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  Subjects -> ENGINEERING (Total: 2277 journals)
    - CHEMICAL ENGINEERING (191 journals)
    - CIVIL ENGINEERING (183 journals)
    - ELECTRICAL ENGINEERING (103 journals)
    - ENGINEERING (1204 journals)
    - ENGINEERING MECHANICS AND MATERIALS (385 journals)
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    - INDUSTRIAL ENGINEERING (66 journals)
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CHEMICAL ENGINEERING (191 journals)                     

Showing 1 - 191 of 191 Journals sorted alphabetically
AATCC Journal of Research     Full-text available via subscription   (Followers: 6)
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: 20)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 7)
Advanced Chemical Engineering Research     Open Access   (Followers: 31)
Advanced Powder Technology     Hybrid Journal   (Followers: 16)
Advances in Applied Ceramics     Hybrid Journal   (Followers: 5)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 24)
Advances in Chemical Engineering and Science     Open Access   (Followers: 53)
Advances in Polymer Technology     Hybrid Journal   (Followers: 13)
African Journal of Pure and Applied Chemistry     Open Access   (Followers: 7)
Annual Review of Analytical Chemistry     Full-text available via subscription   (Followers: 10)
Annual Review of Chemical and Biomolecular Engineering     Full-text available via subscription   (Followers: 13)
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: 7)
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: 7)
ChemBioEng Reviews     Full-text available via subscription   (Followers: 1)
Chemical and Engineering News     Free   (Followers: 12)
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: 41)
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: 19)
Chemical Papers     Hybrid Journal   (Followers: 2)
Chemical Product and Process Modeling     Hybrid Journal   (Followers: 4)
Chemical Reviews     Full-text available via subscription   (Followers: 172)
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: 243)
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: 20)
Corrosion Engineering, Science and Technology     Hybrid Journal   (Followers: 36)
Corrosion Reviews     Hybrid Journal   (Followers: 6)
Crystal Research and Technology     Hybrid Journal   (Followers: 6)
Current Opinion in Chemical Engineering     Open Access   (Followers: 7)
Education for Chemical Engineers     Hybrid Journal   (Followers: 5)
Eksergi     Open Access  
Emerging Trends in Chemical Engineering     Full-text available via subscription   (Followers: 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: 170)
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: 6)
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: 4)
Journal of Chemical Sciences     Partially Free   (Followers: 19)
Journal of Chemical Technology & Biotechnology     Hybrid Journal   (Followers: 10)
Journal of Chemical Theory and Computation     Full-text available via subscription   (Followers: 15)
Journal of CO2 Utilization     Hybrid Journal   (Followers: 2)
Journal of Combinatorial Chemistry     Full-text available via subscription  
Journal of Crystallization Process and Technology     Open Access   (Followers: 8)
Journal of Environmental Chemical Engineering     Hybrid Journal   (Followers: 5)
Journal of Food Measurement and Characterization     Hybrid Journal  
Journal of Food Processing & Technology     Open Access   (Followers: 1)
Journal of Fuel Chemistry and Technology     Full-text available via subscription   (Followers: 4)
Journal of Geochemical Exploration     Hybrid Journal   (Followers: 1)
Journal of Industrial and Engineering Chemistry     Hybrid Journal   (Followers: 1)
Journal of Information Display     Hybrid Journal   (Followers: 1)
Journal of Inorganic and Organometallic Polymers and Materials     Partially Free   (Followers: 9)
Journal of Modern Chemistry & Chemical Technology     Full-text available via subscription   (Followers: 2)
Journal of Molecular Catalysis A: Chemical     Hybrid Journal   (Followers: 6)
Journal of Non-Crystalline Solids     Hybrid Journal   (Followers: 8)
Journal of Organic Semiconductors     Open Access   (Followers: 5)
Journal of Physics and Chemistry of Solids     Hybrid Journal   (Followers: 5)
Journal of Polymer and Biopolymer Physics Chemistry     Open Access   (Followers: 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: 297)
Journal of the Bangladesh Chemical Society     Open Access  
Journal of the Brazilian Chemical Society     Open Access   (Followers: 2)
Journal of The Institution of Engineers (India) : Series E     Hybrid Journal   (Followers: 1)
Journal of the Pakistan Institute of Chemical Engineers     Open Access   (Followers: 1)
Journal of the Taiwan Institute of Chemical Engineers     Hybrid Journal   (Followers: 2)
Journal of Water Chemistry and Technology     Hybrid Journal   (Followers: 9)
Jurnal Bahan Alam Terbarukan     Open Access  
Jurnal Inovasi Pendidikan Kimia     Open Access   (Followers: 5)
Jurnal Reaktor     Open Access  
Jurnal 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: 1)
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: 158)
Polymer Bulletin     Hybrid Journal   (Followers: 7)
Polymer Composites     Hybrid Journal   (Followers: 15)
Polyolefins Journal     Open Access  
Powder Technology     Hybrid Journal   (Followers: 13)
Recyclable Catalysis     Open Access   (Followers: 1)
Research on Chemical Intermediates     Hybrid Journal  
Reviews in Chemical Engineering     Hybrid Journal   (Followers: 5)
Revista ION     Open Access  
Revista Mexicana de Ingeniería Química     Open Access  
Rubber Chemistry and Technology     Full-text available via subscription   (Followers: 2)
Russian Chemical Bulletin     Hybrid Journal   (Followers: 2)
Russian Journal of Applied Chemistry     Hybrid Journal   (Followers: 1)
Science and Engineering of Composite Materials     Hybrid Journal   (Followers: 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: 3)
The Chemical Record     Hybrid Journal   (Followers: 1)
Theoretical Foundations of Chemical Engineering     Hybrid Journal   (Followers: 2)
Transition Metal Chemistry     Hybrid Journal   (Followers: 4)
Transylvanian Review of Systematical and Ecological Research     Open Access  
Visegrad Journal on Bioeconomy and Sustainable Development     Open Access   (Followers: 2)
Zeitschrift für Naturforschung B : A Journal of Chemical Sciences     Open Access   (Followers: 1)

           

Journal Cover Computational Biology and Chemistry
  [SJR: 0.491]   [H-I: 47]   [12 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1476-9271
   Published by Elsevier Homepage  [3043 journals]
  • 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.
      Graphical abstract image

      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.
      Graphical abstract image

      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.
      Graphical abstract image

      PubDate: 2017-08-11T02:21:23Z
       
  • Multi-objective feature selection for warfarin dose prediction
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69
      Author(s): Mohammad Karim Sohrabi, Alireza Tajik
      With increasing the application of decision support systems in various fields, using such systems in different aspects of medical science has been growing. Drug’s dose prediction is one of the most important issues which can be improved using decision support systems. In this paper, a new multi-objective feature approach has been proposed to support warfarin dose prediction decision. Warfarin is an anticoagulant normally used in the prevention of the formation of clots. This research was conducted on 553 patients during 2013–2015 who were candidates for using warfarin and their INR was in the target range. Features affecting dose was implemented and evaluated, which were clinical and genetic characteristics extracted, and new methods of feature selection based on multi-objective optimization methods such as the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) along with the evaluation of artificial neural networks were used. Multi-objective optimization methods have more accuracy and performance compared to the classic methods of feature selection. Furthermore, multi-objective particle swarm optimization algorithm has higher precision than Non-dominated Sorting Genetic Algorithm-II. With a choice of seven features Mean Square Error (MSE), root mean square error (RMSE) and mean absolute error (MAE) were 0.011, 0.1 and 0.109 for MOPSO, respectively.
      Graphical abstract image

      PubDate: 2017-07-31T23:13:58Z
       
  • 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.
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      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
       
  • A novel wearable device for continuous, non-invasion blood pressure
           measurement
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69
      Author(s): Qin Xin, Jianping Wu
      In this paper, we have developed a wearable cuffless device for daily blood pressure (BP) measurement. We incorporated the light based sensor and other hard wares in a small volume for BP detection. With optimized algorithm, the real-time BP reading could be achieved, the data could be presented in the screen and be transmitted by internet of things (IoT) for history data comparison and multi-terminal viewing. Thus, further analysis provides the probability for diet or sports suggestion and alarm. We have measured BP from more than 60 subjects, compare to traditional mercury blood pressure meter, no obvious error in both systolic blood pressure (SBP) and diastolic blood pressure (DBP) are detected. Such device can be used for continues non-invasion BP detection, and further data docking and health analysis could be achieved.

      PubDate: 2017-07-23T22:59:03Z
       
  • Gene expression profiling of tumor-associated macrophages after exposure
           to single-dose irradiation
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69
      Author(s): Wei-Hsiang Kung, Ching-Fang Yu, Andy Chi-Lung Lee, Chi-Dung Yang, Yu-Chen Liu, Fang-Hsin Chen, Hsien-Da Huang
      Radiotherapy (RT) is a common cancer treatment approach that accounts for nearly 50% of patient treatment; however, tumor relapse after radiotherapy is still a major issue. To study the crucial role of tumor-associated macrophages (TAMs) in the regulation of tumor progression post-RT, microarray experiments comparing TAM gene expression profiles between unirradiated and irradiated tumors were conducted to discover possible roles of TAMs in initiation or contribution to tumor recurrence following RT, taking into account the relationships among gene expression, tumor microenvironment, and immunology. A single dose of 25Gy was given to TRAMP C-1 prostate tumors established in C57/B6 mice. CD11b-positive macrophages were extracted from the tumors at one, two and three weeks post-RT. Gene ontology (GO) term analysis using the DAVID database revealed that genes that were differentially expressed at one and two weeks after irradiation were associated with biological processes such as morphogenesis of a branching structure, tube development, and cell proliferation. Analysis using Short Time-Series Expression Miner (STEM) revealed the temporal gene expression profiles and identified 13 significant patterns in four main groups of profiles. The genes in the upregulated temporal profile have diverse functions involved in the intracellular signaling cascade, cell proliferation, and cytokine-mediated signaling pathway. We show that tumor irradiation with a single 25-Gy dose can initiate a time-series of differentially expressed genes in TAMs, which are associated with the immune response, DNA repair, cell cycle arrest, and apoptosis. Our study helps to improve our understanding of the function of the group of genes whose expression changes temporally in an irradiated tumor microenvironment.

      PubDate: 2017-07-23T22:59:03Z
       
  • GQSAR modeling and combinatorial library generation of
           4-phenylquinazoline-2-carboxamide derivatives as antiproliferative agents
           in human Glioblastoma tumors
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69
      Author(s): Debolina Goswami, Sukriti Goyal, Salma Jamal, Ritu Jain, Divya Wahi, Abhinav Grover
      Background TSPO translocator protein, encoded in humans by the Tspo gene plays a crucial role in mitochondria mediated apoptosis and necrotic cell death through its association with Mitochondrial Permeability Transition pore (MPTP). It has been shown that this function can be exploited as a potential treatment for human Glioblastoma Multiforme. In this study, a novel robust fragment based QSAR model has been developed for a series of 4-phenylquinazoline-2-carboxamides experimentally known to be ligands for TSPO, thus triggering apoptotic mechanism cascade. Results Model developed showed satisfactory statistical parameters for the experimentally reported dataset (r2 =0.8259, q2 =0.6788, pred_r2 =0.8237 and F-test=37.9). Low standard error values (r2_se=0.253, q2_se=0.34, pred_r2_se=0.14) confirmed the accuracy of the generated model. The model obtained had 4 descriptors, namely, R1-Volume, R2-SsCH3E-index, R3-SsCH3count and R5-EpsilonR. Two of them had positive contribution while the other two had negative correlation. Conclusion The high binding affinity and the presence of essential structural features in these compounds make them an ideal choice for the consideration as potent anti-GBM drugs. Activity predicted by GQSAR model reinforces their potential as worthy candidates for drugs against GBM. The detailed analysis carried out in this study provides a substantial basis for the prospective design and development of novel 4-phenylquinazoline-2-carboxamide compounds as TSPO ligands capable of inducing apoptosis in cancer cells.

      PubDate: 2017-07-23T22:59:03Z
       
  • Optimal hybrid sequencing and assembly: Feasibility conditions for
           accurate genome reconstruction and cost minimization strategy
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69
      Author(s): Chun-Chi Chen, Noushin Ghaffari, Xiaoning Qian, Byung-Jun Yoon
      Recent advances in high-throughput genome sequencing technologies have enabled the systematic study of various genomes by making whole genome sequencing affordable. Modern sequencers generate a huge number of small sequence fragments called reads, where the read length and the per-base sequencing cost depend on the technology used. To date, many hybrid genome assembly algorithms have been developed that can take reads from multiple read sources to reconstruct the original genome. However, rigorous investigation of the feasibility conditions for complete genome reconstruction and the optimal sequencing strategy for minimizing the sequencing cost has been conspicuously missing. An important aspect of hybrid sequencing and assembly is that the feasibility conditions for genome reconstruction can be satisfied by different combinations of the available read sources, opening up the possibility of optimally combining the sources to minimize the sequencing cost while ensuring accurate genome reconstruction. In this paper, we derive the conditions for whole genome reconstruction from multiple read sources at a given confidence level and also introduce the optimal strategy for combining reads from different sources to minimize the overall sequencing cost. We show that the optimal read set, which simultaneously satisfies the feasibility conditions for genome reconstruction and minimizes the sequencing cost, can be effectively predicted through constrained discrete optimization. Through extensive evaluations based on several genomes and different read sets, we verify the derived feasibility conditions and demonstrate the performance of the proposed optimal hybrid sequencing and assembly strategy.

      PubDate: 2017-07-23T22:59:03Z
       
  • Discovering DNA methylation patterns for long non-coding RNAs associated
           with cancer subtypes
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69
      Author(s): Xiaoke Ma, Liang Yu, Peizhuo Wang, Xiaofei Yang
      Despite growing evidence demonstrates that the long non-coding ribonucleic acids (lncRNAs) are critical modulators for cancers, the knowledge about the DNA methylation patterns of lncRNAs is quite limited. We develop a systematic analysis pipeline to discover DNA methylation patterns for lncRNAs across multiple cancer subtypes from probe, gene and network levels. By using The Cancer Genome Atlas (TCGA) breast cancer methylation data, the pipeline discovers various DNA methylation patterns for lncRNAs across four major subtypes such as luminal A, luminal B, her2-enriched as well as basal-like. On the probe and gene level, we find that both differentially methylated probes and lncRNAs are subtype specific, while the lncRNAs are not as specific as probes. On the network level, the pipeline constructs differential co-methylation lncRNA network for each subtype. Then, it identifies both subtype specific and common lncRNA modules by simultaneously analyzing multiple networks. We show that the lncRNAs in subtype specific and common modules differ greatly in terms of topological structure, sequence conservation as well as expression. Furthermore, the subtype specific lncRNA modules serve as biomarkers to improve significantly the accuracy of breast cancer subtypes prediction. Finally, the common lncRNA modules associate with survival time of patients, which is critical for cancer therapy.
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      PubDate: 2017-07-23T22:59:03Z
       
  • Live phylogeny with polytomies: Finding the most compact parsimonious
           trees
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69
      Author(s): D. Papamichail, A. Huang, E. Kennedy, J.-L. Ott, A. Miller, G. Papamichail
      Construction of phylogenetic trees has traditionally focused on binary trees where all species appear on leaves, a problem for which numerous efficient solutions have been developed. Certain application domains though, such as viral evolution and transmission, paleontology, linguistics, and phylogenetic stemmatics, often require phylogeny inference that involves placing input species on ancestral tree nodes (live phylogeny), and polytomies. These requirements, despite their prevalence, lead to computationally harder algorithmic solutions and have been sparsely examined in the literature to date. In this article we prove some unique properties of most parsimonious live phylogenetic trees with polytomies, and their mapping to traditional binary phylogenetic trees. We show that our problem reduces to finding the most compact parsimonious tree for n species, and describe a novel efficient algorithm to find such trees without resorting to exhaustive enumeration of all possible tree topologies.

      PubDate: 2017-07-23T22:59:03Z
       
  • PECC: Correcting contigs based on paired-end read distribution
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69
      Author(s): Min Li, Binbin Wu, Xiaodong Yan, Junwei Luo, Yi Pan, Fang-Xiang Wu, Jianxin Wang
      Motivation Cheap and fast next generation sequencing (NGS) technologies facilitate research of de novo assembly greatly. The reliability of contigs is critical to construct reliable scaffolding. However, contigs generated from most assemblers contain errors because of the limitation of assembly strategy and computation complexity. Among all these errors, the misassembly error is one of the most harmful types. Results In this paper, we propose a new method named “PECC” to identify and correct misassembly errors in contigs based on the paired-end read distribution. PECC extracts sequence regions with lower paired-end reads supports and verifies them based on the distribution of paired-end supports. To validate the effectiveness of PECC, we applied PECC to the contigs produced by five popular assemblers on four real datasets, and we also carried out experiments to analyze the influences of PECC on scaffolding. The results show that PECC can reduce misassembly errors and improve the performance of scaffolding results, which demonstrate the promising applications of PECC in de novo assembly.

      PubDate: 2017-07-23T22:59:03Z
       
  • Drug–target interaction prediction by integrating multiview network
           data
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69
      Author(s): Xin Zhang, Limin Li, Michael K. Ng, Shuqin Zhang
      Drug–target interaction (DTI) prediction is a challenging step in further drug repositioning, drug discovery and drug design. The advent of high-throughput technologies brings convenience to the development of DTI prediction methods. With the generation of a high number of data sets, many mathematical models and computational algorithms have been developed to identify the potential drug–target pairs. However, most existing methods are proposed based on the single view data. By integrating the drug and target data from different views, we aim to get more stable and accurate prediction results. In this paper, a multiview DTI prediction method based on clustering is proposed. We first introduce a model for single view drug–target data. The model is formulated as an optimization problem, which aims to identify the clusters in both drug similarity network and target protein similarity network, and at the same time make the clusters with more known DTIs be connected together. Then the model is extended to multiview network data by maximizing the consistency of the clusters in each view. An approximation method is proposed to solve the optimization problem. We apply the proposed algorithms to two views of data. Comparisons with some existing algorithms show that the multiview DTI prediction algorithm can produce more accurate predictions. For the considered data set, we finally predict 54 possible DTIs. From the similarity analysis of the drugs/targets, enrichment analysis of DTIs and genes in each cluster, it is shown that the predicted DTIs have a high possibility to be true.

      PubDate: 2017-07-23T22:59:03Z
       
  • Node-based differential network analysis in genomics
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69
      Author(s): Xiao-Fei Zhang, Le Ou-Yang, Hong Yan
      Gene dependency networks often undergo changes in response to different conditions. Understanding how these networks change across two conditions is an important task in genomics research. Most previous differential network analysis approaches assume that the difference between two condition-specific networks is driven by individual edges. Thus, they may fail in detecting key players which might represent important genes whose mutations drive the change of network. In this work, we develop a node-based differential network analysis (N-DNA) model to directly estimate the differential network that is driven by certain hub nodes. We model each condition-specific gene network as a precision matrix and the differential network as the difference between two precision matrices. Then we formulate a convex optimization problem to infer the differential network by combing a D-trace loss function and a row-column overlap norm penalty function. Simulation studies demonstrate that N-DNA provides more accurate estimate of the differential network than previous competing approaches. We apply N-DNA to ovarian cancer and breast cancer gene expression data. The model rediscovers known cancer-related genes and contains interesting predictions.

      PubDate: 2017-07-23T22:59:03Z
       
  • Efficiently predicting large-scale protein-protein interactions using
           MapReduce
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69
      Author(s): Lun Hu, Xiaohui Yuan, Pengwei Hu, Keith C.C. Chan
      With a rapid development of high-throughput genomic technologies, a vast amount of protein-protein interactions (PPIs) data has been generated for difference species. However, such set of PPIs is rather small when compared with all possible PPIs. Hence, there is a necessity to specifically develop computational algorithms for large-scale PPI prediction. In response to this need, we propose a parallel algorithm, namely pVLASPD, to perform the prediction task in a distributed manner. In particular, pVLASPD was modified based on the VLASPD algorithm for the purpose of improving the efficiency of VLASPD while maintaining a comparable effectiveness. To do so, we first analyzed VLASPD step by step to identify the places that caused the bottlenecks of efficiency. After that, pVLASPD was developed by parallelizing those inefficient places with the framework of MapReduce. The extensive experimental results demonstrate the promising performance of pVLASPD when applied to prediction of large-scale PPIs.

      PubDate: 2017-07-23T22:59:03Z
       
  • Acknowledgment to reviewers
    • 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.
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      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
       
  • In silico investigation of propofol binding sites in human serum albumin
           using explicit and implicit solvation models
    • Abstract: Publication date: Available online 4 July 2017
      Source:Computational Biology and Chemistry
      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 methods to compare the binding affinity of propofol at the different binding sites (PR1 and PR2) in the HSA protein. Only the implicit model provided the evidence in accordance with the experimental data indicating that the HSA-ligand interactions are dominant by hydrophobic forces due to the higher drug affinity at the PR1 position with a ΔGMM-PB/SA value of −28.84kcalmol−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.
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      PubDate: 2017-07-04T22:37:41Z
       
  • 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.
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      PubDate: 2017-07-04T22:37:41Z
       
  • Pharmacophore modeling, virtual screening and molecular docking of ATPase
           inhibitors of HSP70
    • Abstract: Publication date: Available online 26 June 2017
      Source:Computational Biology and Chemistry
      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 was predicted .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: 2017-07-04T22:37:41Z
       
  • Computational Design of Peptide Ligands to Target the Intermolecular
           Interaction between Viral Envelope Protein and Pediatric Receptor
    • Abstract: Publication date: Available online 17 June 2017
      Source:Computational Biology and Chemistry
      Author(s): Darong Xu, Hongliang Bian, Jinlan Cai, Daocheng Bao, Qing Jin, Min Zhu, Cuifeng Zhang, Tingting Tao
      The recognition and binding of viral envelope protein to pediatric receptor subverts the membrane-trafficking apparatus to mediate virion export in young children. Here, we described a successful computational design of peptide ligands to target the intermolecular interaction between the virus large envelope protein (LHB) and adaptin receptor (ADT). Based on the crystal structure of ADT in complex with an oligopeptide segment corresponding to the core binding site of LHB, a sequence-specific amino acid preference profile was determined systematically for the ADT-binding peptides using structural bioinformatics approach. With the information harvested from the profile, a genetic evolution procedure was run to improve the biological potency of a peptide population generated randomly from the LHB. A number of potential hits were obtained from the evolution, and four were measured to interact with ADT at micromolar level. A high-affinity hit peptide was then optimized according to computational structural analysis. It is revealed that a potent peptide can be divided into three regions, i.e. a negatively charged region at N-terminus, a hydrophobic core region in middle, and a small, polar region at C-terminal tail. In addition, the two termini of peptide are partially out of the active pocket of ADT, thus contributing moderately to the peptide binding.
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      PubDate: 2017-06-23T13:27:43Z
       
  • Sphingosine kinase 1 (SK1) allosteric inhibitors that target the
           dimerization site
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69
      Author(s): Ozge Bayraktar, Elif Ozkirimli, Kutlu Ulgen
      The sphingosine kinase 1 (SK1)/sphingosine-1-phosphate (S1P) signaling pathway is a crucial target for numerous human diseases from cancer to cardiovascular diseases. However, available SK1 inhibitors that target the active site suffer from poor potency, selectivity and pharmacokinetic properties. The selectivity issue of the kinases, which share a highly-conserved ATP-pocket, can be overcome by targeting the less-conserved allosteric sites. SK1 is known to function minimally as a dimer; however, the crystal structure of the SK1 dimer has not been determined. In this study, a template-based algorithm implemented in PRISM was used to predict the SK1 dimer structure and then the possible allosteric sites at the dimer interface were determined via SiteMap. These sites were used in a virtual screening campaign that includes an integrated workflow of structure-based pharmacophore modeling, virtual screening, molecular docking, re-screening of common scaffolds to propose a series of compounds with different scaffolds as potential allosteric SK1 inhibitors. Finally, the stability of the SK1-ligand complexes was analyzed by molecular dynamics simulations. As a final outcome, ligand 7 having a 4,9-dihydro-1H-purine scaffold and ligand 12 having a 2,3,4,9-tetrahydro-1H-β-carboline scaffold were found to be potential selective inhibitors for SK1.
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      PubDate: 2017-06-04T14:43:17Z
       
  • Differentiating the pre-hydrolysis states of wild-type and A59G mutant
           HRas: An insight through MD simulations
    • Abstract: Publication date: Available online 1 June 2017
      Source:Computational Biology and Chemistry
      Author(s): Neeru Sharma, Uddhavesh Sonavane, Rajendra Joshi
      The most representative member of the Ras subfamily is its HRas isoform. Ras proteins being GTPases, possess an intrinsic activity to hydrolyze the GTP molecule to GDP. During the transition phases, between active and inactive states, P-loop and switch regions show maximum variations. Various hot-spot Ras mutants (G12V, A59G, Q61L etc) have been reported, that limit the protein's conformation in the permanent active state. In the present study, we aim to explore the structural dynamics of one such crucial mutant of Ras namely A59G which belongs to the conserved Switch II region of the protein. Approximately ∼15μs of Classical Molecular Dynamics (CMD) simulations have been carried out on the mutant and wild-type complexes. Further, a metadynamics simulation of 500ns was also carried out, which suggests an energy barrier of ∼9.56kcal/mol between wild-type and mutant conformation. We demonstrate the role of water molecule in maintaining the required interaction networks in the pre-hydrolysis state, its impact on A59G mutation, distinct orientation of the Gln61 residue in two conformations, disruption of crucial Gly60 and γ phosphate and the change in the Switch II region. The outcome of our study captures the pre-hydrolysis state of the HRas protein. It also establishes the fact that this mutation makes the movement of Switch II region and the conserved DXXGQ motif highly constrained, which is known to be an important requirement for hydrolysis. This suggests that the A59G mutation may decrease the rate of intrinsic hydrolysis as well as GAP-mediated hydrolysis.
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      PubDate: 2017-06-04T14:43:17Z
       
  • Modelling toxin effects on protein biosynthesis in eukaryotic cells
    • Abstract: Publication date: Available online 31 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Vladas Skakauskas, Pranas Katauskis
      We present a rather generic model for toxin (ricin) inhibition of protein biosynthesis in eukaryotic cells. We also study reduction of the ricin toxic effects with application of antibodies against the RTB subunit of ricin molecules. Both species initially are delivered extracellularly. The model accounts for the pinocytotic and receptor-mediated toxin endocytosis and the intact toxin exocytotic removal out of the cell. The model also includes the lysosomal toxin destruction, the intact toxin motion to the endoplasmic reticulum (ER) for separation of its molecules into the RTA and RTB subunits, and the RTA chain translocation into the cytosol. In the cytosol, one portion of the RTA undergoes degradation via the ERAD. The other its portion can inactivate ribosomes at a large rate. The model is based on a system of deterministic ODEs. The influence of the kinetic parameters on the protein concentration and antibody protection factor is studied in detail.
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      PubDate: 2017-06-04T14:43:17Z
       
  • Improving Virtual Screening Predictive Accuracy of Human Kallikrein 5
           inhibitors using Machine Learning Models
    • Abstract: Publication date: Available online 29 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Xingang Fang, Sikha Bagui, Subhash Bagui
      The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets.
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      PubDate: 2017-06-04T14:43:17Z
       
  • Codon usage bias and its influencing factors for Y-linked genes in human
    • Abstract: Publication date: Available online 27 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Monisha Nath Choudhury, Arif Uddin, Supriyo Chakraborty
      The non-uniform usage of synonymous codons during translation of a protein is the codon usage bias and is mainly influenced by natural selection and mutation pressure. We have used bioinformatic approaches to analyze codon usage bias of human Y-linked genes. Effective number of codon (ENC) suggested that the overall extent of codon usage bias of genes was low. The relative synonymous codon usage (RSCU) analysis revealed that AGA and CTG codons were over-represented in Y-linked genes. Compositional constraint under mutation pressure influenced the codon usage pattern as revealed by the correspondence analysis (COA). Parity plot suggests that both natural selection and mutation pressure might have influenced the codon usage bias of Y-linked genes.
      Graphical abstract image

      PubDate: 2017-05-29T15:59:05Z
       
  • In silico identification of vaccine candidates against Klebsiella oxytoca
    • Abstract: Publication date: Available online 22 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Sandipan Talukdar, Udeshna Bayan, Kandarpa Kr. Saikia
      Klebsiella oxytoca causes several diseases in immunocompromised as well as healthy individuals. Increasing resistance to a number of antibiotics makes treatment options limited. Prevention using vaccine could be an important solution to get rid of infections caused by Klebsiella oxytoca. In recent time, genome based approaches have contributed significantly in vaccine development. Our aim was to identify the most conserved and immunogenic antigens that can be considered as potential vaccine candidates. KEGG database was used to find out pathways unique to the bacteria. Subcellular localization of the protein sequences taken from the selected 36 pathways were predicted using PSORTb v3.0.2 and CELLO v2.5. Prediction of B cell epitope and the probability of the antigenicity were evaluated by using IEDB and Vaxijen respectively. BLASTp was done to find out the similarity of the selected proteins with the human proteome. Proteins failing to comply with the set parameters were filtered at each step. Finally, we identified 6 surface exposed proteins as potential vaccine candidates against Klebsiella oxytoca.
      Graphical abstract image

      PubDate: 2017-05-24T15:42:00Z
       
  • Extra precision docking, free energy calculation and molecular dynamics
           studies on glutamic acid derivatives as MurD inhibitors
    • Abstract: Publication date: Available online 22 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Mohammed Afzal Azam, Srikanth Jupudi
      The binding modes of well known MurD inhibitors have been studied using molecular docking and molecular dynamics (MD) simulations. The docking results of inhibitors 1-30 revealed similar mode of interaction with Escherichia coli-MurD. Further, residues Thr36, Arg37, His183, Lys319, Lys348, Thr321, Ser415 and Phe422 are found to be important for inhibitors and E. coli-MurD interactions. Our docking procedure precisely predicted crystallographic bound inhibitor 7 as evident from root mean square deviation (0.96Å). In addition inhibitors 2 and 3 have been successfully cross-docked within the MurD active site, which was pre-organized for the inhibitor 7. Induced fit best docked poses of 2, 3, 7 and 15/2Y1O complexes were subjected to 10ns MD simulations to determine the stability of the predicted binding conformations. Induce fit derived docked complexes were found to be in a state of near equilibrium as evident by the low root mean square deviations between the starting complex structure and the energy minimized final average MD complex structures. The results of molecular docking and MD simulations described in this study will be useful for the development of new MurD inhibitors with high potency.
      Graphical abstract image

      PubDate: 2017-05-24T15:42:00Z
       
  • IFC Editorial Board
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68


      PubDate: 2017-05-14T15:31:41Z
       
  • Title page
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68


      PubDate: 2017-05-14T15:31:41Z
       
  • Construction of new EST-SSRs for Fusarium resistant wheat breeding
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Aysen Yumurtaci, Hulya Sipahi, Ayed Al-Abdallat, Abdulqader Jighly, Michael Baum
      Surveying Fusarium resistance in wheat with easy applicable molecular markers such as simple sequence repeats (SSRs) is a prerequest for molecular breeding. Expressed sequence tags (ESTs) are one of the main sources for development of new SSR candidates. Therefore, 18.292 publicly available wheat ESTs were mined and genotyping of newly developed 55 EST-SSR derived primer pairs produced clear fragments in ten wheat cultivars carrying different levels of Fusarium resistance. Among the proved markers, 23 polymorphic EST-SSRs were obtained and related alleles were mostly found on B and D genome. Based on the fragment profiling and similarity analysis, a 327bp amplicon, which was a product of contig 1207 (chromosome 5BL), was detected only in Fusarium head blight (FHB) resistant cultivars (CM82036 and Sumai) and the amino acid sequences showed a similarity to pathogen related proteins. Another FHB resistance related EST-SSR, Contig 556 (chromosome 1BL) produced a 151bp fragment in Sumai and was associated to wax2-like protein. A polymorphic 204bp fragment, derived from Contig 578 (chromosome 1DL), was generated from root rot (FRR) resistant cultivars (2–49; Altay2000 and Sunco). A total of 98 alleles were displayed with an average of 1.8 alleles per locus and the polymorphic information content (PIC) ranged from 0.11 to 0.78. Dendrogram tree with two main and five sub-groups were displayed the highest genetic relationship between FRR resistant cultivars (2–49 and Altay2000), FRR sensitive cultivars (Seri82 and Scout66) and FHB resistant cultivars (CM82036 and Sumai). Thus, exploitation of these candidate EST-SSRs may help to genotype other wheat sources for Fusarium resistance.
      Graphical abstract image

      PubDate: 2017-05-14T15:31:41Z
       
  • 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.
      Graphical abstract image

      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.
      Graphical abstract image

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

      PubDate: 2017-04-21T02:31:08Z
       
  • Enhanced identification of β-lactamases and its classes using sequence,
           physicochemical and evolutionary information with sequence feature
           characterization of the classes
    • Abstract: Publication date: Available online 14 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Abhigyan Nath, S. Karthikeyan
      β-lactamases provides one of the most successful means of evading the therapeutic effects of β lactam class of antibiotics by many gram positive and gram negative bacteria. On the basis of sequence identity, β-lactamases have been identified into four distinct classes- A, B, C and D. The classes A, C and D are the serine β-lactamases and class B is the metallo-lactamse. In the present study, we developed a two stage cascade classification system. The first-stage performs the classification of β-lactamases from non-β-lactamases and the second-stage performs the further classification of β-lactamases into four different β–lactamase classes. In the first-stage binary classification, we obtained an accuracy of 97.3% with a sensitivity of 89.1% and specificity of 98.0% and for the second stage multi-class classification, we obtained an accuracy of 87.3% for the class A, 91.0% for the class B, 96.3% for the class C and 96.4% for class D. A systematic statistical analysis is carried out on the sieved-out, correctly-predicted instances from the second stage classifier, which revealed some interesting patterns. We analyzed different classes of β-lactamases on the basis of sequence and physicochemical property differences between them. Among amino acid composition, H, W, Y and V showed significant differences between the different β-lactamases classes. Differences in average physicochemical properties are observed for isoelectric point, volume, flexibility, hydrophobicity, bulkiness and charge in one or more β-lactamase classes. The key differences in physicochemical property groups can be observed in small and aromatic groups. Among amino acid property group n-grams except charged n-grams, all other property group n-grams are significant in one or more classes. Statistically significant differences in dipeptide counts among different β-lactamase classes are also reported.
      Graphical abstract image

      PubDate: 2017-02-19T07:37:52Z
       
  • In silico structural and functional analysis of Mesorhizobium ACC
           deaminase
    • Abstract: Publication date: Available online 11 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Krishnendu Pramanik, Tithi Soren, Soumik Mitra, Tushar Kanti Maiti
      Nodulation is one of the very important processes of legume plants as it is the initiating event of fixing nitrogen. Although ethylene has essential role in normal plant metabolism but it has also negative impact on plants particularly in nodule formation in legume plants. It is also produced due to a variety of biotic or abiotic stresses. 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase is a rhizobial enzyme which cleaves ACC (immediate precursor of ethylene) into α-ketobutyrate and ammonia. As a result, the level of ethylene from the plant cells is decreased and the negative impact of ethylene on nodule formation is reduced. ACC deaminase is widely studied in several plant growth promoting rhizobacterial (PGPR) strains including many legume nodulating bacteria like Mesorhizobium sp. It is an important symbiotic nitrogen fixer belonging to the class – alphaproteobacteria under the order Rhizobiales. ACC deaminase has positive role in Legume-rhizobium symbiosis. Rhizobial ACC deaminase has the potentiality to reduce the adverse effects of ethylene, thereby triggering the nodulation process. The present study describes an in silico comparative structural (secondary structure prediction, homology modeling) and functional analysis of ACC deaminase from Mesorhizobium spp. to explore physico-chemical properties using a number of bio-computational tools. M. loti was selected as a representative species of Mesorhizobium genera for 3D modelling of ACC deaminase protein. Correlation by the phylogenetic relatedness on the basis of both ACC deaminase enzymes and respective acdS genes of different strains of Mesorhizobium has also studied.
      Graphical abstract image

      PubDate: 2017-02-13T16:04:54Z
       
  • An in-silico approach to find a peptidomimetic targeting extracellular
           domain of HER3 from a HER3 Nanobody
    • Abstract: Publication date: Available online 10 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Z. Pourhashem, M. Mehrpouya, N. Yardehnavi, A. Eslamparast, F. Kazemi-Lomedasht
      HER3 is an important therapeutic target in cancer treatments. HER3 Nanobodies (Nbs) are a novel class of antibodies with several competitive advantages over conventional antibodies. A peptidomimetic derived from these Nbs can be considered to be a small peptide mimicking some of the molecular recognition interactions of a natural peptide or protein in a three-dimensional (3D) space, with a receptor that has improved properties. In this study, we introduce a new approach to design a peptidomimetic derived from HER3 Nb through an in silico analysis. We propose that the complementarity determining region (CDR3) of HER3 Nb is large enough to effectively interact with HER3 antigen as well as with the entire Nb. A computational analysis has been performed using Nb models retrieved from SWISS-pdb Viewer 4.1.0 (spdbv) as a target spot and HER3 extracellular domain as its antigenic target to identify the interactions between them by the protein-protein docking method. Detailed analysis of selected models with docked complex help us to identify the interacting amino acid residues between the two molecules. The results of in silico analysis show that the CDR3 of HER3 Nb might be used by itself as a peptidomimetic drug instead of the full Nb. HER3 peptidomimetic-derived HER3 Nb may reduce Nb production costs and be used as a substitute for HER3 Nb after further experimental work. The paper demonstrates the feasibility of peptidomimetics designs using bioinformatic tools.
      Graphical abstract image

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

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

      PubDate: 2017-02-06T15:49:06Z
       
 
 
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