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  Subjects -> ENGINEERING (Total: 2235 journals)
    - CHEMICAL ENGINEERING (188 journals)
    - CIVIL ENGINEERING (178 journals)
    - ELECTRICAL ENGINEERING (102 journals)
    - ENGINEERING (1194 journals)
    - ENGINEERING MECHANICS AND MATERIALS (374 journals)
    - HYDRAULIC ENGINEERING (54 journals)
    - INDUSTRIAL ENGINEERING (60 journals)
    - MECHANICAL ENGINEERING (85 journals)

CHEMICAL ENGINEERING (188 journals)                  1 2     

AATCC Journal of Research     Full-text available via subscription   (Followers: 4)
ACS Sustainable Chemistry & Engineering     Hybrid Journal  
Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials     Hybrid Journal   (Followers: 6)
Acta Polymerica     Hybrid Journal   (Followers: 7)
Additives for Polymers     Full-text available via subscription   (Followers: 20)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 5)
Advanced Chemical Engineering Research     Open Access   (Followers: 11)
Advanced Powder Technology     Hybrid Journal   (Followers: 16)
Advances in Applied Ceramics     Partially Free   (Followers: 3)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 18)
Advances in Chemical Engineering and Science     Open Access   (Followers: 26)
Advances in Polymer Technology     Hybrid Journal   (Followers: 11)
African Journal of Pure and Applied Chemistry     Open Access   (Followers: 5)
Annual Review of Analytical Chemistry     Full-text available via subscription   (Followers: 9)
Annual Review of Chemical and Biomolecular Engineering     Full-text available via subscription   (Followers: 10)
Anti-Corrosion Methods and Materials     Hybrid Journal   (Followers: 4)
Applied Petrochemical Research     Open Access   (Followers: 3)
Asia-Pacific Journal of Chemical Engineering     Hybrid Journal   (Followers: 6)
Biochemical Engineering Journal     Hybrid Journal   (Followers: 9)
Biofuel Research Journal     Open Access   (Followers: 3)
Biomass Conversion and Biorefinery     Partially Free   (Followers: 8)
Brazilian Journal of Chemical Engineering     Open Access   (Followers: 3)
Bulletin of Chemical Reaction Engineering & Catalysis     Open Access  
Bulletin of the Chemical Society of Ethiopia     Open Access   (Followers: 3)
Carbohydrate Polymers     Hybrid Journal   (Followers: 9)
Catalysts     Open Access   (Followers: 8)
ChemBioEng Reviews     Full-text available via subscription  
Chemical and Engineering News     Free   (Followers: 7)
Chemical and Materials Engineering     Open Access   (Followers: 2)
Chemical and Petroleum Engineering     Hybrid Journal   (Followers: 11)
Chemical and Process Engineering     Open Access   (Followers: 4)
Chemical and Process Engineering Research     Open Access   (Followers: 6)
Chemical Communications     Full-text available via subscription   (Followers: 37)
Chemical Engineering & Technology     Hybrid Journal   (Followers: 25)
Chemical Engineering and Processing: Process Intensification     Hybrid Journal   (Followers: 10)
Chemical Engineering and Science     Open Access   (Followers: 3)
Chemical Engineering Communications     Hybrid Journal   (Followers: 11)
Chemical Engineering Journal     Hybrid Journal   (Followers: 24)
Chemical Engineering Research and Design     Hybrid Journal   (Followers: 19)
Chemical Engineering Research Bulletin     Open Access   (Followers: 1)
Chemical Engineering Science     Hybrid Journal   (Followers: 18)
Chemical Geology     Hybrid Journal   (Followers: 11)
Chemical Papers     Hybrid Journal   (Followers: 3)
Chemical Product and Process Modeling     Hybrid Journal   (Followers: 3)
Chemical Reviews     Full-text available via subscription   (Followers: 128)
Chemical Society Reviews     Full-text available via subscription   (Followers: 37)
Chemical Technology     Open Access   (Followers: 5)
ChemInform     Hybrid Journal   (Followers: 3)
Chemistry & Industry     Hybrid Journal   (Followers: 2)
Chemistry Central Journal     Open Access   (Followers: 7)
Chemistry of Materials     Full-text available via subscription   (Followers: 134)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 8)
ChemSusChem     Hybrid Journal   (Followers: 8)
Chinese Chemical Letters     Full-text available via subscription   (Followers: 3)
Chinese Journal of Chemical Engineering     Full-text available via subscription   (Followers: 3)
Chinese Journal of Chemical Physics     Hybrid Journal   (Followers: 2)
Coke and Chemistry     Hybrid Journal  
Coloration Technology     Hybrid Journal   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 9)
Computer Aided Chemical Engineering     Full-text available via subscription   (Followers: 2)
Computers & Chemical Engineering     Hybrid Journal   (Followers: 8)
CORROSION     Full-text available via subscription   (Followers: 18)
Corrosion Engineering, Science and Technology     Hybrid Journal   (Followers: 38)
Corrosion Reviews     Hybrid Journal   (Followers: 5)
Crystal Research and Technology     Hybrid Journal   (Followers: 2)
Current Opinion in Chemical Engineering     Open Access   (Followers: 3)
Education for Chemical Engineers     Hybrid Journal   (Followers: 4)
Eksergi     Open Access  
Emerging Trends in Chemical Engineering     Full-text available via subscription  
European Polymer Journal     Hybrid Journal   (Followers: 44)
Fibers and Polymers     Full-text available via subscription   (Followers: 3)
Fluorescent Materials     Open Access   (Followers: 1)
Focusing on Modern Food Industry     Open Access   (Followers: 3)
Frontiers of Chemical Science and Engineering     Hybrid Journal   (Followers: 1)
Gels     Open Access  
Geochemistry International     Hybrid Journal  
Handbook of Powder Technology     Full-text available via subscription   (Followers: 3)
Heat Exchangers     Open Access   (Followers: 1)
High Performance Polymers     Hybrid Journal  
Hungarian Journal of Industry and Chemistry     Open Access  
Indian Chemical Engineer     Hybrid Journal   (Followers: 4)
Indian Journal of Chemical Technology (IJCT)     Open Access   (Followers: 11)
Indonesian Journal of Chemical Science     Open Access  
Industrial & Engineering Chemistry     Full-text available via subscription   (Followers: 10)
Industrial & Engineering Chemistry Research     Full-text available via subscription   (Followers: 20)
Industrial Chemistry Library     Full-text available via subscription   (Followers: 4)
Info Chimie Magazine     Full-text available via subscription   (Followers: 2)
International Journal of Chemical and Petroleum Sciences     Open Access   (Followers: 2)
International Journal of Chemical Engineering     Open Access   (Followers: 7)
International Journal of Chemical Reactor Engineering     Hybrid Journal   (Followers: 3)
International Journal of Chemical Technology     Open Access   (Followers: 4)
International Journal of Chemoinformatics and Chemical Engineering     Full-text available via subscription   (Followers: 2)
International Journal of Food Science     Open Access   (Followers: 4)
International Journal of Industrial Chemistry     Open Access  
International Journal of Polymeric Materials     Hybrid Journal   (Followers: 4)
International Journal of Science and Engineering     Open Access   (Followers: 7)
International Journal of Waste Resources     Open Access   (Followers: 5)
Journal of Chemical Engineering & Process Technology     Open Access   (Followers: 3)
Journal of Applied Crystallography     Hybrid Journal   (Followers: 5)
Journal of Applied Electrochemistry     Hybrid Journal   (Followers: 11)

        1 2     

Journal Cover Computational Biology and Chemistry
  [SJR: 0.688]   [H-I: 43]   [9 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1476-9271
   Published by Elsevier Homepage  [2801 journals]
  • Predicting human intestinal absorption of diverse chemicals using ensemble
           learning based QSAR modeling approaches
    • Abstract: Publication date: Available online 29 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Nikita Basant, Shikha Gupta, Kunwar P. Singh
      Human intestinal absorption (HIA) of the drugs administered through the oral route constitutes an important criterion for the candidate molecules. The computational approach for predicting the HIA of molecules may potentiate the screening of new drugs. In this study, ensemble learning (EL) based qualitative and quantitative structure-activity relationship (SAR) models (gradient boosted tree, GBT and bagged decision tree, BDT) have been established for the binary classification and HIA prediction of the chemicals, using the selected molecular descriptors. The structural diversity of the chemicals and the nonlinear structure in the considered data were tested by the similarity index and Brock-Dechert-Scheinkman statistics. The external predictive power of the developed SAR models was evaluated through the internal and external validation procedures recommended in the literature. All the statistical criteria parameters derived for the performance of the constructed SAR models were above their respective thresholds suggesting for their robustness for future applications. In complete data, the qualitative SAR models rendered classification accuracy of  >99%, while the quantitative SAR models yielded correlation (R2) of >0.91 between the measured and predicted HIA values. The performances of the EL-based SAR models were also compared with the linear models (linear discriminant analysis, LDA and multiple linear regression, MLR). The GBT and BDT SAR models performed better than the LDA and MLR methods. A comparison of our models with the previously reported QSARs for HIA prediction suggested for their better performance. The results suggest for the appropriateness of the developed SAR models to reliably predict the HIA of structurally diverse chemicals and can serve as useful tools for the initial screening of the molecules in the drug development process.
      Graphical abstract image

      PubDate: 2016-01-31T02:20:42Z
       
  • Structural Characterization of ANGPTL8 (Betatrophin) With its Interacting
           Partner Lipoprotein Lipase
    • Abstract: Publication date: Available online 25 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Amnah Siddiqa, Jamil Ahmad, Amjad Ali, Rehan Zafar Paracha, Zurah Shafi, Babar Aslam
      Angiopoietin-like protein 8 (ANGPTL8) (also known as betatrophin) is a newly identified secretory protein with a potential role in autophagy, lipid metabolism and pancreatic beta-cell proliferation. Its structural characterization is required to enhance our current understanding of its mechanism of action which could help in identifying its receptor and/or other binding partners. Based on the physiological significance and necessity of exploring structural features of ANGPTL8, the present study is conducted with a specific aim to model the structure of ANGPTL8 and study its possible interactions with Lipoprotein Lipase (LPL). To the best of our knowledge, this is the first attempt to predict 3-dimensional (3D) structure of ANGPTL8. Three different approaches were used for modeling of ANGPTL8 including homology modeling, de-novo structure prediction and their amalgam which is then proceeded by structure verification using ERRATT, PROSA, Qmean and Ramachandran plot scores. The selected models of ANGPTL8 were further evaluated for protein-protein interaction (PPI) analysis with LPL using CPORT and HADDOCK server. Our results have shown that the crystal structure of iSH2 domain of Phosphatidylinositol 3-kinase (PI3K) p85β subunit (PDB entry: 3mtt) is a good candidate for homology modeling of ANGPTL8. Analysis of inter-molecular interactions between the structure of ANGPTL8 and LPL revealed existence of several non covalent interactions. The residues of LPL involved in these interactions belong from its lid region, thrombospondin (TSP) region and heparin binding site which is suggestive of a possible role of ANGPTL8 in regulating the proteolysis, motility and localization of LPL. Besides, the conserved residues of SE1 region of ANGPTL8 formed interactions with the residues around the hinge region of LPL. Overall, our results support a model of inhibition of LPL by ANGPTL8 through the steric block of its catalytic site which will be further explored using wet lab studies in future.
      Graphical abstract image Highlights

      PubDate: 2016-01-31T02:20:42Z
       
  • A multilevel ant colony optimization algorithm for classical and
           isothermic DNA sequencing by hybridization with multiplicity information
           available
    • Abstract: Publication date: Available online 28 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Kamil Kwarciak, Marcin Radom, Piotr Formanowicz
      The classical sequencing by hybridization takes into account a binary information about sequence composition. A given element from an oligonucleotide library is or is not a part of the target sequence. However, the DNA chip technology has been developed and it enables to receive a partial information about multiplicity of each oligonucleotide the analyzed sequence consist of. Currently, it is not possible to assess the exact data of such type but even partial information should be very useful. Two realistic multiplicity information models are taken into consideration in this paper. The first one, called “one and many” assumes that it is possible to obtain information if a given oligonucleotide occurs in a reconstructed sequence once or more than once. According to the second model, called “one, two and many”, one is able to receive from biochemical experiment information if a given oligonucleotide is present in an analysed sequence once, twice or at least three times. An ant colony optimization algorithm has been implemented to verify the above models and to compare with existing algorithms for sequencing by hybridization which utilize the additional information. The proposed algorithm solves the problem with any kind of hybridization errors. Computational experiment results confirm that using even the partial information about multiplicity leads to increased quality of reconstructed sequences. Moreover, they also show that the more precise model enables to obtain better solutions and the ant colony optimization algorithm outperforms the existing ones. Test data sets and the proposed ant colony optimization algorithm are available on: http://bioserver.cs.put.poznan.pl/download/ACO4mSBH.zip.
      Graphical abstract image Highlights

      PubDate: 2016-01-31T02:20:42Z
       
  • Carcinogenicity prediction of noncongeneric chemicals by augmented top
           priority fragment classification
    • Abstract: Publication date: Available online 29 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Mosè Casalegno, Guido Sello
      Carcinogenicity prediction is an important process that can be performed to cut down experimental costs and save animal lives. The current reliability of the results is however disputed. Here, a blind exercise in carcinogenicity category assessment is performed using augmented top priority fragment classification. The procedure analyses the applicability domain of the dataset, allocates in clusters the compounds using a leading molecular fragment, and a similarity measure. The exercise is applied to three compound datasets derived from the Lois Gold Carcinogenic Database. The results, showing good agreement with experimental data, are compared with published ones. A final discussion on our viewpoint on the possibilities that the carcinogenicity modelling of chemical compounds offers is presented.
      Graphical abstract image

      PubDate: 2016-01-31T02:20:42Z
       
  • On origin and evolution of carbonic anhydrase isozymes: A phylogenetic
           analysis from whole-enzyme to active site
    • Abstract: Publication date: Available online 23 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Srijoni Banerjee, Parag A. Deshpande
      Genetic evolution of carbonic anhydrase enzyme provides an interesting instance of functional similarity in spite of structural diversity of the members of a given family of enzymes. Phylogenetic analysis of α-, β- and γ-carbonic anhydrase was carried out to determine the evolutionary relationships among various members of the family with the enzyme marking its presence in a wide range of cellular and chromosomal locations. The presence of more than one classes of enzymes in a particular organism was revealed by phylogenetic time tree. The evolutionary relationships among the members of animal, plant and microbial kingdom were developed. The study revises a long-established notion of kingdom-specificity of the different classes of carbonic anhydrases and provides a new version of the presence of multiple classes of carbonic anhydrases in a single organism and the presence of a given class of carbonic anhydrase across different kingdoms.
      Graphical abstract image Highlights

      PubDate: 2016-01-25T14:15:05Z
       
  • Identification of possible siRNA Molecules for TDP43 Mutants causing
           Amyotrophic Lateral Sclerosis: in silico design and molecular dynamics
           study
    • Abstract: Publication date: Available online 23 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Vishwambhar Vishnu Bhandare, Amutha Ramaswamy
      The DNA binding protein, TDP43 is a major protein involved in Amyotrophic Lateral Sclerosis and other neurological disorders such as Frontotemporal dementia, Alzheimer disease, etc. In the present study, we have designed possible siRNAs for the glycine rich region of tardbp mutants causing ALS disorder based on a systematic theoretical approach including (i) identification of respective codons for all mutants (reported at the protein level) based on both minimum free energy and probabilistic approaches, (ii) rational design of siRNA, (iii) secondary structure analysis for the target accessibility of siRNA, (iii) determination of the ability of siRNA to interact with mRNA and the formation/stability of duplex via molecular dynamics study for a period of 15ns and (iv) characterization of mRNA-siRNA duplex stability based on thermo-physical analysis. The stable GC-rich siRNA expressed strong binding affinity towards mRNA and forms stable duplex in A-form. The linear dependence between the thermo-physical parameters such as Tm, GC content and binding free energy revealed the ability of the identified siRNAs to interact with mRNA in comparable to that of the experimentally reported siRNAs. Hence, this present study proposes few siRNAs as the possible gene silencing agents in RNAi therapy based on the in silico approach.
      Graphical abstract image

      PubDate: 2016-01-25T14:15:05Z
       
  • Synthesis and In Silico Investigation of Thiazoles Bearing Pyrazoles
           Derivatives As Anti-Inflammatory Agents
    • Abstract: Publication date: Available online 23 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Rahul D. Kamble, Rohan J. Meshram, Shrikant V. Hese, Rahul A. More, Sonali S. Kamble, Rajesh N. Gacche, Bhaskar S. Dawane
      Searching novel, safe and effective anti-inflammatory agents has remained an evolving research enquiry in the mainstream of inflammatory disorders. In the present investigation series of thiazoles bearing pyrazole as a possible pharmacophore were synthesized and assessed for their anti inflammatory activity using in vitro and in vivo methods. In order to decipher the possible anti-inflammatory mechanism of action of the synthesized compounds, cyclooxygenase I and II (COX-I and COX-II) inhibition assays were also carried out. The results obtained clearly focus the significance of compounds 5d, 5 h and 5i as selective COX-II inhibitors. Moreover compound 5 h was also identified as a lead molecule for inhibition of the carrageenin induced rat paw edema in animal model studies. Molecular docking results revealed significant interactions of the test compounds with the active site of COX-II, which perhaps can be explored for design and development of novel COX-II selective anti-inflammatory agents.
      Graphical abstract image

      PubDate: 2016-01-25T14:15:05Z
       
  • Insights into the functions of M-T hook structure in HIV fusion inhibitor
           using molecular modeling
    • Abstract: Publication date: Available online 23 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Jianjun Tan, Hongling Yuan, Chunhua Li, Xiaoyi Zhang, Cunxin Wang
      HIV-1 membrane fusion plays an important role in the process that HIV-1 entries host cells. As a treatment strategy targeting HIV-1 entry process, fusion inhibitors have been proposed. Nevertheless, development of a short peptide possessing high anti-HIV potency is considered a daunting challenge. He et al. found that two residues, Met626 and Thr627, located the upstream of the C-terminal heptad repeat of the gp41, formed a unique hook-like structure (M-T hook) that can dramatically improve the binding stability and anti-HIV activity of the inhibitors. In this work, we explored the molecular mechanism why M-T hook structure could improve the anti-HIV activity of inhibitors. Firstly, molecular dynamic simulation was used to obtain information on the time evolution between gp41 and ligands. Secondly, based on the simulations, molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and molecular mechanics Generalized Born surface area (MM-GBSA) methods were used to calculate the binding free energies. The binding free energy of the ligand with M-T hook was considerably higher than the other without M-T. Further studies showed that the hydrophobic interactions made the dominant contribution to the binding free energy. The numbers of Hydrogen bonds between gp41 and the ligand with M-T hook structure were more than the other. These findings should provide insights into the inhibition mechanism of the short peptide fusion inhibitors and be useful for the rational design of novel fusion inhibitors in the future.
      Graphical abstract image

      PubDate: 2016-01-25T14:15:05Z
       
  • Investigation on the isoform selectivity of novel kinesin-like protein 1
           (KIF11) inhibitor using chemical feature based pharmacophore, molecular
           docking, and quantum mechanical studies
    • Abstract: Publication date: Available online 15 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Subramanian Karunagaran, Subramaniyan Subhashchandrabose, Keun Woo Lee, Chandrasekaran Meganathan
      Kinesin-like protein (KIF11) is a molecular motor protein that is essential in mitosis. Removal of KIF11 prevents centrosome migration and causes cell arrest in mitosis. KIF11 defects are linked to the disease of microcephaly, lymph edema or mental retardation. The human KIF11 protein has been actively studied for its role in mitosis and its potential as a therapeutic target for cancer treatment. Pharmacophore modeling, molecular docking and density functional theory approaches was employed to reveal the structural, chemical and electronic features essential for the development of small molecule inhibitor for KIF11. Hence we have developed chemical feature based pharmacophore models using Discovery Studio v 2.5 (DS). The best hypothesis (Hypo1) consisting of four chemical features (two hydrogen bond acceptor, one hydrophobic and one ring aromatic) has exhibited high correlation co-efficient of 0.9521, cost difference of 70.63 and low RMS value of 0.9475. This Hypo1 is cross validated by Cat Scramble method; test set and decoy set to prove its robustness, statistical significance and predictability respectively. The well validated Hypo1 was used as 3Dquery to perform virtual screening. The hits obtained from the virtual screening were subjected to various scrupulous drug-like filters such as Lipinski's rule of five and ADMET properties. Finally, six hit compounds were identified based on the molecular interaction and its electronic properties. Our final lead compound could serve as a powerful tool for the discovery of potent inhibitor as KIF11 agonists.
      Graphical abstract image

      PubDate: 2016-01-16T14:00:41Z
       
  • Dynamic conformational ensembles regulate casein kinase-1 isoforms:
           Insights from molecular dynamics and molecular docking studies
    • Abstract: Publication date: April 2016
      Source:Computational Biology and Chemistry, Volume 61
      Author(s): Surya Pratap Singh, Dwijendra K. Gupta
      Casein kinase-1 (CK1) isoforms actively participate in the down-regulation of canonical Wnt signaling pathway; however recent studies have shown their active roles in oncogenesis of various tissues through this pathway. Functional loss of two isoforms (CK1-α/ε) has been shown to activate the carcinogenic pathway which involves the stabilization of of cytoplasmic β-catenin. Development of anticancer therapeutics is very laborious task and depends upon the structural and conformational details of the target. This study focuses on, how the structural dynamics and conformational changes of two CK1 isoforms are synchronized in carcinogenic pathway. The conformational dynamics in kinases is the responsible for their action as has been supported by the molecular docking experiments.
      Graphical abstract image

      PubDate: 2016-01-13T13:55:49Z
       
  • IFC Editorial Board
    • Abstract: Publication date: February 2016
      Source:Computational Biology and Chemistry, Volume 60




      PubDate: 2016-01-08T13:36:16Z
       
  • Title page
    • Abstract: Publication date: February 2016
      Source:Computational Biology and Chemistry, Volume 60




      PubDate: 2016-01-08T13:36:16Z
       
  • Publisher's note
    • Abstract: Publication date: February 2016
      Source:Computational Biology and Chemistry, Volume 60




      PubDate: 2016-01-08T13:36:16Z
       
  • Comparison of Non-Sequential Sets of Protein Residues
    • Abstract: Publication date: Available online 25 December 2015
      Source:Computational Biology and Chemistry
      Author(s): Leonardo D. Garma, André H. Juffer
      A methodology for performing sequence-free comparison of functional sites in protein structures is introduced. The method is based on a new notion of similarity among superimposed groups of amino acid residues that evaluates both geometry and physico-chemical properties. The method is specifically designed to handle disconnected and sparsely distributed sets of residues. A genetic algorithm is employed to find the superimposition of protein segments that maximizes their similarity. The method was evaluated by performing an all-to-all comparison on two separate sets of ligand-binding sites, comprising 47 protein-FAD (Flavin-Adenine Dinucleotide) and 64 protein-NAD (Nicotinamide-Adenine Dinucleotide) complexes, and comparing the results with those of an existing sequence-based structural alignment tool (TM-Align). The quality of the two methodologies is judged by the methods’ capacity to, among other, correctly predict the similarities in the protein-ligand contact patterns of each pair of binding sites. The results show that using a sequence-free method significantly improves over the sequence-based one, resulting in 23 significant binding-site homologies being detected by the new method but ignored by the sequence-based one.


      PubDate: 2015-12-27T13:03:28Z
       
  • Estimation of kinetic parameters of transcription from temporal single-RNA
           measurements
    • Abstract: Publication date: Available online 17 December 2015
      Source:Computational Biology and Chemistry
      Author(s): Christoph Zimmer, Antti Häkkinen, Andre S. Ribeiro
      Gene expression dynamics in prokaryotes is largely controlled by the multi-step process of transcription initiation whose kinetics is subject to regulation. Since the number and duration of these steps cannot be currently measured in vivo, we propose a novel method for estimating them from time series of RNA numbers in individual cells. We demonstrate the method's applicability on measurements of fluorescence-tagged RNA molecules in E. coli cells, and compare with a previous method. We show that the results of the two methods agree for equal data. We also show that, when incorporating additional data, the new method produces significantly different estimates, which are in closer agreement with qPCR measurements. Unlike the previous method, the new method requires no preprocessing of the RNA numbers, using maximal information from the RNA time series. In addition, it can use data outside of the observed RNA productions. Overall, the new method characterizes the transcription initiation process with enhanced detail.
      Graphical abstract image Highlights

      PubDate: 2015-12-19T12:49:51Z
       
  • A survey of disease connections for CD4+ T cell master genes and their
           directly linked genes
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part B
      Author(s): Wentian Li, Jesús Espinal-Enríquez, Kim R. Simpfendorfer, Enrique Hernández-Lemus
      Genome-wide association studies and other genetic analyses have identified a large number of genes and variants implicating a variety of disease etiological mechanisms. It is imperative for the study of human diseases to put these genetic findings into a coherent functional context. Here we use system biology tools to examine disease connections of five master genes for CD4+ T cell subtypes (TBX21, GATA3, RORC, BCL6, and FOXP3). We compiled a list of genes functionally interacting (protein–protein interaction, or by acting in the same pathway) with the master genes, then we surveyed the disease connections, either by experimental evidence or by genetic association. Embryonic lethal genes (also known as essential genes) are over-represented in master genes and their interacting genes (55% versus 40% in other genes). Transcription factors are significantly enriched among genes interacting with the master genes (63% versus 10% in other genes). Predicted haploinsufficiency is a feature of most these genes. Disease-connected genes are enriched in this list of genes: 42% of these genes have a disease connection according to Online Mendelian Inheritance in Man (OMIM) (versus 23% in other genes), and 74% are associated with some diseases or phenotype in a Genome Wide Association Study (GWAS) (versus 43% in other genes). Seemingly, not all of the diseases connected to genes surveyed were immune related, which may indicate pleiotropic functions of the master regulator genes and associated genes.


      PubDate: 2015-12-12T12:42:10Z
       
  • Transcriptional master regulator analysis in breast cancer genetic
           networks
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part B
      Author(s): Hugo Tovar, Rodrigo García-Herrera, Jesús Espinal-Enríquez, Enrique Hernández-Lemus
      Gene regulatory networks account for the delicate mechanisms that control gene expression. Under certain circumstances, gene regulatory programs may give rise to amplification cascades. Such transcriptional cascades are events in which activation of key-responsive transcription factors called master regulators trigger a series of gene expression events. The action of transcriptional master regulators is then important for the establishment of certain programs like cell development and differentiation. However, such cascades have also been related with the onset and maintenance of cancer phenotypes. Here we present a systematic implementation of a series of algorithms aimed at the inference of a gene regulatory network and analysis of transcriptional master regulators in the context of primary breast cancer cells. Such studies were performed in a highly curated database of 880 microarray gene expression experiments on biopsy-captured tissue corresponding to primary breast cancer and healthy controls. Biological function and biochemical pathway enrichment analyses were also performed to study the role that the processes controlled – at the transcriptional level – by such master regulators may have in relation to primary breast cancer. We found that transcription factors such as AGTR2, ZNF132, TFDP3 and others are master regulators in this gene regulatory network. Sets of genes controlled by these regulators are involved in processes that are well-known hallmarks of cancer. This kind of analyses may help to understand the most upstream events in the development of phenotypes, in particular, those regarding cancer biology.
      Graphical abstract image Highlights

      PubDate: 2015-12-12T12:42:10Z
       
  • Reconstructing gene regulatory networks from knock-out data using Gaussian
           Noise Model and Pearson Correlation Coefficient
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part B
      Author(s): Faridah Hani Mohamed Salleh, Shereena Mohd Arif, Suhaila Zainudin, Mohd Firdaus-Raih
      A gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other’s state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5.
      Graphical abstract image

      PubDate: 2015-12-12T12:42:10Z
       
  • Dynamics of p53 and Wnt cross talk
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part B
      Author(s): Md. Zubbair Malik, Shahnawaz Ali, Md. Jahoor Alam, Romana Ishrat, R.K. Brojen Singh
      We present the mechanism of interaction of Wnt network module, which is responsible for periodic somitogenesis, with p53 regulatory network, which is one of the main regulators of various cellular functions, and switching of various oscillating states by investigating p53–Wnt model. The variation in Nutlin concentration in p53 regulating network drives the Wnt network module to different states, stabilized, damped and sustain oscillation states, and even to cycle arrest. Similarly, the change in Axin2 concentration in Wnt could able to modulate the p53 dynamics at these states. We then solve the set of coupled ordinary differential equations of the model using quasi steady state approximation. We, further, demonstrate the change of p53 and GSK3 interaction rate, due to hypothetical catalytic reaction or external stimuli, can able to regulate the dynamics of the two network modules, and even can control their dynamics to protect the system from cycle arrest (apoptosis).
      Graphical abstract image Highlights

      PubDate: 2015-12-12T12:42:10Z
       
  • Title page
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part A




      PubDate: 2015-12-12T12:42:10Z
       
  • Title page
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part B




      PubDate: 2015-12-12T12:42:10Z
       
  • Advances in systems biology – New trends and perspectives
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part B
      Author(s): Enrique Hernández-Lemus, Wentian Li, Pablo Meyer



      PubDate: 2015-12-12T12:42:10Z
       
  • A robust and efficient method for estimating enzyme complex abundance and
           metabolic flux from expression data
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part B
      Author(s): Brandon E. Barker, Narayanan Sadagopan, Yiping Wang, Kieran Smallbone, Christopher R. Myers, Hongwei Xi, Jason W. Locasale, Zhenglong Gu
      A major theme in constraint-based modeling is unifying experimental data, such as biochemical information about the reactions that can occur in a system or the composition and localization of enzyme complexes, with high-throughput data including expression data, metabolomics, or DNA sequencing. The desired result is to increase predictive capability and improve our understanding of metabolism. The approach typically employed when only gene (or protein) intensities are available is the creation of tissue-specific models, which reduces the available reactions in an organism model, and does not provide an objective function for the estimation of fluxes. We develop a method, flux assignment with LAD (least absolute deviation) convex objectives and normalization (FALCON), that employs metabolic network reconstructions along with expression data to estimate fluxes. In order to use such a method, accurate measures of enzyme complex abundance are needed, so we first present an algorithm that addresses quantification of complex abundance. Our extensions to prior techniques include the capability to work with large models and significantly improved run-time performance even for smaller models, an improved analysis of enzyme complex formation, the ability to handle large enzyme complex rules that may incorporate multiple isoforms, and either maintained or significantly improved correlation with experimentally measured fluxes. FALCON has been implemented in MATLAB and ATS, and can be downloaded from: https://github.com/bbarker/FALCON. ATS is not required to compile the software, as intermediate C source code is available. FALCON requires use of the COBRA Toolbox, also implemented in MATLAB.


      PubDate: 2015-12-12T12:42:10Z
       
  • IFC Editorial Board
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part A




      PubDate: 2015-12-12T12:42:10Z
       
  • IFC Editorial Board
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part B




      PubDate: 2015-12-12T12:42:10Z
       
  • A combined systems and structural modeling approach repositions
           antibiotics for Mycoplasma genitalium
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part B
      Author(s): Denis Kazakiewicz, Jonathan R. Karr, Karol M. Langner, Dariusz Plewczynski
      Bacteria are increasingly resistant to existing antibiotics, which target a narrow range of pathways. New methods are needed to identify targets, including repositioning targets among distantly related species. We developed a novel combination of systems and structural modeling and bioinformatics to reposition known antibiotics and targets to new species. We applied this approach to Mycoplasma genitalium, a common cause of urethritis. First, we used quantitative metabolic modeling to identify enzymes whose expression affects the cellular growth rate. Second, we searched the literature for inhibitors of homologs of the most fragile enzymes. Next, we used sequence alignment to assess that the binding site is shared by M. genitalium, but not by humans. Lastly, we used molecular docking to verify that the reported inhibitors preferentially interact with M. genitalium proteins over their human homologs. Thymidylate kinase was the top predicted target and piperidinylthymines were the top compounds. Further work is needed to experimentally validate piperidinylthymines. In summary, combined systems and structural modeling is a powerful tool for drug repositioning.
      Graphical abstract image

      PubDate: 2015-12-12T12:42:10Z
       
  • Molecular Design and Structural Optimization of Potent Peptide Hydroxamate
           Inhibitors to Selectively Target Human ADAM Metallopeptidase Domain 17
    • Abstract: Publication date: Available online 8 December 2015
      Source:Computational Biology and Chemistry
      Author(s): Zhengting Wang, Lei Wang, Rong Fan, Jie Zhou, Jie Zhong
      Human ADAMs (a disintegrin and metalloproteinases) have been established as an attractive therapeutic target of inflammatory disorders such as inflammatory bowel disease (IBD). The ADAM metallopeptidase domain 17 (ADAM17 or TACE) and its close relative ADAM10 are two of the most important ADAM members that share high conservation in sequence, structure and function, but exhibit subtle difference in regulation of downstream cell signaling events. Here, we described a systematic protocol that combined computational modeling and experimental assay to discover novel peptide hydroxamate derivatives as potent and selective inhibitors for ADAM17 over ADAM10. In the procedure, a virtual combinatorial library of peptide hydroxamate compounds was generated by exploiting intermolecular interactions involved in crystal and modeled structures. The library was examined in detail to identify few promising candidates with both high affinity to ADAM17 and low affinity to ADAM10, which were then tested in vitro with enzyme inhibition assay. Consequently, two peptide hydroxamates Hxm-Phe-Ser-Asn and Hxm-Phe-Arg-Gln were found to exhibit potent inhibition against ADAM17 (K i =92 and 47nM, respectively) and strong selectivity for ADAM17 over ADAM10 (∼7-fold and ∼5-fold, S =0.86 and 0.71, respectively). The structural basis and energetic property of ADAM17 and ADAM10 interactions with the designed inhibitors were also investigated systematically. It is found that the exquisite network of nonbonded interactions involving the side chains of peptide hydroxamates is primarily responsible for inhibitor selectivity, while the coordination interactions and hydrogen bonds formed by the hydroxamate moiety and backbone of peptide hydroxamates confer high affinity to inhibitor binding.
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      PubDate: 2015-12-12T12:42:10Z
       
  • Crosstalk events in the estrogen signaling pathway may affect tamoxifen
           efficacy in breast cancer molecular subtypes
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part B
      Author(s): Guillermo de Anda-Jáuregui, Raúl A. Mejía-Pedroza, Jesús Espinal-Enríquez, Enrique Hernández-Lemus
      Steroid hormones are involved on cell growth, development and differentiation. Such effects are often mediated by steroid receptors. One paradigmatic example of this coupling is the estrogen signaling pathway. Its dysregulation is involved in most tumors of the mammary gland. It is thus an important pharmacological target in breast cancer. This pathway, however, crosstalks with several other molecular pathways, a fact that may have consequences for the effectiveness of hormone modulating drug therapies, such as tamoxifen. For this work, we performed a systematic analysis of the major routes involved in crosstalk phenomena with the estrogen pathway – based on gene expression experiments (819 samples) and pathway analysis (493 samples) – for biopsy-captured tissue and contrasted in two independent datasets with in vivo and in vitro pharmacological stimulation. Our results confirm the presence of a number of crosstalk events across the estrogen signaling pathway with others that are dysregulated in different molecular subtypes of breast cancer. These may be involved in proliferation, invasiveness and apoptosis-evasion in patients. The results presented may open the way to new designs of adjuvant and neoadjuvant therapies for breast cancer treatment.


      PubDate: 2015-12-12T12:42:10Z
       
  • Core and peripheral connectivity based cluster analysis over PPI network
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part B
      Author(s): Hasin A. Ahmed, Dhruba K. Bhattacharyya, Jugal K. Kalita
      A number of methods have been proposed in the literature of protein–protein interaction (PPI) network analysis for detection of clusters in the network. Clusters are identified by these methods using various graph theoretic criteria. Most of these methods have been found time consuming due to involvement of preprocessing and post processing tasks. In addition, they do not achieve high precision and recall consistently and simultaneously. Moreover, the existing methods do not employ the idea of core-periphery structural pattern of protein complexes effectively to extract clusters. In this paper, we introduce a clustering method named CPCA based on a recent observation by researchers that a protein complex in a PPI network is arranged as a relatively dense core region and additional proteins weakly connected to the core. CPCA uses two connectivity criterion functions to identify core and peripheral regions of the cluster. To locate initial node of a cluster we introduce a measure called DNQ (Degree based Neighborhood Qualification) index that evaluates tendency of the node to be part of a cluster. CPCA performs well when compared with well-known counterparts. Along with protein complex gold standards, a co-localization dataset has also been used for validation of the results.
      Graphical abstract image Highlights

      PubDate: 2015-12-12T12:42:10Z
       
  • Transcriptome-wide identification of Rauvolfia serpentina microRNAs and
           prediction of their potential targets
    • Abstract: Publication date: Available online 9 December 2015
      Source:Computational Biology and Chemistry
      Author(s): Pravin Prakash, Raja Rajakani, Vikrant Gupta
      MicroRNAs (miRNAs) are small non-coding RNAs of ∼19–24 nucleotides (nt) in length and considered as potent regulators of gene expression at transcriptional and post-transcriptional levels. Here we report the identification and characterization of 15 conserved miRNAs belonging to 13 families from Rauvolfia serpentina through in silico analysis of available nucleotide dataset. The identified mature R. serpentina miRNAs (rse-miRNAs) ranged between 20 and 22 nt in length, and the average minimal folding free energy index (MFEI) value of rse-miRNA precursor sequences was found to be –0.815kcal/mol. Using the identified rse-miRNAs as query their potential targets were predicted in R. serpentina and other plant species. Gene Ontology (GO) annotation showed that predicted targets of rse-miRNAs include transcription factors as well as genes involved in diverse biological processes such as primary and secondary metabolism, stress response, disease resistance, growth, and development. Few rse-miRNAs were predicted to target genes of pharmaceutically important secondary metabolic pathways such as alkaloids and anthocyanin biosynthesis. Phylogenetic analysis showed the evolutionary relationship of rse-miRNAs and their precursor sequences to homologous pre-miRNA sequences from other plant species. The findings under present study besides giving first hand information about R. serpentina miRNAs and their targets, also contributes towards the better understanding of miRNA-mediated gene regulatory processes in plants.
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      PubDate: 2015-12-12T12:42:10Z
       
  • Analysis of Molecular Structures and Mechanisms for Toxins Derived from
           Venomous Animals
    • Abstract: Publication date: Available online 2 December 2015
      Source:Computational Biology and Chemistry
      Author(s): L.F.O. Rocha
      As predominant component in the venom of many dangerous animal species, toxins have been thoroughly investigated for drug design or as pharmacologic tools. The present study demonstrated the use of size and hydrophobicity of amino acid residues for the purposes of quantifying the valuable sequence–structure relationship and performing further analysis of interactional mechanisms in secondary structure elements (SSEs) for toxin native conformations. First, we showed that the presence of large and hydrophobic residues varying in availability in the primary sequences correspondingly affects the amount of these residues being used in the SSEs in accordance with linear behavioral patterns from empirical assessments of experimentally derived toxins and non-toxins. Subsequent derivation of prediction rules was established with the aim of analyzing molecular structures and mechanisms by means of 114 residue compositions for venom toxins. The obtained results concerning the linear behavioral patterns demonstrated the nature of the information transfer occurring from the primary to secondary structures. A dual action mechanism was established, taking into account steric and hydrophobic interactions. Finally, a new residue composition prediction method for SSEs of toxins was suggested.
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      PubDate: 2015-12-04T12:32:11Z
       
  • Deceptive responsive genes in gel-based proteomics
    • Abstract: Publication date: Available online 3 December 2015
      Source:Computational Biology and Chemistry
      Author(s): Sara Hamzelou, Hossein Askari, Nona Abolfathi Nobari
      The standard method of the global quantitative analysis of gene expression at the protein level combines high-resolution two-dimensional gel electrophoresis (2DE) with mass spectrometric identification of protein spots. One of the major concerns with the application of gel-based proteomics is the need for the analytical and biological accuracy of the datasets. We mathematically and empirically simulated the possibility of the technical regulations of gene expression using 2DE. Our developed equation predicted a detectable alteration in the quantity of protein spots in response to a new protein added in, with various amounts. Testing the predictability of the developed equation, we observed that a new protein could form deceptive expression profiles, classified using prevalent tools for the analysis of 2DE results. In spite of the theoretically predicted overall reduction of proteins that resulted from adding the new protein, the empirical data revealed differential amount of proteins when various quantities of the new protein were added to the protein sample. The present work emphasize that employment of 2DE would not be a reliable approach for biological samples with extensive proteome alterations such as the developmental and differentiation stages of cells without depletion of high abundant proteins.
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      PubDate: 2015-12-04T12:32:11Z
       
  • GPCR-drug Interactions Prediction Using Random Forest with
           Drug-Association-Matrix-Based Post-Processing Procedure
    • Abstract: Publication date: Available online 1 December 2015
      Source:Computational Biology and Chemistry
      Author(s): Jun Hu, Yang Li, Jing-Yu Yang, Hong-Bin Shen, Dong-Jun Yu
      G-protein-coupled receptors (GPCRs) are important targets of modern medicinal drugs. The accurate identification of interactions between GPCRs and drugs is of significant importance for both protein function annotations and drug discovery. In this paper, a new sequence-based predictor called TargetGDrug is designed and implemented for predicting GPCR-drug interactions. In TargetGDrug, the evolutionary feature of GPCR sequence and the wavelet-based molecular fingerprint feature of drug are integrated to form the combined feature of a GPCR-drug pair; then, the combined feature is fed to a trained random forest (RF) classifier to perform initial prediction; finally, a novel drug-association-matrix-based post-processing procedure is applied to reduce potential false positive or false negative of the initial prediction. Experimental results on benchmark datasets demonstrate the efficacy of the proposed method, and an improvement of 15% in the Matthews correlation coefficient (MCC) was observed over independent validation tests when compared with the most recently released sequence-based GPCR-drug interactions predictor. The implemented webserver, together with the datasets used in this study, is freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetGDrug.
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      PubDate: 2015-12-04T12:32:11Z
       
  • A new heuristic method for approximating the number of local minima in
           partial RNA energy landscapes
    • Abstract: Publication date: Available online 19 November 2015
      Source:Computational Biology and Chemistry
      Author(s): Andreas A Albrecht, Luke Day, Ouala Abdelhadi Ep Souki, Kathleen Steinhöfel
      The analysis of energy landscapes plays an important role in mathematical modelling, simulation and optimisation. Among the main features of interest are the number and distribution of local minima within the energy landscape. Granier and Kallel proposed in 2002 a new sampling procedure for estimating the number of local minima. In the present paper, we focus on improved heuristic implementations of the general framework devised by Granier and Kallel with regard to run-time behaviour and accuracy of predictions. The new heuristic method is demonstrated for the case of partial energy landscapes induced by RNA secondary structures. While the computation of minimum free energy RNA secondary structures has been studied for a long time, the analysis of folding landscapes has gained momentum over the past years in the context of co-transcriptional folding and deeper insights into cell processes. The new approach has been applied to ten RNA instances of length between 99nt and 504nt and their respective partial energy landscapes defined by secondary structures within an energy offset ΔE above the minimum free energy conformation. The number of local minima within the partial energy landscapes ranges from 1,440 to 3,441. Our heuristic method produces for the best approximations on average a deviation below 3.0% from the true number of local minima.
      Graphical abstract image Highlights

      PubDate: 2015-11-29T18:31:20Z
       
  • CDH1/E-CADHERIN AND SOLID TUMORS. AN UPDATED GENE-DISEASE ASSOCIATION
           ANALYSIS USING BIOINFORMATICS TOOLS
    • Abstract: Publication date: Available online 7 November 2015
      Source:Computational Biology and Chemistry
      Author(s): María Florencia Abascal, María José Besso, Marina Rosso, María Victoria Mencucci, Evangelina Aparicio, Gala Szapiro, Laura Inés Furlong, Mónica Hebe Vazquez-Levin
      Cancer is a group of diseases that causes millions of deaths worldwide. Among cancers, Solid Tumors (ST) stand-out due to their high incidence and mortality rates. Disruption of cell-cell adhesion is highly relevant during tumor progression. Epithelial-cadherin (protein: E-cadherin; gene: CDH1) is a key molecule in cell-cell adhesion and an abnormal expression or/and function(s) contributes to tumor progression and is altered in ST. A systematic study was carried out to gather and summarize current knowledge on CDH1/E-cadherin and ST using bioinformatics resources. The DisGeNET database was exploited to survey CDH1-associated diseases. Reported mutations in specific ST were obtained by interrogating COSMIC and IntOGen tools. CDH1 Single Nucleotide Polymorphisms (SNP) were retrieved from the dbSNP database. DisGeNET analysis identified 609 genes annotated to ST, among which CDH1 was listed. Using CDH1 as query term, 26 disease concepts were found, 21 of which were neoplasms-related terms. Using DisGeNET ALL, 172 disease concepts were identified. Of those, 80 ST disease-related terms were subjected to manual curation and 75/80 (93.75%) associations were validated. On selected ST, 489 CDH1 somatic mutations were listed in COSMIC and IntOGen databases. Breast neoplasms had the highest CDH1-mutation rate. CDH1 was positioned among the 20 genes with highest mutation frequency and was confirmed as driver gene in breast cancer. Over 14,000 SNP for CDH1 were found in the dbSNP database. This report used DisGeNET to gather/compile current knowledge on gene-disease association for CDH1/E-cadherin and ST; data curation expanded the number of terms that relate them. An updated list of CDH1 somatic mutations was obtained with COSMIC and IntOGen databases and of SNP from dbSNP. This information can be used to further understand the role of CDH1/E-cadherin in health and disease.
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      PubDate: 2015-11-29T18:31:20Z
       
  • In silico allergenicity prediction of several lipid transfer proteins
    • Abstract: Publication date: February 2016
      Source:Computational Biology and Chemistry, Volume 60
      Author(s): Cristiano Garino, Jean Daniel Coïsson, Marco Arlorio
      Non-specific lipid transfer proteins (nsLTPs) are common allergens and they are particularly widespread within the plant kingdom. They have a highly conserved three-dimensional structure that generate a strong cross-reactivity among the members of this family. In the last years several web tools for the prediction of allergenicity of new molecules based on their homology with known allergens have been released, and guidelines to assess potential allergenicity of proteins through bioinformatics have been established. Even if such tools are only partially reliable yet, they can provide important indications when other kinds of molecular characterization are lacking. The potential allergenicity of 28 amino acid sequences of LTPs homologs, either retrieved from the UniProt database or in silico deduced from the corresponding EST coding sequence, was predicted using 7 publicly available web tools. Moreover, their similarity degree to their closest known LTP allergens was calculated, in order to evaluate their potential cross-reactivity. Finally, all sequences were studied for their identity degree with the peach allergen Pru p 3, considering the regions involved in the formation of its known conformational IgE-binding epitope. Most of the analyzed sequences displayed a high probability to be allergenic according to all the software employed. The analyzed LTPs from bell pepper, cassava, mango, mungbean and soybean showed high homology (>70%) with some known allergenic LTPs, suggesting a potential risk of cross-reactivity for sensitized individuals. Other LTPs, like for example those from canola, cassava, mango, mungbean, papaya or persimmon, displayed a high degree of identity with Pru p 3 within the consensus sequence responsible for the formation, at three-dimensional level, of its major conformational epitope. Since recent studies highlighted how in patients mono-sensitized to peach LTP the levels of IgE seem directly proportional to the chance of developing cross-reactivity to LTPs from non-Rosaceae foods, and these chances increase the more similar the protein is to Pru p 3, these proteins should be taken into special account for future studies aimed at evaluating the risk of cross-allergenicity in highly sensitized individuals.
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      PubDate: 2015-11-29T18:31:20Z
       
  • Homology modelling and molecular docking studies of human placental
           cadherin protein for its role in teratogenic effects of anti-epileptic
           drugs
    • Abstract: Publication date: February 2016
      Source:Computational Biology and Chemistry, Volume 60
      Author(s): Sakshi Piplani, Vandana Saini, Ravi Ranjan K. Niraj, Adya Pushp, Ajit Kumar
      Anti-epileptic drugs (AEDs) have high risk of teratogenic side effects, including neural tube defects while mother is on AEDs for her own prevention of convulsions during pregnancy. The present study investigated the interaction of major marketed AEDs and human placental (hp)-cadherin protein, in-silico, to establish the role of hp-cadherin protein in teratogenicity and also to evaluate the importance of Ca2+ ion in functioning of the protein. A set of 21 major marketed AEDs were selected for the study and 3D-structure of hp-cadherin was constructed using homology modelling and energy minimized using MD simulations. Molecular docking studies were carried out using selected AEDs as ligand with hp-cadherin (free and bound Ca2+ ion) to study the behavioural changes in hp-cadherin due to presence of Ca2+ ion. The study reflected that four AEDs (Gabapentin, Pregabalin, Remacimide and Vigabatrine) had very high affinity towards hp-cadherin and thus the later may have prominent role in the teratogenic effects of these AEDs. From docking simulation analysis it was observed that Ca2+ ion is required to make hp-cadherin energetically favourable and sterically functional.
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      PubDate: 2015-11-29T18:31:20Z
       
  • Theoretical investigations on the interactions of glucokinase regulatory
           protein with fructose phosphates
    • Abstract: Publication date: February 2016
      Source:Computational Biology and Chemistry, Volume 60
      Author(s): Baoping Ling, Xueyuan Yan, Min Sun, Siwei Bi
      Glucokinase (GK) plays a critical role in maintaining glucose homeostasis in the human liver and pancreas. In the liver, the activity of GK is modulated by the glucokinase regulatory protein (GKRP) which functions as a competitive inhibitor of glucose to bind to GK. Moreover, the inhibitory intensity of GKRP–GK is suppressed by fructose 1-phosphate (F1P), and reinforced by fructose 6-phosphate (F6P). Here, we employed a series of computational techniques to explore the interactions of fructose phosphates with GKRP. Calculation results reveal that F1P and F6P can bind to the same active site of GKRP with different binding modes, and electrostatic interaction provides a major driving force for the ligand binding. The presence of fructose phosphate severely influences the motions of protein and the conformational space, and the structural change of sugar phosphate influences its interactions with GKRP, leading to a large conformational rearrangement of loop2 in the SIS2 domain. In particular, the binding of F6P to GKRP facilitates the protruding loop2 contacting with GK to form the stable GK–GKRP complex. The conserved residues 179–184 of GKRP play a major role in the binding of phosphate group and maintaining the stability of GKRP. These results may provide deep insight into the regulatory mechanism of GKRP to the activity of GK.
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      PubDate: 2015-11-29T18:31:20Z
       
  • Network regularised Cox regression and multiplex network models to predict
           disease comorbidities and survival of cancer
    • Abstract: Publication date: Available online 19 October 2015
      Source:Computational Biology and Chemistry
      Author(s): Haoming Xu, Mohammad Ali Moni, Pietro Liò
      In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease–gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git.


      PubDate: 2015-11-29T18:31:20Z
       
  • Computational based functional analysis of Bacillusphytases
    • Abstract: Publication date: Available online 10 November 2015
      Source:Computational Biology and Chemistry
      Author(s): Anukriti Verma, Vinay Kumar Singh, Smriti Gaur
      Phytase is an enzyme which catalyzes the total hydrolysis of phytate to less phosphorylated myo-inositol derivatives and inorganic phosphate and digests the undigestable phytate part present in seeds and grains and therefore provides digestible phosphorus, calcium and other mineral nutrients. Phytases are frequently added to the feed of monogastric animals so that bioavailability of phytic acid-bound phosphate increases, ultimately enhancing the nutritional value of diets. The Bacillusphytase is very suitable to be used in animal feed because of its optimum pH with excellent thermal stability. Present study is aimed to perform an in silicocomparative characterization and functional analysis of phytases from Bacillus amyloliquefaciens to explore physico-chemical properties using various bio-computational tools. All proteins are acidic and thermostable and can be used as suitable candidates in the feed industry.
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      PubDate: 2015-11-29T18:31:20Z
       
  • In silico drug re-purposing against African sleeping sickness using
           GlcNAc-PI de-N-acetylase as an experimental target
    • Abstract: Publication date: Available online 24 September 2015
      Source:Computational Biology and Chemistry
      Author(s): Mayank Rashmi, D Swati
      Trypanosoma brucei is a protozoan that causes African sleeping sickness in humans. Many glycoconjugate compounds are present on the entire cell surface of Trypanosoma brucei to control the infectivity and survival of this pathogen. These gycoconjugates are anchored to the plasma membrane with the help of glycosyl phosphatidyl inositol (GPI) anchors. This type of anchor is much more common in protozoans than in other eukaryotes. The second step of glycosyl phosphatidyl inositol (GPI) anchor biosynthesis is catalyzed by an enzyme, which is GlcNAc-PI de-N-acetylase. GlcNAc-PI de-N-acetylase has a conserved GPI domain, which is responsible for the functionality of this enzyme. In this study, the three-dimensional structure of the target is modelled by I-TASSER and the ligand is modelled by PRODRG server. It is found that the predicted active site residues of the GPI domain are ultra-conserved for the Trypanosomatidae family. The predicted active site residues are His41, Pro42, Asp43, Asp44, Met47, Phe48, Ser74, Arg80, His103, Val144, Ser145, His147 and His150. Two hydrogen bond acceptors and four hydrogen bond donors are found in the modelled pharmacophore. All compounds of the Drugbank database and twenty three known inhibitors have been considered for structure based virtual screening. This work is focused on approved drugs because they are already tested for safety and effectiveness in humans. After the structure-based virtual screening, seventeen approved drugs and two inhibitors are found, which interact with the ligand on the basis of the designed pharmacophore. The docking has been performed for the resultant seventeen approved drugs and two known inhibitors. Two approved drugs have negative binding energy and their pKa values are similar to the selected known inhibitors. The result of this study suggests that the approved drugs Ethambutol (DB00330) and Metaraminol (DB00610) may prove useful in the treatment of African sleeping sickness.
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      PubDate: 2015-09-26T20:26:46Z
       
  • Impact of heuristics in clustering large biological networks
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part A
      Author(s): Md. Kishwar Shafin, Kazi Lutful Kabir, Iffatur Ridwan, Tasmiah Tamzid Anannya, Rashid Saadman Karim, Mohammad Mozammel Hoque, M. Sohel Rahman
      Traditional clustering algorithms often exhibit poor performance for large networks. On the contrary, greedy algorithms are found to be relatively efficient while uncovering functional modules from large biological networks. The quality of the clusters produced by these greedy techniques largely depends on the underlying heuristics employed. Different heuristics based on different attributes and properties perform differently in terms of the quality of the clusters produced. This motivates us to design new heuristics for clustering large networks. In this paper, we have proposed two new heuristics and analyzed the performance thereof after incorporating those with three different combinations in a recently celebrated greedy clustering algorithm named SPICi. We have extensively analyzed the effectiveness of these new variants. The results are found to be promising.
      Graphical abstract image Highlights

      PubDate: 2015-09-22T20:25:02Z
       
  • Multiple ligand simultaneous docking (MLSD): A novel approach to study the
           effect of inhibitors on substrate binding to PPO
    • Abstract: Publication date: Available online 18 September 2015
      Source:Computational Biology and Chemistry
      Author(s): S. Raghavendra, S.J. Rao Aditya, Vadlapudi Kumar, C.K. Ramesh
      Multiple ligand simultaneous docking is a computational approach to study the concurrent interactions between the substrate and the macromolecule binding together in the presence of an inhibitor. The present investigation deals with the study of the effect of different inhibitors on binding of substrate to the protein Polyphenoloxidase (PPO). The protein was isolated from Mucuna pruriens and confirmed as tyrosinases involved in L-DOPA production. The activity was measured using different inhibitors at different concentrations taking catechol as substrate. A high-throughput binding study was conducted to compare the binding orientations of individual ligands and multiple ligands employing Autodock 4.2. The results of single substrate docking showed a better binding of urea with the binding energy of -3.48kJ mol−1 and inter molecular energy of -3.48kJ mol−1 while the results of MLSD revealed that ascorbic acid combined with the substrate showed better inhibition with a decreased binding energy of -2.37kJ mol−1.
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      PubDate: 2015-09-22T20:25:02Z
       
  • In silico approaches for the identification of virulence candidates
           amongst hypothetical proteins of Mycoplasma pneumoniae 309
    • Abstract: Publication date: Available online 18 September 2015
      Source:Computational Biology and Chemistry
      Author(s): Mohd. Shahbaaz, Krishna Bisetty, Faizan Ahmad, Md. Imtaiyaz Hassan
      Mycoplasma pneumoniae type 2a strain 309 is a simplest known bacterium and is the primary cause of community acquired pneumonia in the children. It mainly causes severe atypical pneumonia as well as several other non-pulmonary manifestations such as neurological, hepatic, hemolytic anemia, cardiac diseases and polyarthritis. The size of M. pneumoniae genome (Accession number-NC_016807.1) is relatively smaller as compared to other bacteria and contains 707 functional proteins, in which 204 are classified as hypothetical proteins (HPs) because of the unavailability of experimentally validated functions. The functions of the HPs were predicted by integrating a variety of protein classification systems, motif discovery tools as well as methods that are based on characteristic features obtained from the protein sequence and metabolic pathways. The probable functions of 83HPs were predicted successfully. The accuracy of the diverse tools used in the adopted pipeline was evaluated on the basis of statistical techniques of Receiver Operating Characteristic (ROC), which indicated the reliability of the functional predictions. Furthermore, the virulent HPs present in the set of 83 functionally annotated proteins were predicted by using the Bioinformatics tools and the conformational behaviours of the proteins with highest virulence scores were studied by using the molecular dynamics (MD) simulations. This study will facilitate in the better understanding of various drug resistance and pathogenesis mechanisms present in the M. pneumoniae and can be utilized in designing of better therapeutic agents.
      Graphical abstract image

      PubDate: 2015-09-22T20:25:02Z
       
  • Identification of chebulinic acid as potent natural inhibitor of M.
           tuberculosis DNA gyrase and molecular insights into its binding mode of
           action
    • Abstract: Publication date: Available online 11 September 2015
      Source:Computational Biology and Chemistry
      Author(s): Kunal Patel, Chetna Tyagi, Sukriti Goyal, Salma Jamal, Divya Wahi, Ritu Jain, Navneeta Bharadvaja, Abhinav Grover
      Drug resistant Tuberculosis has threatened all the advances that have been made in TB control at the global stage in the last few decades. DNA gyrase enzymes are an excellent target for antibacterial drug discovery as they are involved in essential functions like DNA replication. Here we report, a successful application of High Throughput Virtual Screening (HTVS) to identify an inhibitor of Mycobacterium DNA gyrase targeting the wild type and the most prevalent three double mutants of quinolone resistant DNA gyrase namely A90V+D94G, A74S+D94G and A90V+S91P. HTVS of 179.299 compounds gave five compounds with significant binding affinity. Extra presicion (XP) docking and MD simulations gave a clear view of their interaction pattern. Among them, Chebulinic Acid (CA), a phytocompound obtained from Terminalia chebula was the most potent inhibitor with significantly high XP docking score, -14.63, -16.46, -15.94 and -15.11 against wild type and three variants respectively. Simulation studies for a period of 16ns indicated stable DNA gyrA-CA complex formation. This stable binding would result in inhibition of the enzyme by two mechanisms. Firstly, binding of CA causes displacement of catalytic Tyr129 away from its target DNA-phosphate molecule from 1.6Å to 3.8Å - 7.3Å and secondly, by causing steric hindrance to the binding of DNA strand at DNA binding site of enzyme. The combined effect would result in loss of cleavage and religation activity of enzyme leading to bactericidal effect on Tuberculosis. This phytocompound displays desirable quality for carrying forward as a lead compound for anti-tuberculosis drug development.
      Graphical abstract image

      PubDate: 2015-09-14T20:09:09Z
       
  • Machine Learnable Fold Space Representation based on Residue Cluster
           Classes
    • Abstract: Publication date: December 2015
      Source:Computational Biology and Chemistry, Volume 59, Part A
      Author(s): Ricardo Corral-Corral, Edgar Chavez, Gabriel Del Rio
      Motivation Protein fold space is a conceptual framework where all possible protein folds exist and ideas about protein structure, function and evolution may be analyzed. Classification of protein folds in this space is commonly achieved by using similarity indexes and/or machine learning approaches, each with different limitations. Results We propose a method for constructing a compact vector space model of protein fold space by representing each protein structure by its residues local contacts. We developed an efficient method to statistically test for the separability of points in a space and showed that our protein fold space representation is learnable by any machine-learning algorithm. Availability An API is freely available at https://code.google.com/p/pyrcc/.
      Graphical abstract image Highlights

      PubDate: 2015-09-14T20:09:09Z
       
  • Genome-wide identification and expression analysis of WNK kinase gene
           family in rice
    • Abstract: Publication date: Available online 8 September 2015
      Source:Computational Biology and Chemistry
      Author(s): Rakesh Manuka, Ankush Ashok Saddhe, Kundan Kumar
      Eukaryotic protein kinases represent one of the largest gene families involved in diverse regulatory functions. WNK (With No Lysine) kinases are members of ser/thr protein kinase family, which lack conserved catalytic lysine (K) residue at protein kinase subdomain II and is replaced by either asparagine, serine or glycine residues. They are involved in regulation of flowering time, circadian rhythms and abiotic stresses in Arabidopsis thaliana. In the present study, we have identified 9 members of WNK in rice, showed resemblance to Arabidopsis and human WNK and clustered into five main clades phylogenetically. The predicted genes structure, bonafide conserved signature motif and domains strongly support their identity, as members of WNK kinase family. We have analyzed their chromosomal distribution, physio-chemical properties, subcellular localizations and cis-elements in the promoter regions in silico. Further, transcript analysis of OsWNK by qRT-PCR revealed their differential regulation in tissue specific and abiotic stresses libraries. In conclusion, the identification of nine OsWNK and transcript level expression pattern under abiotic stress using qRT-PCR in rice will significantly contribute towards the understanding of WNK genes in monocots and thus provide a set up for functional genomics studies of WNK protein kinases.
      Graphical abstract image

      PubDate: 2015-09-10T20:04:29Z
       
  • Structural and functional impact of missense mutations in TPMT: an
           integrated computational approach
    • Abstract: Publication date: Available online 9 September 2015
      Source:Computational Biology and Chemistry
      Author(s): Esmat Fazel-Najafabadi, Elham Vahdat Ahar, Shirin Fattahpour, Maryam Sedghi
      Background Thiopurine S-methyltransferase (TPMT) detoxifies thiopurine drugs which are used for treatment of various diseases including inflammatory bowel disease (IBD), and hematological malignancies. Individual variation in TPMT activity results from mutations in TPMT gene. In this study, the effects of all the known missense mutations in TPMT enzyme were studied at the sequence and structural level Methods A broad set of bioinformatic tools was used to assess all the known missense mutations affecting enzyme activity. The effects of these mutations on protein stability, aggregation propensity, and residue interaction network were analyzed. Results Our results indicate that the missense mutations have diverse effects on TPMT structure and function. Stability and aggregation propensities are affected by various mutations. Several mutations also affect residues in ligand binding site. Conclusions In vitro study of missense mutation is laborious and time-consuming. However, computational methods can be used to obtain information about effects of missense mutations on protein structure. In this study, the effects of most of the mutations on enzyme activity could be explained by computational methods. Thus, the present approach can be used for understanding the protein structure-function relationships.
      Graphical abstract image

      PubDate: 2015-09-10T20:04:29Z
       
  • Abundance of intrinsic structural disorder in the histone H1 subtypes
    • Abstract: Publication date: Available online 3 September 2015
      Source:Computational Biology and Chemistry
      Author(s): Andrzej Kowalski
      The intrinsically disordered proteins consist of partially structured regions linked to the unstructured stretches, which consequently form the transient and dynamic conformational ensembles. They undergo disorder to order transition upon binding their partners. Intrinsic disorder is attributed to histones H1, perceived as assemblers of chromatin structure and the regulators of DNA and proteins activity. In this work, the comparison of intrinsic disorder abundance in the histone H1 subtypes was performed both by the analysis of their amino acid composition and by the prediction of disordered stretches, as well as by identifying molecular recognition features (MoRFs) and ANCHOR protein binding regions (APBR) that are responsible for recognition and binding. Both human and model organisms - animals, plants, fungi and protists - have H1 histone subtypes with the properties typical of disordered state. They possess a significantly higher content of hydrophilic and charged amino acid residues, arranged in the long regions, covering over half of the whole amino acid residues in chain. Almost complete disorder corresponds to histone H1 terminal domains, including MoRFs and ANCHOR. Those motifs were also identified in a more ordered histone H1 globular domain. Compared to the control (globular and fibrous) proteins, H1 histones demonstrate the increased folding rate and a higher proportion of low-complexity segments. The results of this work indicate that intrinsic disorder is an inherent structural property of histone H1 subtypes and it is essential for establishing a protein conformation which defines functional outcomes affecting on DNA- and/or partner protein-dependent cell processes.
      Graphical abstract image

      PubDate: 2015-09-06T20:00:35Z
       
  • H7N9 Influenza Outbreak in China 2013: In silico Analyses of Conserved
           Segments of the Hemagglutinin as a basis for the Selection of Peptide
           Vaccine Targets
    • Abstract: Publication date: Available online 7 August 2015
      Source:Computational Biology and Chemistry
      Author(s): Tapati Sarkar, Sukhen Das, Antara De, Papiya Nandy, Shiladitya Chattopadhyay, Mamta Chawla-Sarkar, Ashesh Nandy
      The sudden emergence of a human infecting strain of H7N9 influenza virus in China in 2013 leading to fatalities in about 30% of the cases has caused wide concern that additional mutations in the strain leading to human to human transmission could lead to a deadly pandemic. It may happen in a short time span as the outbreak of H7N9 is more and more recurrent, which implies that H7N9 evolution is speeding up. H7N9 flu strains were not known to infect humans before this attack in China in February 2013 and it was solely an avian strain. While currently available drugs such as oseltamivir have been found to be largely effective against the H7N9, albeit with recent reported cases of development of resistance to the drug, there is a necessity to identify alternatives to combat this disease, especially if it assumes pandemic proportions. In our work, we have tried to investigate for the genetic changes in hemagglutinin (HA) protein sequence that lead to human infection by an avian infecting virus and identify possible peptide targets to design vaccines to control this upcoming risk. We identified three highly conserved regions in all H7 subtypes, of which one particular immunogenic surface exposed region was found to be well conserved in all human infecting H7N9 strains (accessed up to 27th March 2014). Compared to H7N9 avian strains, we identified two mutations in this conserved region at the receptor binding site of all post-February 2013 human-infecting H7N9China hemagglutinin protein sequences. One of the mutations is very close (3.6A°) to the hemagglutinin sialic acid binding pocket that may lead to better binding to human host's sialic acid due to the changes in hydrophobicity of the microenvironment of the binding site. We found that the peptide region with these mutational changes that are specific for human infecting H7N9 virus possess the possibility of being used as target for a peptide vaccine.
      Graphical abstract image

      PubDate: 2015-08-08T17:45:25Z
       
 
 
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