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  Subjects -> ENGINEERING (Total: 1957 journals)
    - CHEMICAL ENGINEERING (150 journals)
    - CIVIL ENGINEERING (146 journals)
    - ELECTRICAL ENGINEERING (84 journals)
    - ENGINEERING (1124 journals)
    - ENGINEERING MECHANICS AND MATERIALS (284 journals)
    - HYDRAULIC ENGINEERING (43 journals)
    - INDUSTRIAL ENGINEERING (53 journals)
    - MECHANICAL ENGINEERING (73 journals)

CHEMICAL ENGINEERING (150 journals)                  1 2     

ACS Combinatorial Science     Full-text available via subscription   (7 followers)
Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials     Hybrid Journal   (3 followers)
Acta Polymerica     Hybrid Journal   (4 followers)
Additives for Polymers     Full-text available via subscription   (17 followers)
Adhesion Adhesives & Sealants     Hybrid Journal   (3 followers)
Advanced Chemical Engineering Research     Open Access   (7 followers)
Advances in Applied Ceramics     Partially Free   (2 followers)
Advances in Chemical Engineering     Full-text available via subscription   (14 followers)
Advances in Chemical Engineering and Science     Open Access   (19 followers)
Advances in Polymer Technology     Hybrid Journal   (9 followers)
African Journal of Pure and Applied Chemistry     Open Access  
Annual Review of Analytical Chemistry     Full-text available via subscription   (8 followers)
Annual Review of Chemical and Biomolecular Engineering     Full-text available via subscription   (8 followers)
Anti-Corrosion Methods and Materials     Hybrid Journal   (3 followers)
Applied Petrochemical Research     Open Access   (3 followers)
Asia-Pacific Journal of Chemical Engineering     Hybrid Journal   (6 followers)
Biochemical Engineering Journal     Hybrid Journal   (8 followers)
Biomass Conversion and Biorefinery     Partially Free   (5 followers)
BMC Chemical Biology     Open Access   (4 followers)
Brazilian Journal of Chemical Engineering     Open Access   (2 followers)
Bulletin of the Chemical Society of Ethiopia     Open Access   (1 follower)
Carbohydrate Polymers     Hybrid Journal   (8 followers)
Catalysts     Open Access   (5 followers)
Chemical and Petroleum Engineering     Hybrid Journal   (7 followers)
Chemical and Process Engineering     Open Access   (3 followers)
Chemical and Process Engineering Research     Open Access   (5 followers)
Chemical Communications     Full-text available via subscription   (28 followers)
Chemical Engineering & Technology     Hybrid Journal   (24 followers)
Chemical Engineering and Processing: Process Intensification     Hybrid Journal   (9 followers)
Chemical Engineering and Science     Open Access   (2 followers)
Chemical Engineering Communications     Hybrid Journal   (10 followers)
Chemical Engineering Journal     Hybrid Journal   (16 followers)
Chemical Engineering Research and Design     Hybrid Journal   (15 followers)
Chemical Engineering Science     Hybrid Journal   (9 followers)
Chemical Geology     Hybrid Journal   (9 followers)
Chemical Papers     Hybrid Journal   (3 followers)
Chemical Product and Process Modeling     Full-text available via subscription   (3 followers)
Chemical Reviews     Full-text available via subscription   (187 followers)
Chemical Society Reviews     Full-text available via subscription   (26 followers)
Chemical Technology     Open Access   (4 followers)
ChemInform     Hybrid Journal   (3 followers)
Chemistry & Industry     Hybrid Journal   (2 followers)
Chemistry Central Journal     Open Access   (5 followers)
Chemistry of Materials     Full-text available via subscription   (136 followers)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (6 followers)
ChemSusChem     Hybrid Journal   (6 followers)
Chinese Chemical Letters     Full-text available via subscription   (1 follower)
Chinese Journal of Chemical Engineering     Full-text available via subscription   (3 followers)
Chinese Journal of Chemical Physics     Hybrid Journal   (1 follower)
Coke and Chemistry     Hybrid Journal  
Coloration Technology     Hybrid Journal   (1 follower)
Computational Biology and Chemistry     Hybrid Journal   (8 followers)
Computer Aided Chemical Engineering     Full-text available via subscription   (2 followers)
Computers & Chemical Engineering     Hybrid Journal   (6 followers)
Corrosion Engineering, Science and Technology     Hybrid Journal   (18 followers)
Corrosion Reviews     Full-text available via subscription   (4 followers)
Crystal Research and Technology     Hybrid Journal   (2 followers)
Current Opinion in Chemical Engineering     Open Access   (2 followers)
Education for Chemical Engineers     Hybrid Journal   (3 followers)
European Polymer Journal     Hybrid Journal   (41 followers)
Fibers and Polymers     Full-text available via subscription   (3 followers)
Focusing on Modern Food Industry     Open Access   (3 followers)
Frontiers of Chemical Science and Engineering     Hybrid Journal   (1 follower)
Geochemistry International     Hybrid Journal  
High Performance Polymers     Hybrid Journal  
Indian Chemical Engineer     Hybrid Journal   (3 followers)
Indian Journal of Chemical Technology (IJCT)     Open Access   (12 followers)
Industrial & Engineering Chemistry     Full-text available via subscription   (9 followers)
Industrial & Engineering Chemistry Research     Full-text available via subscription   (16 followers)
Industrial Chemistry Library     Full-text available via subscription   (4 followers)
International Journal of Chemical and Petroleum Sciences     Open Access   (1 follower)
International Journal of Chemical Engineering     Open Access   (6 followers)
International Journal of Chemical Reactor Engineering     Full-text available via subscription   (3 followers)
International Journal of Chemical Technology     Open Access   (3 followers)
International Journal of Chemoinformatics and Chemical Engineering     Full-text available via subscription   (2 followers)
International Journal of Food Science     Open Access   (2 followers)
International Journal of Industrial Chemistry     Open Access  
International Journal of Polymeric Materials     Hybrid Journal   (3 followers)
International Journal of Science and Engineering     Open Access   (7 followers)
International Journal of Waste Resources     Open Access   (3 followers)
ISRN Chemical Engineering     Open Access   (3 followers)
ISRN Polymer Science     Open Access   (10 followers)
Journal of Applied Crystallography     Hybrid Journal   (4 followers)
Journal of Applied Electrochemistry     Hybrid Journal   (6 followers)
Journal of Applied Polymer Science     Hybrid Journal   (122 followers)
Journal of Biomaterials Science, Polymer Edition     Hybrid Journal   (7 followers)
Journal of Chemical & Engineering Data     Full-text available via subscription   (6 followers)
Journal of Chemical Ecology     Hybrid Journal   (1 follower)
Journal of Chemical Engineering     Open Access   (3 followers)
Journal of Chemical Engineering and Materials Science     Open Access  
Journal of Chemical Science and Technology     Open Access   (1 follower)
Journal of Chemical Sciences     Partially Free   (13 followers)
Journal of Chemical Technology & Biotechnology     Hybrid Journal   (2 followers)
Journal of Chemical Theory and Computation     Full-text available via subscription   (8 followers)
Journal of Coatings     Open Access   (2 followers)
Journal of Crystallization Process and Technology     Open Access   (4 followers)
Journal of Food Measurement and Characterization     Hybrid Journal  
Journal of Fuel Chemistry and Technology     Full-text available via subscription   (5 followers)
Journal of Geochemical Exploration     Hybrid Journal  
Journal of Industrial and Engineering Chemistry     Hybrid Journal   (1 follower)

        1 2     

Computational Biology and Chemistry    [10 followers]  Follow    
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
     ISSN (Print) 1476-9271
     Published by Elsevier Homepage  [2556 journals]   [SJR: 0.558]   [H-I: 39]
  • All-atomic Molecular Dynamic Studies of Human CDK8: Insight into the
           A-loop, Point Mutations and Binding with Its Partner CycC
    • Abstract: Publication date: Available online 3 April 2014
      Source:Computational Biology and Chemistry
      Author(s): Wu Xu , Benjamin Amire-Brahimi , Xiao-Jun Xie , Liying Huang , Jun-Yuan Ji
      The Mediator, a conserved multisubunit protein complex in eukaryotic organisms, regulates gene expression by bridging sequence-specific DNA-binding transcription factors to the general RNA polymerase II machinery. In yeast, Mediator complex is organized in three core modules (head, middle and tail) and a separable ‘CDK8 submodule’ consisting of four subunits including Cyclin-dependent kinase CDK8 (CDK8), Cyclin C (CycC), MED12, and MED13 The 3-D structure of human CDK8-CycC complex has been recently experimentally determined. To take advantage of this structure and the improved theoretical calculation methods, we have performed molecular dynamic simulations to study dynamics of CDK8 and two CDK8 point mutations (D173A and D189N), which have been identified in human cancers, with and without full length of the A-loop as well as the binding between CDK8 and CycC. We found that CDK8 structure gradually loses two helical structures during the 50-ns molecular dynamic simulation, likely due to the presence of the full-length A-loop. In addition, our studies showed the hydrogen bond occupation of the CDK8 A-loop increases during the first 20-ns MD simulation and stays stable during the later 30-ns MD simulation. Four residues in the A-loop of CDK8 have high hydrogen bond occupation, while the rest residues have low or no hydrogen bond occupation. The hydrogen bond dynamic study of the A-loop residues exhibits three types of changes: increasing, decreasing, and stable. Furthermore, the 3-D structures of CDK8 point mutations D173A, D189N, T196A and T196D have been built by molecular modeling and further investigated by 50-ns molecular dynamic simulations. D173A has the highest average potential energy, while T196D has the lowest average potential energy, indicating that T196D is the most stable structure. Finally, we calculated theoretical binding energy of CDK8 and CycC by MM/PBSA and MM/GBSA methods, and the negative values obtained from both methods demonstrate stability of CDK8-CycC complex. Taken together, these analyses will improve our understanding of the exact functions of CDK8 and the interaction with its partner CycC.
      Graphical abstract image

      PubDate: 2014-04-04T04:24:48Z
       
  • IFC Editorial Board
    • Abstract: Publication date: April 2014
      Source:Computational Biology and Chemistry, Volume 49




      PubDate: 2014-03-31T02:29:03Z
       
  • Title page
    • Abstract: Publication date: April 2014
      Source:Computational Biology and Chemistry, Volume 49




      PubDate: 2014-03-31T02:29:03Z
       
  • Publisher Note
    • Abstract: Publication date: April 2014
      Source:Computational Biology and Chemistry, Volume 49




      PubDate: 2014-03-31T02:29:03Z
       
  • Genome-wide analysis and evolutionary study of sucrose non-fermenting
           1-related protein kinase 2 (SnRK2) gene family members in Arabidopsis and
           Oryza
    • Abstract: Publication date: April 2014
      Source:Computational Biology and Chemistry, Volume 49
      Author(s): Jayita Saha , Chitrita Chatterjee , Atreyee Sengupta , Kamala Gupta , Bhaskar Gupta
      The over-expression of plant specific SnRK2 gene family members by hyperosmotic stress and some by abscisic acid is well established. In this report, we have analyzed the evolution of SnRK2 gene family in different plant lineages including green algae, moss, lycophyte, dicot and monocot. Our results provide some evidences to indicate that the natural selection pressure had considerable influence on cis-regulatory promoter region and coding region of SnRK2 members in Arabidopsis and Oryza independently through time. Observed degree of sequence/motif conservation amongst SnRK2 homolog in all the analyzed plant lineages strongly supported their inclusion as members of this family. The chromosomal distributions of duplicated SnRK2 members have also been analyzed in Arabidopsis and Oryza. Massively Parallel Signature Sequencing (MPSS) database derived expression data and the presence of abiotic stress related promoter elements within the 1kb upstream promoter region of these SnRK2 family members further strengthen the observations of previous workers. Additionally, the phylogenetic relationships of SnRK2 have been studied in all plant lineages along with their respective exon–intron structural patterns. Our results indicate that the ancestral SnRK2 gene of land plants gradually evolved by duplication and diversification and modified itself through exon–intron loss events to survive under environmental stress conditions.
      Graphical abstract image

      PubDate: 2014-03-31T02:29:03Z
       
  • In-silico study of anti-carcinogenic Lysyl Oxidase-Like 2 inhibitors
    • Abstract: Publication date: Available online 20 March 2014
      Source:Computational Biology and Chemistry
      Author(s): Syed Aun Muhammad , Amjad Ali , Tariq Ismail , Rehan Zafar , Umair Ilyas , Jamil Ahmad
      Lysyl oxidase homolog 2 (LOXL2), also known as Lysyl oxidase-like protein 2 is recently been explored as regulator of carcinogenesis and has been shown to be involved in tumor progression and metastasis of several carcinomas. Therefore LOXL2 has been considered as potential therapeutic target. Doing so, its inhibitors as new chemotherapeutic lead molecules: 4-Amino-5-(2-Hydroxyphenyl)-1,2,4-Triazol-3-Thione (2a) and 4-(2-hydroxybenzalidine) amine-5-(2-hydroxy) phenyl-1,2,4-triazole-3-thiol (2b) are synthesized by fusion method (refluxed at 160°C). Spectral analysis of these triazole derivatives are characterized by FTIR and NMR. Active binding sites and quality of the LOXL2 model is assessed by Ramachandran plots and finally drug-target analysis is performed by computational virtual screening tools. Compounds 2a and 2b showed optimum target binding affinity with -6.2Kcal/mol and -8.9Kcal/mol binding energies. This in-silico study will add to our understanding of the drug designing and development, and to target cancer-causing proteins more precisely and quickly than before.


      PubDate: 2014-03-22T23:22:05Z
       
  • Determining common insertion sites based on retroviral insertion
           distribution across tumors
    • Abstract: Publication date: Available online 12 March 2014
      Source:Computational Biology and Chemistry
      Author(s): Feng Chen , Zhoufang Li , Yi-Ping Phoebe Chen
      A CIS (Common Insertion Site) indicates a genome region that is hit more frequently by retroviral insertions than expected by chance. Such a region is strongly related to cancer gene loci, which leads to the detection of cancer genes. An algorithm for detecting CISs should satisfy the following: 1) it does not require any prior knowledge of underlying insertion distribution; 2) it can resolve the insertion biases caused by hotspots; 3) it can detect CISs of any biological width; 4) it can identify noises resulting from statistic mistakes and non-CIS insertions; and 5) it can identify the widths of CISs as accurately as possible. We develop a method to resolve these difficulties. We verify a region's significance from two perspectives: distribution width and distribution depth. The former indicates how many insertions in a region while the latter evaluates the insertion distribution across the tumors in a region. We compare our method with kernel density estimation and sliding window on the simulated data, showing that our method not only identifies cancer-related insertions effectively, but also filters noises correctly. The experiments on the real data show that taking insertion distribution into account can highlight significant CISs. We detect 53 novel CISs, some of which have been proven correct by the biological literature.
      Graphical abstract image

      PubDate: 2014-03-14T22:32:16Z
       
  • An ensemble method for prediction of conformational B-cell epitopes from
           antigen sequences
    • Abstract: Publication date: Available online 18 February 2014
      Source:Computational Biology and Chemistry
      Author(s): Wei Zheng , Chen Zhang , Michelle Hanlon , Jishou Ruan , Jianzhao Gao
      Epitopes are immunogenic regions in antigen protein. Prediction of B-cell epitopes is critical for immunological applications. B-cell epitopes are categorized into linear and conformational. The majority of B-cell epitopes are conformational. Several machine learning methods have been proposed to identify conformational B-cell epitopes. However, the quality of these methods is not ideal. One question is whether or not the prediction of conformational B-cell epitopes can be improved by using ensemble methods. In this paper, we propose an ensemble method, which combined 12 support vector machine-based predictors, to predict the conformational B-cell epitopes, using an unbound dataset. AdaBoost and resampling methods are used to deal with an imbalanced labeled dataset. The proposed method achieves AUC of 0.642-0.672 on training dataset with 5-fold cross validation and AUC of 0.579-0.604 on test dataset. We also find some interesting results with the bound and unbound datasets. Epitopes are more accessible than non-epitopes, in bound and unbound datasets. Epitopes are also preferred in beta-turn, in bound and unbound datasets. The flexibility and polarity of epitopes are higher than non-epitopes. In a bound dataset, Asn (N), Glu (E), Gly (G), Lys (K), Ser (S), and Thr (T) are preferred in epitope regions, while Ala (A), Leu (L) and Val (V) are preferred in non-epitope regions. In the unbound dataset, Glu(E) and Lys(K) are preferred in epitope sites, while Leu(L) and Val(V) are preferred in non-epitiopes sites.


      PubDate: 2014-02-19T05:21:52Z
       
  • ISDTool: A computational model for predicting immunosuppressive domain of
           HERVs
    • Abstract: Publication date: Available online 12 February 2014
      Source:Computational Biology and Chemistry
      Author(s): Hongqiang Lv , Jiuqiang Han , Jun Liu , Jiguang Zheng , Dexing Zhong , Ruiling Liu
      Human endogenous retroviruses (HERVs) have been found to act as etiological cofactors in several chronic diseases, including cancer, autoimmunity and neurological dysfunction. Immunosuppressive domain (ISD) is a conserved region of transmembrane proteins (TM) in envelope gene (env) of retroviruses. In vitro and vivo, evidence has shown that retroviral TM is highly immunosuppressive and a synthetic peptide (CKS-17) that shows homology to ISD inhibits immune function. ISD is probably a potential pathogenic element in HERVs. However, only less than one hundred ISDs of HERVs have been annotated by researchers so far, and universal software for domain prediction could not achieve sufficient accuracy for specific ISD. In this paper, a computational model is proposed to identify ISD in HERVs based on genome sequences only. It has a classification accuracy of 97.9% using Jack-knife test. 117 HERVs families were scanned with the model, 1 002 new putative ISDs have been predicted and annotated in the human chromosomes. This model is also applicable to search for ISDs in human T-lymphotropic virus (HTLV), simian T-lymphotropic virus (STLV) and murine leukemia virus (MLV) because of the evolutionary relationship between endogenous and exogenous retroviruses. Furthermore, software named ISDTool has been developed to facilitate the application of the model. Datasets and the software involved in the paper are all available at https://sourceforge.net/projects/isdtool/files/ISDTool-1.0.
      Graphical abstract image

      PubDate: 2014-02-15T02:49:12Z
       
  • IFC Editorial Board
    • Abstract: Publication date: February 2014
      Source:Computational Biology and Chemistry, Volume 48




      PubDate: 2014-02-11T22:22:37Z
       
  • Title page
    • Abstract: Publication date: February 2014
      Source:Computational Biology and Chemistry, Volume 48




      PubDate: 2014-02-11T22:22:37Z
       
  • Editorial
    • Abstract: Publication date: February 2014
      Source:Computational Biology and Chemistry, Volume 48
      Author(s): Wentian Li



      PubDate: 2014-02-11T22:22:37Z
       
  • Using volcano plots and regularized-chi statistics in genetic association
           studies
    • Abstract: Publication date: February 2014
      Source:Computational Biology and Chemistry, Volume 48
      Author(s): Wentian Li , Jan Freudenberg , Young Ju Suh , Yaning Yang
      Labor intensive experiments are typically required to identify the causal disease variants from a list of disease associated variants in the genome. For designing such experiments, candidate variants are ranked by their strength of genetic association with the disease. However, the two commonly used measures of genetic association, the odds-ratio (OR) and p-value may rank variants in different order. To integrate these two measures into a single analysis, here we transfer the volcano plot methodology from gene expression analysis to genetic association studies. In its original setting, volcano plots are scatter plots of fold-change and t-test statistic (or −log of the p-value), with the latter being more sensitive to sample size. In genetic association studies, the OR and Pearson's chi-square statistic (or equivalently its square root, chi; or the standardized log(OR)) can be analogously used in a volcano plot, allowing for their visual inspection. Moreover, the geometric interpretation of these plots leads to an intuitive method for filtering results by a combination of both OR and chi-square statistic, which we term “regularized-chi”. This method selects associated markers by a smooth curve in the volcano plot instead of the right-angled lines which corresponds to independent cutoffs for OR and chi-square statistic. The regularized-chi incorporates relatively more signals from variants with lower minor-allele-frequencies than chi-square test statistic. As rare variants tend to have stronger functional effects, regularized-chi is better suited to the task of prioritization of candidate genes.
      Graphical abstract image Highlights

      PubDate: 2014-02-11T22:22:37Z
       
  • Mode of action classification of chemicals using multi-concentration
           time-dependent cellular response profiles
    • Abstract: Publication date: Available online 5 February 2014
      Source:Computational Biology and Chemistry
      Author(s): Zhankun Xi , Swanand Khare , Aaron Cheung , Biao Huang , Tianhong Pan , Weiping Zhang , Fadi Ibrahim , Can Jin , Stephan Gabos
      In this paper, we present a new statistical pattern recognition method for classifying cytotoxic cellular responses to toxic agents. The advantage of the proposed method is to quickly assess the toxicity level of an unclassified toxic agent on human health by bringing cytotoxic cellular responses with similar patterns (Mode Of Action, MoOA) into the same class. The proposed method is a model-based hierarchical classification approach incorporating Principal Component Analysis (PCA) & Functional Data Analysis (FDA). The cytotoxic cell responses are represented by multi-concentration Time-dependent Cellular Response Profiles (TCRPs) which are dynamically recorded by using the xCELLigence Real-Time Cell Analysis High-Throughput (RTCA HT) system. The classification results obtained using our algorithm show satisfactory discrimination and are validated using biological facts by examining common chemical mechanisms of actions with treatment on human hepatocellular carcinoma cells (HepG2).
      Graphical abstract image Highlights

      PubDate: 2014-02-07T20:13:17Z
       
  • Affinity of HIV-1 antibody 2G12 with monosaccharides: A theoretical study
           based on explicit and implicit water models
    • Abstract: Publication date: Available online 4 February 2014
      Source:Computational Biology and Chemistry
      Author(s): Yuka Koyama , Kaori Ueno-Noto , Keiko Takano
      In order to develop potential ligands to HIV-1 antibody 2G12 toward HIV-1 vaccine, binding mechanisms of the antibody 2G12 with the glycan ligand of D-mannose and D-fructose were theoretically examined. D-fructose, whose molecular structure is slightly different from D-mannose, has experimentally shown to have stronger binding affinity to the antibody than that of D-mannose. To clarify the nature of D-fructose's higher binding affinity over D-mannose, we studied interaction between the monosaccharides and the antibody using ab initio fragment molecular orbital (FMO) method considering solvation effect as implicit model (FMO-PCM) as well as explicit water model. The calculated binding free energies of the glycans were qualitatively well consistent with the experimentally reported order of their affinities with the antibody 2G12. In addition, the FMO-PCM calculation elucidated the advantages of D-fructose over D-mannose in the solvation energy as well as the entropic contribution term obtained by MD simulations. The effects of explicit water molecules observed in the x-ray crystal structure were also scrutinized by means of FMO methods. Significant pair interaction energies among D-fructose, amino acids, and water molecules were uncovered, which indicated contributions from the water molecules to the strong binding ability of D-fructose to the antibody 2G12. These FMO calculation results of explicit water model as well as implicit water model indicated that the strong binding of D-fructose over D-mannose was due to the solvation effects on the D-fructose interaction energy.
      Graphical abstract image

      PubDate: 2014-02-07T20:13:17Z
       
  • Fast detection of high-order epistatic interactions in genome-wide
           association studies using information theoretic measure
    • Abstract: Publication date: Available online 27 January 2014
      Source:Computational Biology and Chemistry
      Author(s): Sangseob Leem , Hyun-hwan Jeong , Jungseob Lee , Kyubum Wee , Kyung-Ah Sohn
      There are many algorithms for detecting epistatic interactions in GWAS. However, most of these algorithms are applicable only for detecting two-locus interactions. Some algorithms are designed to detect only two-locus interactions from the beginning. Others do not have limits to the order of interactions, but in practice take very long time to detect higher order interactions in real data of GWAS. Even the better ones take days to detect higher order interactions in WTCCC data. We propose a fast algorithm for detection of high order epistatic interactions in GWAS. It runs k-means clustering algorithm on the set of all SNPs. Then candidates are selected from each cluster. These candidates are examined to find the causative SNPs of k-locus interactions. We use mutual information from information theory as the measure of association between genotypes and phenotypes. We tested the power and speed of our method on extensive sets of simulated data. The results show that our method has more or equal power, and runs much faster than previously reported methods. We also applied our algorithm on each of seven diseases in WTCCC data to analyze up to 5-locus interactions. It takes only a few hours to analyze 5-locus interactions in one dataset. From the results we make some interesting and meaningful observations on each disease in WTCCC data. In this study, a simple yet powerful two-step approach is proposed for fast detection of high order epistatic interaction. Our algorithm makes it possible to detect high order epistatic interactions in GWAS in a matter of hours on a PC.


      PubDate: 2014-01-30T17:17:58Z
       
  • Improved homology model of cyclohexanone monooxygenase from Acinetobacter
           calcoaceticus based on multiple templates
    • Abstract: Publication date: Available online 28 January 2014
      Source:Computational Biology and Chemistry
      Author(s): Eduardo Bermúdez , Oscar N. Ventura , Leif A. Eriksson , Patricia Saenz-Méndez
      A new homology model of cyclohexanone monooxygenase (CHMO) from Acinetobacter calcoaceticus is derived based on multiple templates, and in particular the crystal structure of CHMO from Rhodococcus sp. The derived model was fully evaluated, showing that the quality of the new structure was improved over previous models. Critically, the nicotinamide cofactor is included in the model for the first time. Analysis of several molecular dynamics snapshots of intermediates in the enzymatic mechanism led to a description of key residues for cofactor binding and intermediate stabilization during the reaction, in particular Arg327 and the well known conserved motif (FxGxxxHxxxW) in Baeyer-Villiger monooxygenases, in excellent agreement with known experimental and computational data.
      Graphical abstract image

      PubDate: 2014-01-30T17:17:58Z
       
  • lncRNAMap: A map of putative regulatory functions in the long non-coding
           transcriptome
    • Abstract: Publication date: Available online 23 January 2014
      Source:Computational Biology and Chemistry
      Author(s): Wen-Ling Chan , Hsien-Da Huang , Jan-Gowth Chang
      Background Recent studies have demonstrated the importance of long non-coding RNAs (lncRNAs) in chromatin remodeling, and in transcriptional and post-transcriptional regulation. However, only a few specific lncRNAs are well understood, whereas others are completely uncharacterised. To address this, there is a need for user-friendly platform to studying the putative regulatory functions of human lncRNAs. Description lncRNAMap is an integrated and comprehensive database relating to exploration of the putative regulatory functions of human lncRNAs with two mechanisms of regulation, by encoding siRNAs and by acting as miRNA decoys. To investigate lncRNAs producing siRNAs that regulate protein-coding genes, lncRNAMap integrated small RNAs (sRNAs) that were supported by publicly available deep sequencing data from various sRNA libraries and constructed lncRNA-derived siRNA-target interactions. In addition, lncRNAMap demonstrated that lncRNAs can act as targets for miRNAs that would otherwise regulate protein-coding genes. Previously studies indicated that intergenic lncRNAs (lincRNAs) either positive or negative regulated neighboring genes, therefore, lncRNAMap surveyed neighboring genes within a 1 Mb distance from the genomic location of specific lncRNAs and provided the expression profiles of lncRNA and its neighboring genes. The gene expression profiles may supply the relationship between lncRNA and its neighboring genes. Conclusions lncRNAMap is a powerful user-friendly platform for the investigation of putative regulatory functions of human lncRNAs with producing siRNAs and acting as miRNA decoy. lncRNAMap is freely available on the web at ht*tp://lncRNAMap.mbc.nctu.edu.tw/.


      PubDate: 2014-01-26T22:15:30Z
       
  • Predicting Essential Genes for Identifying Potential Drug Targets In
           Aspergillus fumigatus
    • Abstract: Publication date: Available online 23 January 2014
      Source:Computational Biology and Chemistry
      Author(s): Yao Lu , Jingyuan Deng , Judith C. Rhodes , Hui Lu , Long Jason Lu
      Background Aspergillus fumigatus (Af) is a ubiquitous and opportunistic pathogen capable of causing acute, invasive pulmonary disease in susceptible hosts. Despite current therapeutic options, mortality associated with invasive Af infections remains unacceptably high, increasing 357% since 1980. Therefore, there is an urgent need for the development of novel therapeutic strategies, including more efficacious drugs acting on new targets. Thus, as noted in a recent review, “the identification of essential genes in fungi represents a crucial step in the development of new antifungal drugs”. Expanding the target space by rapidly identifying new essential genes has thus been described as “the most important task of genomics-based target validation”. Results In previous research, we were the first to show that essential gene annotation can be reliably transferred between distantly related four Prokaryotic species. In this study, we extend our machine learning approach to the much more complex Eukaryotic fungal species. A compendium of essential genes is predicted in Af by transferring known essential gene annotations from another filamentous fungus N. crassa. This approach predicts essential genes by integrating diverse types of intrinsic and context-dependent genomic features encoded in microbial genomes. The predicted essential datasets contained 1,674 genes. We validated our results by comparing our predictions with known essential genes in Af, comparing our predictions with those predicted by homology mapping, and conducting conditional expressed alleles. We applied several layers of filters and selected a set of potential drug targets from the predicted essential genes. Finally, we have conducted wet lab knockout experiments to verify our predictions, which further validates the accuracy and wide applicability of the machine learning approach. Conclusions The approach presented here significantly extended our ability to predict essential genes beyond orthologs and made it possible to predict an inventory of essential genes in Eukaryotic fungal species, amongst which a preferred subset of suitable drug targets may be selected. By selecting the best new targets, we believe that resultant drugs would exhibit an unparalleled clinical impact against a naive pathogen population. Additional benefits that a compendium of essential genes can provide are important information on cell function and evolutionary biology. Furthermore, mapping essential genes to pathways may also reveal critical check points in the pathogen's metabolism. Finally, this approach is highly reproducible and portable, and can be easily applied to predict essential genes in many more pathogenic microbes, especially those unculturable.


      PubDate: 2014-01-26T22:15:30Z
       
  • Pharmacoepidemiological characterization of drug-inducedadverse reaction
           clusters towards understanding of their mechanisms
    • Abstract: Publication date: Available online 24 January 2014
      Source:Computational Biology and Chemistry
      Author(s): Sayaka Mizutani , Yousuke Noro , Masaaki Kotera , Susumu Goto
      A big challenge in pharmacology is the understanding of the underlying mechanisms that cause drug-induced adverse reactions (ADRs), which are in some cases similar to each other regardless of different drug indications, and are in other cases different regardless of same drug indications. The FDA Adverse Event Reporting System (FAERS) provides a variable resource for pharmacoepidemiology, the study of the uses and the effects of drugs in large human population. However, FAERS is a spontaneous reporting system that inevitably contains noise that deviates the application of conventional clustering approaches. By performing a biclustering analysis on the FAERS data we identified 163 biclusters of drug-induced adverse reactions, counting for 691 ADRs and 240 drugs in total, where the number of ADR occurrences are consistently high across the associated drugs. Medically similar ADRs are derived from several distinct indications for use in the majority (145/163=88%) of the biclusters, which enabled us to interpret the underlying mechanisms that lead to similar ADRs. Furthermore, we compared the biclusters that contain same drugs but different ADRs, finding the cases where the populations of the patients were different in terms of age, sex, and body weight. We applied a biclustering approach to catalogue the relationship between drugs and adverse reactions from a large FAERS data set, and demonstrated a systematic way to uncover the cases different drug administrations resulted in similar adverse reactions, and the same drug can cause different reactions dependent on the patients’ conditions.


      PubDate: 2014-01-26T22:15:30Z
       
  • Practical halving; the Nelumbo nucifera evidence on early eudicot
           evolution
    • Abstract: Publication date: Available online 26 January 2014
      Source:Computational Biology and Chemistry
      Author(s): Chunfang Zheng , David Sankoff
      We present a stepwise optimal genome halving algorithm designed for large eukaryote genomes with largely single-copy genes, taking advantage of a signature pattern of paralog distribution in ancient polyploids. This is applied to the genome of Nelumbo nucifera, the sacred lotus, which is the descendant of a duplicated basal eudicot genome. In concert with the reconstructed ancestor of the grape, we investigate early events in eudicot evolution and show that the chromosome number of the common ancestor of lotus and grape was likely between 5 and 7. We show that the duplication of the ancestor of lotus and the triplication of the ancestor of grape were not closely preceded by any additional such event before the divergence of their two lineages.


      PubDate: 2014-01-26T22:15:30Z
       
  • Identification and characterization of lysine-methylated sites on histones
           and non-histone proteins
    • Abstract: Publication date: Available online 24 January 2014
      Source:Computational Biology and Chemistry
      Author(s): Tzong-Yi Lee , Cheng-Wei Chang , Cheng-Tzung Lu , Tzu-Hsiu Cheng , Tzu-Hao Chang
      Protein methylation is a kind of post-translational modification (PTM), and typically takes place on lysine and arginine amino acid residues. Protein methylation is involved in many important biological processes, and most recent studies focused on lysine methylation of histones due to its critical roles in regulating transcriptional repression and activation. Histones possess highly conserved sequences and are homologous in most species. However, there is much less sequence conservation among non-histone proteins. Therefore, mechanisms for identifying lysine-methylated sites may greatly differ between histones and non-histone proteins. Nevertheless, this point of view was not considered in previous studies. Here we constructed two support vector machine (SVM) models by using lysine-methylated data from histones and non-histone proteins for predictions of lysine-methylated sites. Numerous features, such as the amino acid composition (AAC) and accessible surface area (ASA), were used in the SVM models, and the predictive performance was evaluated using five-fold cross-validations. For histones, the predictive sensitivity was 85.62% and specificity was 80.32%. For non-histone proteins, the predictive sensitivity was 69.1% and specificity was 88.72%. Results showed that our model significantly improved the predictive accuracy of histones compared to previous approaches. In addition, features of the flanking region of lysine-methylated sites on histones and non-histone proteins were also characterized and are discussed. A gene ontology functional analysis of lysine-methylated proteins and correlations of lysine-methylated sites with other PTMs in histones were also analyzed in detail. Finally, a web server, MethyK, was constructed to identify lysine-methylated sites. MethK now is available at http://csb.cse.yzu.edu.tw/MethK/.


      PubDate: 2014-01-26T22:15:30Z
       
  • Parallel molecular computation of modular-multiplication withtwo same
           inputs over finite field GF(2n) usingself-assembly of DNA tiles
    • Abstract: Publication date: Available online 23 January 2014
      Source:Computational Biology and Chemistry
      Author(s): Yongnan Li , Limin Xiao , Li Ruan
      Two major advantages of DNA computing — huge memory capacity and high parallelism — are being explored for large-scale parallel computing, mass data storage and cryptography. Tile assembly model is a highly distributed parallel model of DNA computing. Finite field GF(2 n ) is one of the most commonly used mathematic sets for constructing public-key cryptosystem. It is still an open question that how to implement the basic operations over finite field GF(2 n ) using DNA tiles. This paper proposes how the parallel tile assembly process could be used for computing the modular-square, modular-multiplication with two same inputs, over finite field GF(2 n ). This system could obtain the final result within less steps than another molecular computing system designed in our previous study, because square and reduction are executed simultaneously and the previous system computes reduction after calculating square. Rigorous theoretical proofs are described and specific computing instance is given after defining the basic tiles and the assembly rules. Time complexity of this system is 3n −1 and space complexity is 2n 2.
      Graphical abstract image Highlights

      PubDate: 2014-01-23T18:42:06Z
       
  • Effect of sampling on the extent and accuracy of the inferred genetic
           history of recombining genome
    • Abstract: Publication date: Available online 23 January 2014
      Source:Computational Biology and Chemistry
      Author(s): Daniel E. Platt , Filippo Utro , Laxmi Parida
      Accessible biotechnology is enabling the cataloging of genetic variants in individuals in populations at unprecedented scales. The use of phylogeny of the individuals within populations allows a model-based approach to studying these variations, which is important in understanding relationships between and across populations. For the somatic genome, however, the phylogeny must take recombinations (and other genetic mixing events) into account. Hence the resulting topology is more complex than a tree. Unlike a tree topology, it is not as apparent which events are visible from the extant samples. An earlier work presented a mathematical model (called the minimal descriptor) for teasing apart the inherent visible information from that which any specific algorithm might see. We use this framework to study the effect of sampling sizes on the overall inferred genetic history. In this paper, we seek to understand the extent, characteristics (in terms of recent versus ancient genetic events) and reliability of what was resolvable within field samples drawn from modern populations. We observed that most of the visible ancient events are recoverable from relatively small sample sizes. However, without identification of this relatively small minority of ancient genetic events, most of the signal will appear to reflect modern events and admixtures. We also found that the more ancient events are likely to be reproduced with higher fidelity between multiple samplings, and that the identified older events are less likely to yield false positive discrimination between populations. We conclude that a recombinant phylogenetic reconstruction is necessary to identify which markers are most likely to discriminate ancient events, and to discriminate between populations with lower risk of false positives. Secondly, on a broader note, this study also provides a general methodology for a critical assessment of the inferred common genetic history of populations (say, in plant cultivars or animal populations).
      Graphical abstract image Highlights

      PubDate: 2014-01-23T18:42:06Z
       
  • piClust: A density based piRNA clustering algorithm
    • Abstract: Publication date: Available online 23 January 2014
      Source:Computational Biology and Chemistry
      Author(s): Inuk Jung , Jong Chan Park , Sun Kim
      Piwi-interacting RNAs (piRNAs) are recently discovered, endogenous small non-coding RNAs. piRNAs protect the genome from invasive transposable elements (TE) and sustain integrity of the genome in germ cell lineages. Due to lack of sequence conservation across species and poor sequence characteristics, such as length and 1T or 10A nucleotide bias, piRNAs are poorly characterized. Pioneering studies showed that piRNAs appear in clusters. Due to the lack of discriminating characteristics, piRNA cluster detection is the reliable method for detecting piRNA origins. Development of sophisticated computational methods for detecting piRNA clusters is needed. proTRAC, a state of the art method, detects piRNA clusters based on a probabilistic analysis with assumption of a uniform distribution. However, with careful investigation on data sets, we found that a uniform or any statistical distribution for detecting piRNA clusters may not be assumed. Furthermore, small RNA-seq data contains noisy data that was not carefully taken into account in previous studies. Our study was motivated due to unsuccessful cluster detection using proTRAC on our proprietary chicken germ cell line small RNA-seq data. To improve piRNA cluster identification, we used a density based clustering approach without assumption of any parametric distribution which is robust to noise in the data. In experiments with piRNA data from human, mouse, rat and chicken, piClust was able to detect piRNA clusters from total small RNA-seq data from germ cell lines, while proTRAC was not successful. piClust outperformed proTRAC in terms of sensitivity and running time (up to 200 folds). piClust is currently available as a web service at http://epigenomics.snu.ac.kr/piclustweb.
      Graphical abstract image Highlights

      PubDate: 2014-01-23T18:42:06Z
       
  • Gene expression regulation of the PF00480 or PF14340 domain proteins
           suggests their involvement in sulfur metabolism
    • Abstract: Publication date: Available online 22 January 2014
      Source:Computational Biology and Chemistry
      Author(s): Vassily A. Lyubetsky , Semen A. Korolev , Alexandr V. Seliverstov , Oleg A. Zverkov , Lev. I. Rubanov
      The paper studies proteins with domains PF00480 or PF14340, as well as some other poorly characterized proteins, encoded by genes associated with leader peptide genes containing a tract of cysteine codons. Such proteins are hypothetically regulated with cysteine-dependent transcription attenuation, namely the Rho-dependent or classic transcription attenuation. Cysteine is an important structural amino acid in various proteins and is required for synthesis of many sulfur-containing compounds, such as methionine, thiamine, glutathione, taurine, the lipoic acid, etc. Earlier a few species of mycobacteria were predicted by the authors to have cysteine-dependent regulation of operons containing the сysK gene. In E. coli this regulation is absent, and the same operon is regulated by the CysB transcription activator. The paper also studies Rho-dependent and classic transcription regulations in all annotated genes of mycobacteria available in GenBank and their orthologs in Actinomycetales. We predict regulations for many genes involved in sulfur metabolism and transport of sulfur-containing compounds; these regulations differ considerably among species. On the basis of predictions, we assign a putative role to proteins encoded by the regulated genes with unknown function, and also describe the structure of corresponding regulons, predict the lack of such regulations for many genes. Thus, all proteins with the uncharacterized Pfam domains PF14340 and PF00480, as well as some others, are predicted to be involved in sulfur metabolism. We also surmise the affinity of some transporters to sulfur-containing compounds. The obtained results considerably extend earlier large-scale studies of Rho-dependent and classic transcription attenuations.
      Graphical abstract image

      PubDate: 2014-01-23T18:42:06Z
       
  • Deciphering Histone Code of Transcriptional Regulation in Malaria
           Parasites by Large-scale Data Mining
    • Abstract: Publication date: Available online 23 January 2014
      Source:Computational Biology and Chemistry
      Author(s): Haifen Chen , Stefano Lonardi , Jie Zheng
      Histone modifications play a major role in the regulation of gene expression. Evidence has been accumulated to show that histone modifications mediate biological processes such as transcription cooperatively. This has led to the hypothesis of ‘histone code’ which suggests that combinations of different histone modifications correspond to unique chromatin states and have distinct functions. In this paper, we propose a framework based on association rule mining to discover the potential regulatory relations between histone modifications and gene expression in Plasmodium falciparum. Our approach can output rules with statistical significance. Some of the discovered rules are supported by literature of experimental results. Moreover, we have also discovered de novo rules which can guide further research in epigenetic regulation of transcription. Based on our association rules we build a model to predict gene expression, which outperforms a published Bayesian network model for gene regulation by histone modifications. The results of our study reveal mechanisms for histone modifications to regulate transcription in large-scale. Among our findings, the cooperation among histone modifications provides new evidence for the hypothesis of histone code. Furthermore, the rules output by our method can be used to predict the change of gene expression.


      PubDate: 2014-01-23T18:42:06Z
       
  • Collective variable driven molecular dynamics to improve protein-protein
           docking scoring
    • Abstract: Publication date: Available online 18 January 2014
      Source:Computational Biology and Chemistry
      Author(s): Diego Masone , Solène Grosdidier
      In biophysics, the structural prediction of protein–protein complexes starting from the unbound form of the two interacting monomers is a major difficulty. Although current computational docking protocols are able to generate near-native solutions in a reasonable time, the problem of identifying near-native conformations from a pool of solutions remains very challenging. In this study we use molecular dynamics simulations driven by a collective reaction coordinate to optimize full hydrogen bond networks in a set of protein-protein docking solutions. The collective coordinate biases the system to maximize the formation of hydrogen bonds at the protein-protein interface as well as all over the structure. The reaction coordinate is therefore a measure for docking poses affinity and hence is used as scoring function to identify near-native conformations.
      Graphical abstract image

      PubDate: 2014-01-20T17:24:07Z
       
  • Improving the prediction of chemotherapeutic sensitivity of tumors in
           breast cancer via optimizing the selection of candidate genes
    • Abstract: Publication date: Available online 1 January 2014
      Source:Computational Biology and Chemistry
      Author(s): Lina Jiang , Liqiu Huang , Qifan Kuang , Juan Zhang , Menglong Li , Zhining Wen , Li He
      Estrogen receptor status and the pathologic response to preoperative chemotherapy are two important indicators of chemotherapeutic sensitivity of tumors in breast cancer, which are used to guide the selection of specific regimens for patients. Microarray-based gene expression profiling, which is successfully applied to the discovery of tumor biomarkers and the prediction of drug response, was suggested to predict the cancer outcomes using the gene signatures differentially expressed between two clinical states. However, many false positive genes unrelated to the phenotypic differences will be involved in the lists of differentially expressed genes (DEGs) when only using the statistical methods for gene selection, e.g. Student's t test, and subsequently affect the performance of the predictive models. For the purpose of improving the prediction of clinical outcomes, we optimized the selection of DEGs by using a combined strategy, for which the DEGs were firstly identified by the statistical methods, and then filtered by a similarity profiling approach that used for candidate gene prioritization. In our study, we firstly verified the molecular functions of the DEGs identified by the combined strategy with the gene expression data generated in the microarray experiments of Si-Wu-Tang, which is a popular formula in traditional Chinese medicine. The results showed that, for Si-Wu-Tang experimental data set, the cancer-related signaling pathways were significantly enriched by gene set enrichment analysis when using the DEG lists generated by the combined strategy, confirming the potentially cancer-preventive effect of Si-Wu-Tang. To verify the performance of the predictive models in clinical application, we used the combined strategy to select the DEGs as features from the gene expression data of the clinical samples, which were collected from the breast cancer patients, and constructed models to predict the chemotherapeutic sensitivity of tumors in breast cancer. After refining the DEG lists by a similarity profiling approach, the Matthew's correlation coefficients of predicting estrogen receptor status and the pathologic response to preoperative chemotherapy with the DEGs selected by the fold change ranking were 0.770 and 0.428, respectively, and were 0.748 and 0.373 with the DEGs selected by SAM, respectively, which were generally higher than those achieved with unrefined DEG lists and those achieved by the candidate models in the second phase of Microarray Quality Control project (0.732 and 0.301, respectively). Our results demonstrated that the strategy of integrating the statistical methods with the gene prioritization methods based on similarity profiling was a powerful tool for DEG selection, which effectively improved the performance of prediction models in clinical applications and can guide the personalized chemotherapy better.
      Graphical abstract image

      PubDate: 2014-01-02T22:17:02Z
       
  • Protein Fold Recognition Based on Functional Domain Composition
    • Abstract: Publication date: Available online 25 December 2013
      Source:Computational Biology and Chemistry
      Author(s): Qin Wang , Yan Jin-Li , Li Xiao-Qin
      Recognition of protein fold types is an important step in protein structure and function predictions and is also an important method in protein sequence-structure research. Protein fold type reflects the topological pattern of the structure's core. Now there’re three methods of protein structure prediction, comparative modeling, fold recognition and de novo prediction. Since comparative modeling is limited by sequence similarity and there is too much workload in de novo prediction, fold recognition has the greatest potential. In order to improve recognition accuracy, a recognition method based on functional domain composition is proposed in this paper. This article focuses on the 124 fold types which have more than 2 samples in LIFCA database. We apply the functional domain composition to predict the fold types of a protein or a domain. In order to evaluate our method and its sensibility to the samples involving SCOP family divided, we tested our results from different aspects. The average sensitivity, specificity and Matthew's correlation coefficient (MCC) of the 124 fold types were found to be 94.58%, 99.96% and 0.91, respectively. Our results indicate that the functional domain composition method is a very promising method for protein fold recognition. And though based on simple classification rules, LIFCA database can grasp the functional features of different proteins, reflecting the corresponding relation between protein structure and function.


      PubDate: 2013-12-27T07:17:53Z
       
  • Identification and Characterization of Potential Drug Targets by
           Subtractive Genome Analyses of Methicillin Resistant Staphylococcus aureus
           
    • Abstract: Publication date: Available online 5 December 2013
      Source:Computational Biology and Chemistry
      Author(s): Reaz Uddin , Kiran Saeed
      Methicillin resistant Staphylococcus aureus (MRSA) causes serious infections in humans and becomes resistant to a number of antibiotics. Due to the emergence of antibiotic resistance strains, there is an essential need to develop novel drug targets to address the challenge of multidrug-resistant bacteria. In current study, the idea was to utilize the available genome or proteome in a subtractive genome analyses protocol to identify drug targets within two of the MRSA types i.e., MRSA ST398 and MRSA 252. Recently, the use of subtractive genomic approaches helped in the identification and characterization of novel drug targets of a number of pathogens. Our protocol involved a similarity search between pathogen and host, essentiality study using the database of essential genes, metabolic functional association study using Kyoto Encyclopedia of Genes and Genomes database (KEGG), cellular membrane localization analysis and drug bank database. Functional family characterizations of the identified non homologous hypothetical essential proteins were done by SVMProt server. Druggability potential of each of the identified drug targets was also evaluated by Drug Bank database. Moreover, metabolic pathway analysis of the identified druggable essential proteins with KEGG revealed that the identified proteins are participating in unique and essential metabolic pathways amongst MRSA strains. In short, the complete proteome analyses by the use of advanced computational tools, databases and servers resulted in identification and characterization of few nonhomologous/hypothetical and essential proteins which are not homologous to the host genome. Therefore, these non-homologous essential targets ensure the survival of the pathogen and hence can be targeted for drug discovery.
      Graphical abstract image

      PubDate: 2013-12-07T22:18:47Z
       
  • Subgrouping Automata: Automatic sequence subgrouping using phylogenetic
           tree-based optimum subgrouping algorithm
    • Abstract: Publication date: Available online 1 December 2013
      Source:Computational Biology and Chemistry
      Author(s): Joo-Hyun Seo , Jihyang Park , Eun-Mi Kim , Juhan Kim , Keehyoung Joo , Jooyoung Lee , Byung-Gee Kim
      Sequence subgrouping for a given sequence set can enable various informative tasks such as the functional discrimination of sequence subsets and the functional inference of unknown sequences. Because an identity threshold for sequence subgrouping may vary according to the given sequence set, it is highly desirable to construct a robust subgrouping algorithm which automatically identifies an optimal identity threshold and generates subgroups for a given sequence set. To meet this end, an automatic sequence subgrouping method, named ‘Subgrouping Automata’ was constructed. Firstly, tree analysis module analyzes the structure of tree and calculates the all possible subgroups in each node. Sequence similarity analysis module calculates average sequence similarity for all subgroups in each node. Representative sequence generation module finds a representative sequence using profile analysis and self-scoring for each subgroup. For all nodes, average sequence similarities are calculated and ‘Subgrouping Automata’ searches a node showing statistically maximum sequence similarity increase using student's t-value. A node showing the maximum t-value, which gives the most significant differences in average sequence similarity between two adjacent nodes, is determined as an optimum subgrouping node in the phylogenetic tree. Further analysis showed that the optimum subgrouping node from SA prevents under-subgrouping and over-subgrouping
      Graphical abstract image

      PubDate: 2013-12-01T22:15:20Z
       
  • The Intrinsic dynamics of Cse1p and Xpot elucidated by coarse-grained
           models
    • Abstract: Publication date: Available online 28 November 2013
      Source:Computational Biology and Chemistry
      Author(s): Ming-Wen Hu , Chiung-Fang Hsu , Byung Kim
      Cselp and Xopt are two karypherin proteins that transport the corresponding cargos during the nucleocytoplasmic transport. We utilized Elastic Network Model (ENM) and Finite Element Analysis (FEA) to study their conformational dynamics. These dynamics were interpreted by their intrinsic modes that played key roles in the flexibility of karyopherins, which further affected the binding affinities. The findings included that it was the karyopherin's versatile conformations composed of the same superhelices of HEAT repeats that produced different degrees of functional flexibilities. We presented evidence that these coarse-grained methods could help to elucidate the biological function behind the structures of the two karypherins.
      Graphical abstract image

      PubDate: 2013-11-28T17:05:27Z
       
  • Editorial
    • Abstract: Publication date: December 2013
      Source:Computational Biology and Chemistry, Volume 47
      Author(s): Wentian Li



      PubDate: 2013-11-22T11:57:34Z
       
  • Editorial Board
    • Abstract: Publication date: December 2013
      Source:Computational Biology and Chemistry, Volume 47




      PubDate: 2013-11-22T11:57:34Z
       
  • Title page
    • Abstract: Publication date: December 2013
      Source:Computational Biology and Chemistry, Volume 47




      PubDate: 2013-11-22T11:57:34Z
       
  • Multiscale modelling to understand the self-assembly mechanism of human
           β2-adrenergic receptor in lipid bilayer
    • Abstract: Publication date: Available online 16 November 2013
      Source:Computational Biology and Chemistry
      Author(s): Anirban Ghosh , Uddhavesh Sonavane , Rajendra Joshi
      The long perceived notion that G-Protein Coupled Receptors (GPCRs) function in monomeric form has recently been changed by the description of a number of GPCRs that are found in oligomeric states. The mechanism of GPCR oligomerization, and its effect on receptor function, is not well understood. In the present study, coarse grained molecular dynamics (CGMD) approach was adopted for studying the self-assembly process of the human GPCR, β2-adrenergic receptor (β2-AR), for which several experimental evidences of the dimerization process and its effect on cellular functions are available. Since the crystal structure of β2-AR lacks the third intracellular loop, initially it was modelled and simulated using restrained MD in order to get a stable starting conformation. This structure was then converted to CG representation and 16 copies of it, inserted into a hydrated lipid bilayer, were simulated for 10μs using the MARTINI force field. At the end of 10μs, oligomers of β2-AR were found to be formed through the self-assembly mechanism which were further validated through various analyses of the receptors. The lipid bilayer analysis also helped to quantify this assembly mechanism. In order to identify the domains which are responsible for this oligomerization, a reverse transformation of the CG system back to all-atom structure and simulated annealing run were carried out at the end of 10μs CGMD run. Analysis of the all-atom dimers thus obtained, revealed that TM1/TM1, H8/H8, TM1/TM5 and TM6/TM6 regions formed most of the dimerization surfaces, which is in accordance with some of the experimental observations and recent simulation results.
      Graphical abstract image

      PubDate: 2013-11-16T17:23:40Z
       
  • Computational simulation of ligand docking to L-type pyruvate kinase
           subunit
    • Abstract: Publication date: Available online 8 November 2013
      Source:Computational Biology and Chemistry
      Author(s): Aleksei Kuznetsov , Ilona Faustova , Jaak Järv
      Computational blind docking approach was used for mapping of possible binding sites in L-type pyruvate kinase subunit for peptides, RRASVA and the phosphorylated derivative RRAS(Pi)VA, which model the phosphorylatable N-terminal regulatory domain of the enzyme. In parallel, the same docking analysis was done for both substrates of this enzyme, phosphoenolpyruvate (PEP) and adenosine diphosphate (ADP), and for docking of fructose 1,6-bisphosphate (FBP), which is the allosteric activator of the enzyme. The binding properties of the entire surface of the protein were scanned and several possible binding sites were identified in domains A and C of the protein, while domain B revealed no docking sites for peptides or for substrates or the allosteric regulator. It was found that the docking sites of different ligands were partially overlapping, pointing to the possibility that some regulatory effects, observed in the case of L-type pyruvate kinase, may be caused by the competition of different ligands for the same binding sites.
      Graphical abstract image

      PubDate: 2013-11-10T22:15:15Z
       
  • The Optimization of Running Time for a Maximum Common Substructure-Based
           Algorithm and Its Application in Drug Design
    • Abstract: Publication date: Available online 7 November 2013
      Source:Computational Biology and Chemistry
      Author(s): Jian Chen , Jia Sheng , Dijing Lv , Yang Zhong , Guoqing Zhang , Peng Nan
      In the field of drug discovery, it is particularly important to discover bioactive compounds through high-throughput virtual screening. The Maximum Common Substructure-based (MCS) algorithm is a promising method for the virtual screening of drug candidates. However, in practical applications, there is always a trade-off between efficiency and accuracy. In this paper, we optimized this method by running time evaluation using essential drugs defined by WHO and FDA-approved small-molecule drugs. The amount of running time allocated to the MCS-based virtual screening was varied, and statistical analysis was conducted to study the impact of computation running time on the screening results. It was determined that the running time efficiency can be improved without compromising accuracy by setting proper running time thresholds. In addition, the similarity of compound structures and its relevance to biological activity are analyzed quantitatively, which highlight the applicability of the MCS-based methods in predicting functions of small molecules. 15s to 30s was established as a reasonable range for selecting a candidate running time threshold. The effect of CPU speed is considered and the conclusion is generalized. The potential biological activity of small molecules with unknown functions can be predicted by the MCS-based methods
      Graphical abstract image

      PubDate: 2013-11-07T22:17:22Z
       
  • Targeting the Akt1 allosteric site to identify novel scaffolds through
           virtual screening
    • Abstract: Publication date: Available online 29 October 2013
      Source:Computational Biology and Chemistry
      Author(s): Oya Gursoy Yilmaz , Elif Ozkirimli Olmez , Kutlu O. Ulgen
      Preclinical data and tumor specimen studies report that AKT kinases are related to many human cancers. Therefore, identification and development of small molecule inhibitors targeting AKT and its signaling pathway can be therapeutic in treatment of cancer. Numerous studies report inhibitors that target the the ATP-binding pocket in the kinase domains, but the similarity of this site, within the kinase family makes selectivity a major problem. The sequence identity amongst PH domains is significantly lower than that in kinase domains and developing more selective inhibitors is possible if PH domain is targeted. This in silico screening study is the first time report toward the identification of potential allosteric inhibitors expected to bind the cavity between kinase and PH domains of Akt1. Structural information of Akt1 was used to develop structure-based pharmacophore models comprising hydrophobic, acceptor, donor and ring features. The 3D structural information of previously identified allosteric Akt inhibitors obtained from literature was employed to develop a ligand-based pharmacophore model. Database was generated with drug like subset of ZINC and screening was performed based on 3D similarity to the selected pharmacophore hypotheses. Binding modes and affinities of the ligands were predicted by Glide software. Top scoring hits were further analyzed considering 2D similarity between the compounds, interactions with Akt1, fitness to pharmacophore models, ADME, druglikeness criteria and Induced-Fit docking. Using virtual screening methodologies, derivatives of 3-methyl-xanthine, quinoline-4-carboxamide and 2-[4-(cyclohexa-1,3-dien-1-yl)-1H-pyrazol-3-yl]phenol were proposed as potential leads for allosteric inhibiton of Akt1.
      Graphical abstract image

      PubDate: 2013-11-01T22:16:48Z
       
  • Modeling of Tumor Growth in Dendritic Cell-based Immunotherapy Using
           Artificial Neural Networks
    • Abstract: Publication date: Available online 29 October 2013
      Source:Computational Biology and Chemistry
      Author(s): Mohammad Mehrian , Davud Asemani , Abazar Arabameri , Arash Pourgholaminejad , Jamshid Hadjati
      Exposure-response Modeling and Simulation is especially useful in oncology as it permits to predict and design un-experimented clinical trials as well as dose selection. Dendritic Cells (DC) are the most effective immune cells in the regulation of immune system. To activate immune system, DCs may be matured by many factors like bacterial CpG-DNA, Lipopolysaccharaide (LPS) and other microbial products In this paper, a model based on Artificial Neural Network (ANN) is presented for analyzing the dynamics of antitumor vaccines using empirical data obtained from the experimentations of different groups of mice treated with DCs matured by bacterial CpG-DNA, LPS and whole lysate of a Gram-positive bacteria Listeria monocytogenes. Also, tumor lysate was added to DCs followed by addition of maturation factors. Simulations show that the proposed model can interpret the important features of empirical data. Owing to the nonlinearity properties, the proposed ANN model has been able not only to describe the contradictory empirical results, but also to predict new vaccination patterns for controlling the tumor growth. For example, the proposed model predicts an exponentially-increasing pattern of CpG-matured DC to be effective in suppressing the tumor growth
      Graphical abstract image

      PubDate: 2013-10-29T17:22:57Z
       
  • Modeling, docking and dynamics simulations of a non-specific lipid
           transfer protein from Peganum harmala L.
    • Abstract: Publication date: Available online 15 July 2013
      Source:Computational Biology and Chemistry
      Author(s): Zheng Shi , Zi-jie Wang , Huai-long Xu , Yang Tian , Xin Li , Jin-ku Bao , Su-rong Sun , Bi-song Yue
      Non-specific lipid transfer proteins (ns-LTPs), ubiquitously found in various types of plants, have been well-known to transfer amphiphilic lipids and promote the lipid exchange between mitochondria and microbody. In this study, an in silico analysis was proposed to study ns-LTP in Peganum harmala L., which may belong to ns-LTP1 family, aiming at constructing its three-dimensional structure. Moreover, we adopted MEGA to analyze ns-LTPs and other species phylogenetically, which brought out an initial sequence alignment of ns-LTPs. In addition, we used molecular docking and molecular dynamics simulations to further investigate the affinities and stabilities of ns-LTP with several ligands complexes. Taken together, our results about ns-LTPs and their ligand-binding activities can provide a better understanding of the lipid-protein interactions, indicating some future applications of ns-LTP-mediated transport.
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      PubDate: 2013-07-15T21:17:26Z
       
  • Computational Intelligence Techniques in Bioinformatics
    • Abstract: Publication date: Available online 10 July 2013
      Source:Computational Biology and Chemistry
      Author(s): Aboul Ella Hassanien , Eiman Tamah Al-Shammari , Neveen Ghali
      Computational intelligence (CI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. It is a methodology involving adaptive mechanisms and/or an ability to learn that facilitate intelligent behavior in complex and changing environments, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association and abstraction. The objective of this article is to present to the CI and bioinformatics research communities some of the state-of-the-art in CI applications to bioinformatics and motivate research in new trend-setting directions. In this article, we present an overview of the CI techniques in bioinformatics. We will show how CI techniques including neural networks, restricted Boltzmann machine, deep belief network, fuzzy logic, rough sets, evolutionary algorithms (EA), genetic algorithms (GA), swarm intelligence, artificial immune systems and support vector machines, could be successfully employed to tackle various problems such as gene expression clustering and classification, protein sequence classification, gene selection, DNA fragment assembly, multiple sequence alignment, and protein function prediction and its structure. We discuss some representative methods to provide inspiring examples to illustrate how CI can be utilized to address these problems and how bioinformatics data can be characterized by CI. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included.


      PubDate: 2013-07-12T16:50:03Z
       
  • Computer modeling of the dynamic properties of the cAMP-dependent protein
           kinase catalytic subunit
    • Abstract: Publication date: Available online 3 July 2013
      Source:Computational Biology and Chemistry
      Author(s): Andrei Izvolski , Jaak Järv , Aleksei Kuznetsov
      The structural dynamics of the cAMP-dependent protein kinase catalytic subunit were modeled using molecular dynamics computational methods. It was shown that the structure of this protein as well as its complexes with ATP and peptide ligand PKI(5-24) consisted of a large number of rapidly inter-converting conformations which could be grouped into subsets proceeding from their similarity. This cluster analysis revealed that conformations which correspond to the „opened” and „closed” structures of the protein were already present in the free enzyme, and most surprisingly co-existed in enzyme-ATP and enzyme-PKI(5-24) complexes as well as in the ternary complex, which included both of these ligands. The results also demonstrated that the most mobile structure segments of the protein were located in the regions of substrate binding sites and that their dynamics were most significantly affected by the binding of the ATP and peptide ligand.
      Graphical abstract image

      PubDate: 2013-07-06T20:49:01Z
       
  • Computational Structure Analysis of Biomacromolecule Complexes by
           Interface Geometry
    • Abstract: Publication date: Available online 25 June 2013
      Source:Computational Biology and Chemistry
      Author(s): Sedigheh Mahdavi , Ali Salehzadeh-Yazdi , Ali Mohades , Ali Masoudi-Nejad
      The ability to analyze and compare protein-nucleic acid and protein-protein interaction interface has critical importance in understanding the biological function and essential processes occurring in the cells. Since, high-resolution three-dimensional (3D) structures of biomacromolecule complexes are available, computational characterizing of the interface geometry become an important research topic in the field of molecular biology. In this study, the interfaces of a set of 180 protein-nucleic acid and protein-protein complexes are computed to understand the principles of their interactions. The weighted Voronoi diagram of the atoms and the Alpha complex has provided an accurate description of the interface atoms. Our method is implemented in the presence and absence of water molecules. A comparison among the three types of interaction interfaces show that RNA-protein complexes have the largest size of an interface. The results show a high correlation coefficient between our method and the PISA server in the presence and absence of water molecules in the Voronoi model and the traditional model, based on solvent accessibility and the high validation parameters in comparison to the classical model.
      Graphical abstract image

      PubDate: 2013-06-27T21:17:27Z
       
  • An efficient nonlinear finite-difference approach in the computational
           modeling of the dynamics of a nonlinear diffusion-reaction equation in
           microbial ecology
    • Abstract: Publication date: Available online 20 June 2013
      Source:Computational Biology and Chemistry
      Author(s): J.E. Macías-Díaz , Siegfried Macías , I.E. Medina-Ramírez
      In this manuscript, we present a computational model to approximate the solutions of a partial differential equation which describes the growth dynamics of microbial films. The numerical technique reported in this work is an explicit, nonlinear finite-difference methodology which is computationally implemented using Newton's method. Our scheme is compared numerically against an implicit, linear finite-difference discretization of the same partial differential equation, whose computer coding requires an implementation of the stabilized bi-conjugate gradient method. Our numerical results evince that the nonlinear approach results in a more efficient approximation to the solutions of the biofilm model considered, and demands less computer memory. Moreover, the positivity of initial profiles is preserved in the practice by the nonlinear scheme proposed.
      Highlights • Design of a nonlinear, explicit computational model to simulate the growth dynamics of a biofilm equation. • Faster computational performance of the proposed method with respect to linear, implicit discretizations available in the literature. • Efficient use of computational resources when compared against linear finite-difference schemes. • Numerical preservation of the property of positivity, which is physically meaningful in realistic applications.

      PubDate: 2013-06-21T21:17:10Z
       
  • PPM-Dom: A Novel Method for Domain Position Prediction
    • Abstract: Publication date: Available online 19 June 2013
      Source:Computational Biology and Chemistry
      Author(s): Jing Sun , Runyu Jing , Yuelong Wang , Tuanfei Zhu , Menglong Li , Yizhou Li
      Domains are the structural basis of the physiological functions of proteins, and the prediction of which is an advantageous process on the study of protein structure and function. This article proposes a new complete automatic prediction method, PPM-Dom (Domain Position Prediction Method), for predicting the particular positions of domains in a target protein via its atomic coordinate. The presented method integrates complex networks, community division, and fuzzy mean operator (FMO). The whole sequences are divided into potential domain regions by the complex network and community division, and FMO allows the final determination for the domain position. This method will suffice to predict regions that will form a domain structure and those that are unstructured based on completely new atomic coordinate information of the query sequence, and be able to separate different domains in the same query sequence from each other. On evaluating the performance using an independent testing dataset, PPM-Dom reached 91.41% for prediction accuracy, 96.12% for sensitivity and 92.86% for specificity. The tool bag of PPM-Dom is freely available at http://cic.scu.edu.cn/bioinformatics/PPMDom.zip.
      Graphical abstract image

      PubDate: 2013-06-21T21:17:10Z
       
  • H-bond refinement for electron transfer membrane-bound protein-protein
           complexes: cytochrome c oxidase and cytochrome c552
    • Abstract: Publication date: Available online 19 June 2013
      Source:Computational Biology and Chemistry
      Author(s): Diego Masone , Facundo Ciocco Aloia , Mario G. Del Pópolo
      In this study we propose a protocol to evaluate membrane-bound cytochrome c oxidase - cytochrome c552 docking candidates. An initial rigid docking algorithm generates docking poses of the cytochrome c oxidase–cytochrome c552, candidates are then aggregated into a 512-DPPC membrane model and solvated in explicit solvent. Molecular dynamic simulations are performed to induce conformational changes to membrane-bound protein complexes. Lastly each protein-protein complex is optimized in terms of its hydrogen bond network, evaluated energetically and ranked. The protocol is directly applicable to other membrane-protein complexes, such as protein-ligand systems.
      Graphical abstract image

      PubDate: 2013-06-21T21:17:10Z
       
  • Mutually exclusive binding of APPLPH to BAR domain and Reptin regulates
           β-catenin dependent transcriptional events
    • Abstract: Publication date: Available online 20 June 2013
      Source:Computational Biology and Chemistry
      Author(s): Sajid Rashid , Zahida Parveen , Saba Ferdous , Nousheen Bibi
      Reptin functions in a wide range of biological processes including chromatin remodelling, nucleolar organization and transcriptional regulation of WNT signaling. As β-catenin dependent transcriptional repression and activation events involve binding of Reptin and histone deacetylase 1 to APPL endocytic proteins, this complex has become an important target to identify molecules governing endocytic processes and WNT signaling. Here, we describe the structural basis of APPL binding to Reptin to explore their mode of binding in context with APPL1/APPL2 dimerization. There is an evidence that both PH and BAR domains of APPL proteins exhibit alternately conserved regions involved in hetero-dimerization process and our in-silico data also corroborate this fact. Moreover, APPL2PH domain binds to the BAR domain region encompassing a nuclear localization signal. We conclude that APPLPH binding to BAR domain and Reptin is mutually exclusive which regulates the nucleocytoplasmic shuttling of Reptin. Furthermore, Reptin is unable to bind with membrane-associated APPL proteins. These observations were further expanded by experimental approaches where we identified a novel point mutation D316N lying in the APPL1PH domain which resulted in a significantly reduced binding with Reptin. By luciferase assays, we observed that overexpression of APPL1D316N and APPL1WT stimulated β-catenin/TCF dependent transcriptional activity in a similar manner which suggested that binding of Reptin to APPL1 is not necessary for β-catenin dependent target gene expression. Overall, our data attempt to highlight a comparative role of APPL proteins in controlling β-catenin dependent transcription mechanism which may improve our understanding of gene regulation.
      Graphical abstract image

      PubDate: 2013-06-21T21:17:10Z
       
  • A model for the proteolytic regulation of LpxC in the lipopolysaccharide
           pathway of Escherichia coli
    • Abstract: Publication date: Available online 14 June 2013
      Source:Computational Biology and Chemistry
      Author(s): Akintunde Emiola , Paolo Falcarin , Joanne Tocher , John George
      Lipopolysaccharide (LPS) is an essential structural component found in Gram-negative bacteria. The molecule is comprised of a highly conserved lipid A and a variable outer core consisting of various sugars. LPS plays important roles in membrane stability in the bacterial cell and is also a potent activator of the human immune system. Despite its obvious importance, little is understood regarding the regulation of the individual enzymes involved or the pathway as a whole. LpxA and LpxC catalyze the first two steps in the LPS pathway. The reaction catalyzed by LpxA possesses a highly unfavourable equilibrium constant with no evidence of coupling to an energetically favourable reaction. In our model the presence of the second enzyme LpxC was sufficient to abate this unfavourable reaction and confirming previous studies suggesting that this reaction is the first committed step in LPS synthesis. It is believed that the protease FtsH regulates LpxC activity via cleavage. It is also suspected that the activity of FtsH is regulated by a metabolite produced by the LPS pathway; however, it is not known which one. In order to investigate these mechanisms, we obtained kinetic parameters from literature and developed estimates for other simulation parameters. Our simulations suggest that under modest increases in LpxC activity, FtsH is able to regulate the rate of product formation. However, under extreme increases in LpxC activities such as over-expression or asymmetrical cell division then FtsH activation may not be sufficient to regulate this first stage of synthesis.
      Graphical abstract image

      PubDate: 2013-06-15T21:16:28Z
       
 
 
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