hamidreza Ghafouri, Mohsen Ranjbar, Amirhossein Sakhteman
      A great challenge in medicinal chemistry is to develop different methods for structural design based on the pattern of the previously synthesized compounds. In this study two different QSAR methods were established and compared for a series of piperidine acetylcholinesterase inhibitors. In one novel approach, PC-LS-SVM and PLS-LS-SVM was used for modeling 3D interaction descriptors, and in the other method the same nonlinear techniques were used to build QSAR equations based on field descriptors. Different validation methods were used to evaluate the models and the results revealed the more applicability and predictive ability of the model generated by field descriptors (Q2 LOO-CV =1, R2 ext =0.97). External validation criteria revealed that both methods can be used in generating reasonable QSAR models. It was concluded that due to ability of interaction descriptors in prediction of binding mode, using this approach can be implemented in future 3D-QSAR softwares.
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      PubDate: 2017-05-14T15:31:41Z
       
  • Molecular characterization of pigeon torque teno virus (PTTV) in Jiangsu
           province
    • Abstract: Publication date: Available online 6 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Zhingcheng Zhang, Wei Dai, Dingzhen Dai
      The torque teno virus (TTV) is a recently discovered DNA virus that has been detected in many different hosts, including humans, livestock and poultry. To date, there is no report of pigeon TTV (PTTV) from anywhere in the world. To investigate the distribution of PTTV in pigeons from the eastern Chinese province of Jiangsu and characterize their genomes, we employed PCR to detect PTTV in 144 samples collected from 6 pigeon plants in Jiangsu province, amplify complete genomes from representative samples and analyze genetic characteristics using bioinformatics. The results demonstrated that 71.5% (103/144) of samples were PTTV positive. The rate of sequence homology among the six PTTV complete genomes obtained from Jiangsu province ranged from 99.7% to 100%. Phylogenetic analysis suggested that PTTV genomes had a high degree of genetic similarity and were similar to chicken anemia virus that also had poultry as a host. Although with the same host, PTTV shared distant relationship with PiCV in both complete genome, Rep and Cap genes. The results of this study provided evidence that PTTV could be detected in Chinese pigeons at a high level, the evolutionary process of complete genome, Rep and Cap genes of Anelloviridae family had obvious divergence.
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      PubDate: 2017-05-09T15:11:48Z
       
  • Identification of Novel Human Renin Inhibitors through a combined approach
           of Pharmacophore modelling, Molecular DFT analysis and in silico screening
           
    • Abstract: Publication date: Available online 27 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Dhrubajyoti Gogoi, Vishwa Jyoti Baruah, Amrita Kashyap Chaliha, Bibhuti Bhushan Kakoti, Diganta Sarma, Alak Kumar Buragohain
      Renin is an aspartyl protease of the renin–angiotensin system (RAS) and the first enzyme of the biochemical pathway for the generation of Angiotensin II- a potent vasoconstrictor involved in the maintenance of cardiovascular homeostasis and the regulation of blood pressure. High enzymatic specificity of renin and its involvement in the catalysis of the rate-limiting step of the RAS hormone system qualifies it as a good target for inhibition of hypertension and other associated diseases. Ligand-based pharmacophore model (Hypo1) was generated from a training set of 24 compounds with renin inhibitory activity. The best hypothesis consisted of one Hydrogen Bond Acceptor (HBA), three Hydrophobic Aliphatic (HY-Al) and one Ring Aromatic (AR) features. This well-validated pharmacophore hypothesis (correlation coefficient 0.95) was further utilized as a 3D query to screen database compounds, which included structures from two natural product repositories. These screened compounds were further analyzed for drug-likeness and ADMET studies. The compounds which satisfied the qualifying criteria were then subjected to molecular docking and Density Functional Theory (DFT) analysis in order to discern their atomic level interactions at the active site of the 3D structure of rennin. The pharmacophore-based modeling that has been used to generate the novel findings of the present study would be an avant-garde approach towards the development of potent inhibitors of renin.
      Graphical abstract image

      PubDate: 2017-04-28T02:46:13Z
       
 
 
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