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  Subjects -> ENGINEERING (Total: 2160 journals)
    - CHEMICAL ENGINEERING (186 journals)
    - CIVIL ENGINEERING (168 journals)
    - ELECTRICAL ENGINEERING (93 journals)
    - ENGINEERING (1165 journals)
    - ENGINEERING MECHANICS AND MATERIALS (355 journals)
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CHEMICAL ENGINEERING (186 journals)                  1 2     

AATCC Journal of Research     Full-text available via subscription   (Followers: 3)
ACS Combinatorial Science     Full-text available via subscription   (Followers: 8)
Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials     Hybrid Journal   (Followers: 4)
Acta Polymerica     Hybrid Journal   (Followers: 6)
Additives for Polymers     Full-text available via subscription   (Followers: 20)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 5)
Advanced Chemical Engineering Research     Open Access   (Followers: 9)
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: 17)
Advances in Chemical Engineering and Science     Open Access   (Followers: 23)
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: 5)
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: 1)
Biomass Conversion and Biorefinery     Partially Free   (Followers: 6)
BMC Chemical Biology     Open Access   (Followers: 4)
Brazilian Journal of Chemical Engineering     Open Access   (Followers: 3)
Bulletin of the Chemical Society of Ethiopia     Open Access   (Followers: 2)
Carbohydrate Polymers     Hybrid Journal   (Followers: 9)
Catalysts     Open Access   (Followers: 8)
Chemical and Engineering News     Free   (Followers: 6)
Chemical and Materials Engineering     Open Access   (Followers: 1)
Chemical and Petroleum Engineering     Hybrid Journal   (Followers: 9)
Chemical and Process Engineering     Open Access   (Followers: 3)
Chemical and Process Engineering Research     Open Access   (Followers: 5)
Chemical Communications     Full-text available via subscription   (Followers: 31)
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: 10)
Chemical Engineering Journal     Hybrid Journal   (Followers: 22)
Chemical Engineering Research and Design     Hybrid Journal   (Followers: 18)
Chemical Engineering Research Bulletin     Open Access   (Followers: 1)
Chemical Engineering Science     Hybrid Journal   (Followers: 16)
Chemical Geology     Hybrid Journal   (Followers: 10)
Chemical Papers     Hybrid Journal   (Followers: 3)
Chemical Product and Process Modeling     Hybrid Journal   (Followers: 3)
Chemical Reviews     Full-text available via subscription   (Followers: 160)
Chemical Society Reviews     Full-text available via subscription   (Followers: 33)
Chemical Technology     Open Access   (Followers: 5)
ChemInform     Hybrid Journal   (Followers: 3)
Chemistry & Industry     Hybrid Journal   (Followers: 2)
Chemistry Central Journal     Open Access   (Followers: 5)
Chemistry of Materials     Full-text available via subscription   (Followers: 141)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 6)
ChemSusChem     Hybrid Journal   (Followers: 8)
Chinese Chemical Letters     Full-text available via subscription   (Followers: 2)
Chinese Journal of Chemical Engineering     Full-text available via subscription   (Followers: 3)
Chinese Journal of Chemical Physics     Hybrid Journal   (Followers: 1)
Coke and Chemistry     Hybrid Journal  
Coloration Technology     Hybrid Journal   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 8)
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: 3)
Corrosion Engineering, Science and Technology     Hybrid Journal   (Followers: 22)
Corrosion Reviews     Hybrid Journal   (Followers: 4)
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)
Ekologia : The Journal of Institute of Landscape Ecology of Slovak Academy of Sciences     Open Access  
Eksergi     Open Access  
Emerging Trends in Chemical Engineering     Full-text available via subscription  
European Polymer Journal     Hybrid Journal   (Followers: 43)
Fibers and Polymers     Full-text available via subscription   (Followers: 3)
Fluorescent Materials     Open Access  
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  
Indian Chemical Engineer     Hybrid Journal   (Followers: 3)
Indian Journal of Chemical Technology (IJCT)     Open Access   (Followers: 11)
Industrial & Engineering Chemistry     Full-text available via subscription   (Followers: 9)
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: 1)
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: 3)
International Journal of Industrial Chemistry     Open Access  
International Journal of Polymeric Materials     Hybrid Journal   (Followers: 3)
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: 4)
Journal of Applied Electrochemistry     Hybrid Journal   (Followers: 12)
Journal of Applied Polymer Science     Hybrid Journal   (Followers: 125)
Journal of Biomaterials Science, Polymer Edition     Hybrid Journal   (Followers: 8)

        1 2     

Journal Cover   Computational Biology and Chemistry
  [SJR: 0.688]   [H-I: 43]   [8 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1476-9271
   Published by Elsevier Homepage  [2812 journals]
  • Analysis of image-based phenotypic parameters for high throughput gene
           perturbation assays
    • Abstract: Publication date: Available online 19 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Mee Song, Euna Jeong, Tae-Kyu Lee, Yury Tsoy, Yong-Jun Kwon, Sukjoon Yoon
      Although image-based phenotypic assays are considered a powerful tool for siRNA library screening, the reproducibility and biological implications of various image-based assays are not well-characterized in a systematic manner. Here, we compared the resolution of high throughput assays of image-based cell count and typical cell viability measures for cancer samples. It was found that the optimal plating density of cells was important to obtain maximal resolution in both types of assays. In general, cell counting provided better resolution than the cell viability measure in diverse batches of siRNAs. In addition to cell count, diverse image-based measures were simultaneously collected from a single screening and showed good reproducibility in repetitions. They were classified into a few functional categories according to biological process, based on the differential patterns of hit (i.e., siRNAs) prioritization from the same screening data. The presented systematic analyses of image-based parameters provide new insight to a multitude of applications and better biological interpretation of high content cell-based assays.
      Graphical abstract image

      PubDate: 2015-07-19T21:39:34Z
       
  • MOEPGA: A Novel Method to Detect Protein Complexes in Yeast
           Protein–Protein Interaction Networks based on MultiObjective
           Evolutionary Programming Genetic Algorithm
    • Abstract: Publication date: Available online 7 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Buwen Cao , Jiawei Luo , Cheng Liang , Shulin Wang
      The identification of protein complexes in Protein-Protein Interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.
      Graphical abstract image

      PubDate: 2015-07-11T15:49:22Z
       
  • Genetic Bee Colony (GBC) algorithm: A new gene selection method for
           microarray cancer classification
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Hala M. Alshamlan , Ghada H. Badr , Yousef A. Alohali
      Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.
      Graphical abstract image Highlights

      PubDate: 2015-07-03T15:42:05Z
       
  • Comparative analysis of plant lycopene cyclases
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Ibrahim Koc , Ertugrul Filiz , Huseyin Tombuloglu
      Carotenoids are essential isoprenoid pigments produced by plants, algae, fungi and bacteria. Lycopene cyclase (LYC) commonly cyclize carotenoids, which is an important branching step in the carotenogenesis, at one or both end of the backbone. Plants have two types of LYC (β-LCY and ϵ-LCY). In this study, plant LYCs were analyzed. Based on domain analysis, all LYCs accommodate lycopene cyclase domain (Pf05834). Furthermore, motif analysis indicated that motifs were conserved among the plants. On the basis of phylogenetic analysis, β-LCYs and ϵ-LCYs were classified in β and ϵ groups. Monocot and dicot plants separated from each other in the phylogenetic tree. Subsequently, Oryza sativa Japonica Group and Zea mays of LYCs as monocot plants and Vitis vinifera and Solanum lycopersicum of LYCs as dicot plants were analyzed. According to nucleotide diversity analysis of β-LCY and ϵ-LCY genes, nucleotide diversities were found to be π: 0.30 and π: 0.25, respectively. The result highlighted β-LCY genes showed higher nucleotide diversity than ϵ-LCY genes. LYCs interacting genes and their co-expression partners were also predicted using String server. The obtained data suggested the importance of LYCs in carotenoid metabolism. 3D modeling revealed that depicted structures were similar in O. sativa, Z mays, S. lycopersicum, and V. vinifera β-LCYs and ϵ-LCYs. Likewise, the predicted binding sites were highly similar between O. sativa, Z mays, S. lycopersicum, and V. vinifera LCYs. Most importantly, analysis elucidated the V/IXGXGXXGXXXA motif for both type of LYC (β-LCY and ϵ-LCY). This motif related to Rossmann fold domain and probably provides a flat platform for binding of FAD in O. sativa, Z mays, S. lycopersicum, and V. vinifera β-LCYs and ϵ-LCYs with conserved structure. In addition to lycopene cyclase domain, the V/IXGXGXXGXXXA motif can be used for exploring LYCs proteins and to annotate the function of unknown proteins containing lycopene cyclase domain. Overall results indicated that a high degree of conserved signature were observed in plant LYCs.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Exploring the relationship between hub proteins and drug targets based on
           GO and intrinsic disorder
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Yuanyuan Fu , Yanzhi Guo , Yuelong Wang , Jiesi Luo , Xuemei Pu , Menglong Li , Zhihang Zhang
      Protein–protein interactions (PPIs) play essential roles in many biological processes. In protein–protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Gene expression patterns combined with network analysis identify hub genes
           associated with bladder cancer
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Dongbin Bi , Hao Ning , Shuai Liu , Xinxiang Que , Kejia Ding
      Objectives To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. Methods The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein–protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Results Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Conclusions Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Abstraction for data integration: Fusing mammalian molecular, cellular and
           phenotype big datasets for better knowledge extraction
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Andrew D. Rouillard , Zichen Wang , Avi Ma’ayan
      With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction. To fuse and reuse biomedical datasets from diverse resources, data abstraction is frequently required. Here we summarize some of the major Big Data biomedical research resources for genomics, proteomics and phenotype data, collected from mammalian cells, tissues and organisms. We then suggest simple data abstraction methods for fusing this diverse but related data. Finally, we demonstrate examples of the potential utility of such data integration efforts, while warning about the inherit biases that exist within such data.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Evaluation of the effect of the chiral centers of Taxol on binding to
           β-tubulin: A docking and molecular dynamics simulation study
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Rahim Ghadari , Fatemeh S. Alavi , Mansour Zahedi
      Taxol is one of the most important anti-cancer drugs. The interaction between different variants of Taxol, by altering one of its chiral centers at a time, with β-tubulin protein has been investigated. To achieve such goal, docking and molecular dynamics (MD) simulation studies have been performed. In docking studies, the preferred conformers have been selected to further study by MD method based on the binding energies reported by the AutoDock program. The best result of docking study which shows the highest affinity between ligand and protein has been used as the starting point of the MD simulations. All of the complexes have shown acceptable stability during the simulation process, based on the RMSDs of the backbone of the protein structure. Finally, MM-GBSA calculations have been carried out to select the best ligand, considering the binding energy criteria. The results predict that two of the structures have better affinity toward the mentioned protein, in comparison with Taxol. Three of the structures have affinity similar to that of the Taxol toward the β-tubulin.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Identification of evolutionarily conserved Momordica charantia microRNAs
           using computational approach and its utility in phylogeny analysis
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Krishnaraj Thirugnanasambantham , Subramanian Saravanan , Kulandaivelu Karikalan , Rajaraman Bharanidharan , Perumal Lalitha , S. Ilango , Villianur Ibrahim HairulIslam
      Momordica charantia (bitter gourd, bitter melon) is a monoecious Cucurbitaceae with anti-oxidant, anti-microbial, anti-viral and anti-diabetic potential. Molecular studies on this economically valuable plant are very essential to understand its phylogeny and evolution. MicroRNAs (miRNAs) are conserved, small, non-coding RNA with ability to regulate gene expression by bind the 3′ UTR region of target mRNA and are evolved at different rates in different plant species. In this study we have utilized homology based computational approach and identified 27 mature miRNAs for the first time from this bio-medically important plant. The phylogenetic tree developed from binary data derived from the data on presence/absence of the identified miRNAs were noticed to be uncertain and biased. Most of the identified miRNAs were highly conserved among the plant species and sequence based phylogeny analysis of miRNAs resolved the above difficulties in phylogeny approach using miRNA. Predicted gene targets of the identified miRNAs revealed their importance in regulation of plant developmental process. Reported miRNAs held sequence conservation in mature miRNAs and the detailed phylogeny analysis of pre-miRNA sequences revealed genus specific segregation of clusters.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Construction of regulatory networks mediated by small RNAs responsive to
           abiotic stresses in rice (Oryza sativa)
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Jingping Qin , Xiaoxia Ma , Zhonghai Tang , Yijun Meng
      Plants have evolved exquisite molecular mechanisms to adapt to diverse abiotic stresses. MicroRNAs play an important role in stress response in plants. However, whether the other small RNAs (sRNAs) possess stress-related roles remains elusive. In this study, thousands of sRNAs responsive to cold, drought and salt stresses were identified in rice seedlings and panicles by using high-throughput sequencing data. These sRNAs were classified into 12 categories, including “Panicle_Cold_Down”, “Panicle_Cold_Up”, “Panicle_Drought_Down”, “Panicle_Drought_Up”, “Panicle_Salt_Down”, “Panicle_Salt_Up”, “Seedling_Cold_Down”, “Seedling_Cold_Up”, “Seedling_Drought_Down”, “Seedling_Drought_Up”, “Seedling_Salt_Down” and “Seedling_Salt_Up”. The stress-responsive sRNAs enriched in Argonaute 1 were extracted for target prediction and degradome sequencing data-based validation, which enabled network construction. Within certain subnetworks, some target genes were further supported by microarray data. Literature mining indicated that certain targets were potentially involved in stress response. These results demonstrate that the established networks are biologically meaningful. We discovered that in some cases, one sRNA sequence could be assigned to two or more categories. Moreover, within certain target-centered subnetworks, one transcript was regulated by several stress-responsive sRNAs assigned to different categories. It implies that these subnetworks are potentially implicated in stress signal crosstalk. Together, our results could advance the current understanding of the biological role of plant sRNAs in stress signaling.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Title page
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57




      PubDate: 2015-07-03T15:42:05Z
       
  • No title
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Hsien-Da Huang , Yi-Ping Phoebe Chen



      PubDate: 2015-07-03T15:42:05Z
       
  • Characterizing the protonation states of the catalytic residues in apo and
           substrate-bound human T-cell leukemia virus type 1 protease
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Shuhua Ma , Kimberly A. Vogt , Natalie Petrillo , Alyce J. Ruhoff
      Human T-cell leukemia virus type 1 (HTLV-1) protease is an attractive target when developing inhibitors to treat HTLV-1 associated diseases. To study the catalytic mechanism and design novel HTLV-1 protease inhibitors, the protonation states of the two catalytic aspartic acid residues must be determined. Free energy simulations have been conducted to study the proton transfer reaction between the catalytic residues of HTLV-1 protease using a combined quantum mechanical and molecular mechanical (QM/MM) molecular dynamics simulation. The free energy profiles for the reaction in the apo-enzyme and in an enzyme – substrate complex have been obtained. In the apo-enzyme, the two catalytic residues are chemically equivalent and are expected to be both unprotonated. Upon substrate binding, the catalytic residues of HTLV-1 protease evolve to a singly protonated state, in which the OD1 of Asp32 is protonated and forms a hydrogen bond with the OD1 of Asp32′, which is unprotonated. The HTLV-1 protease–substrate complex structure obtained from this simulation can serve as the Michaelis complex structure for further mechanistic studies of HTLV-1 protease while providing a receptor structure with the correct protonation states for the active site residues toward the design of novel HTLV-1 protease inhibitors through virtual screening.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Pru du 2S albumin or Pru du vicilin'
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Cristiano Garino , Angelo De Paolis , Jean Daniel Coïsson , Marco Arlorio
      A short partial sequence of 28 amino acids is all the information we have so far about the putative allergen 2S albumin from almond. The aim of this work was to analyze this information using mainly bioinformatics tools, in order to verify its rightness. Based on the results reported in the paper describing this allergen from almond, we analyzed the original data of amino acids sequencing through available software. The degree of homology of the almond 12kDa protein with any other known 2S albumin appears to be much lower than the one reported in the paper that firstly described it. In a publicly available cDNA library we discovered an expressed sequence tag which translation generates a protein that perfectly matches both of the sequencing outputs described in the same paper. A further analysis indicated that the latter protein seems to belong to the vicilin superfamily rather than to the prolamin one. The fact that also vicilins are seed storage proteins known to be highly allergenic would explain the IgE reactivity originally observed. Based on our observations we suggest that the IgE reactive 12kDa protein from almond currently known as Pru du 2S albumin is in reality the cleaved N-terminal region of a 7S vicilin like protein.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • On the impact of discreteness and abstractions on modelling noise in gene
           regulatory networks
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Chiara Bodei , Luca Bortolussi , Davide Chiarugi , Maria Luisa Guerriero , Alberto Policriti , Alessandro Romanel
      In this paper, we explore the impact of different forms of model abstraction and the role of discreteness on the dynamical behaviour of a simple model of gene regulation where a transcriptional repressor negatively regulates its own expression. We first investigate the relation between a minimal set of parameters and the system dynamics in a purely discrete stochastic framework, with the twofold purpose of providing an intuitive explanation of the different behavioural patterns exhibited and of identifying the main sources of noise. Then, we explore the effect of combining hybrid approaches and quasi-steady state approximations on model behaviour (and simulation time), to understand to what extent dynamics and quantitative features such as noise intensity can be preserved.
      Graphical abstract image Highlights

      PubDate: 2015-07-03T15:42:05Z
       
  • Molecular characterization, modeling and docking of CYP107CB2 from
           Bacillus lehensis G1, an alkaliphile
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Swi See Ang , Abu Bakar Salleh , Adam Leow Thean Chor , Yahaya M. Normi , Bimo Ario Tejo , Mohd Basyaruddin Abdul Rahman
      Cytochrome P450s are a superfamily of heme monooxygenases which catalyze a wide range of biochemical reactions. The reactions involve the introduction of an oxygen atom into an inactivated carbon of a compound which is essential to produce an intermediate of a hydroxylated product. The diversity of chemical reactions catalyzed by cytochrome P450s has led to their increased demand in numerous industrial and biotechnology applications. A recent study showed that a gene sequence encoding a CYP was found in the genome of Bacillus lehensis G1, and this gene shared structural similarity with the bacterial vitamin D hydroxylase (Vdh) from Pseudonocardia autotrophica. The objectives of present study was to mine, for a novel CYP from a new isolate B. lehensis G1 alkaliphile and determine the biological properties and functionalities of CYP in this bacterium. Our study employed the usage of computational methods to search for the novel CYP from CYP structural databases to identify the conserved pattern, functional domain and sequence properties of the uncharacterized CYP from B. lehensis G1. A computational homology model of the protein’s structure was generated and a docking analysis was performed to provide useful structural knowledge on the enzyme’s possible substrate and their interaction. Sequence analysis indicated that the newly identified CYP, termed CYP107CB2, contained the fingerprint heme binding sequence motif FxxGxxxCxG at position 336-345 as well as other highly conserved motifs characteristic of cytochrome P450 proteins. Using docking studies, we identified Ser-79, Leu-81, Val-231, Val-279, Val-383, Ala-232, Thr-236 and Thr-283 as important active site residues capable of stabilizing interactions with several potential substrates, including vitamin D3, 25-hydroxyvitamin D3 and 1α-hydroxyvitamin D3, in which all substrates docked proximally to the enzyme’s heme center. Biochemical analysis indicated that CYP107CB2 is a biologically active protein to produce 1α,25-dihydroxyvitamin D3 from 1α-hydroxyvitamin D3. Based on these results, we conclude that the novel CYP107CB2 identified from B. lehensis G1 is a putative vitamin D hydroxylase which is possibly capable of catalyzing the bioconversion of parental vitamin D3 to calcitriol, or related metabolic products.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Study of early stages of amyloid Aβ13-23 formation using molecular
           dynamics simulation in implicit environments
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Marek Bajda , Slawomir Filipek
      β-amyloid aggregation and formation of senile plaques is one of the hallmarks of Alzheimer’s disease (AD). It leads to degeneration of neurons and decline of cognitive functions. The most aggregative and toxic form of β-amyloid is Aβ1-42 but in experiments, the shorter forms able to form aggregates are also used. The early stages of amyloid formation are of special interest due to the influence of this peptide on progression of AD. Here, we employed nine helices of undecapeptide Aβ13-23 and studied progress of amyloid formation using 500ns molecular dynamics simulation and implicit membrane environment. The small β-sheets emerged very early during simulation as separated two-strand structures and a presence of the membrane facilitated this process. Later, the larger β-sheets were formed. However, the ninth helix which did not form paired structure stayed unchanged till the end of MD simulation. Paired helix–helix interactions seemed to be a driving force of β-sheet formation at early stages of amyloid formation. Contrary, the specific interactions between α-helix and β-sheet can be very stable and be stabilized by the membrane.
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      PubDate: 2015-07-03T15:42:05Z
       
  • CUDA ClustalW: An efficient parallel algorithm for progressive multiple
           sequence alignment on Multi-GPUs
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Che-Lun Hung , Yu-Shiang Lin , Chun-Yuan Lin , Yeh-Ching Chung , Yi-Fang Chung
      For biological applications, sequence alignment is an important strategy to analyze DNA and protein sequences. Multiple sequence alignment is an essential methodology to study biological data, such as homology modeling, phylogenetic reconstruction and etc. However, multiple sequence alignment is a NP-hard problem. In the past decades, progressive approach has been proposed to successfully align multiple sequences by adopting iterative pairwise alignments. Due to rapid growth of the next generation sequencing technologies, a large number of sequences can be produced in a short period of time. When the problem instance is large, progressive alignment will be time consuming. Parallel computing is a suitable solution for such applications, and GPU is one of the important architectures for contemporary parallel computing researches. Therefore, we proposed a GPU version of ClustalW v2.0.11, called CUDA ClustalW v1.0, in this work. From the experiment results, it can be seen that the CUDA ClustalW v1.0 can achieve more than 33× speedups for overall execution time by comparing to ClustalW v2.0.11.
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      PubDate: 2015-07-03T15:42:05Z
       
  • Structural properties and interaction energies affecting drug design. An
           approach combining molecular simulations, statistics, interaction energies
           and neural networks
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Dimitris Ioannidis , Georgios E. Papadopoulos , Georgios Anastassopoulos , Alexandros Kortsaris , Konstantinos Anagnostopoulos
      In order to elucidate some basic principles for protein–ligand interactions, a subset of 87 structures of human proteins with their ligands was obtained from the PDB databank. After a short molecular dynamics simulation (to ensure structure stability), a variety of interaction energies and structural parameters were extracted. Linear regression was performed to determine which of these parameters have a potentially significant contribution to the protein–ligand interaction. The parameters exhibiting relatively high correlation coefficients were selected. Important factors seem to be the number of ligand atoms, the ratio of N, O and S atoms to total ligand atoms, the hydrophobic/polar aminoacid ratio and the ratio of cavity size to the sum of ligand plus water atoms in the cavity. An important factor also seems to be the immobile water molecules in the cavity. Nine of these parameters were used as known inputs to train a neural network in the prediction of seven other. Eight structures were left out of the training to test the quality of the predictions. After optimization of the neural network, the predictions were fairly accurate given the relatively small number of structures, especially in the prediction of the number of nitrogen and sulfur atoms of the ligand.
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      PubDate: 2015-07-03T15:42:05Z
       
  • Editorial
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56




      PubDate: 2015-07-03T15:42:05Z
       
  • Genome level analysis of bacteriocins of lactic acid bacteria
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Neetigyata Pratap Singh , Abhay Tiwari , Ankiti Bansal , Shruti Thakur , Garima Sharma , Reema Gabrani
      Bacteriocins are antimicrobial peptides which are ribosomally synthesized by mainly all bacterial species. LABs (lactic acid bacteria) are a diverse group of bacteria that include around 20 genera of various species. Though LABs have a tremendous potential for production of anti-microbial peptides, this group of bacteria is still underexplored for bacteriocins. To study the diversity among bacteriocin encoding clusters and the putative bacteriocin precursors, genome mining was performed on 20 different species of LAB not reported to be bacteriocin producers. The phylogenetic tree of gyrB, rpoB, and 16S rRNA were constructed using MEGA6 software to analyze the diversity among strains. Putative bacteriocins operons identified were found to be diverse and were further characterized on the basis of physiochemical properties and the secondary structure. The presence of at least two cysteine residues in most of the observed putative bacteriocins leads to disulphide bond formation and provide stability. Our data suggests that LABs are prolific source of low molecular weight non modified peptides.
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      PubDate: 2015-07-03T15:42:05Z
       
  • A mathematical model of insulin resistance in Parkinson’s disease
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Elise M. Braatz , Randolph A. Coleman
      This paper introduces a mathematical model representing the biochemical interactions between insulin signaling and Parkinson’s disease. The model can be used to examine the changes that occur over the course of the disease as well as identify which processes would be the most effective targets for treatment. The model is mathematized using biochemical systems theory (BST). It incorporates a treatment strategy that includes several experimental drugs along with current treatments. In the past, BST models of neurodegeneration have used power law analysis and simulation (PLAS) to model the system. This paper recommends the use of MATLAB instead. MATLAB allows for more flexibility in both the model itself and in data analysis. Previous BST analyses of neurodegeneration began treatment at disease onset. As shown in this model, the outcomes of delayed, realistic treatment and full treatment at disease onset are significantly different. The delayed treatment strategy is an important development in BST modeling of neurodegeneration. It emphasizes the importance of early diagnosis, and allows for a more accurate representation of disease and treatment interactions.
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      PubDate: 2015-07-03T15:42:05Z
       
  • IFC Editorial Board
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57




      PubDate: 2015-07-03T15:42:05Z
       
  • The aspartate aminotransferase-like domain of Firmicutes MocR
           transcriptional regulators
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Teresa Milano , Roberto Contestabile , Alessandra Lo Presti , Massimo Ciccozzi , Stefano Pascarella
      Bacterial MocR transcriptional regulators possess an N-terminal DNA-binding domain containing a conserved helix-turn-helix module and an effector-binding and/or oligomerization domain at the C-terminus, homologous to fold type-I pyridoxal 5′-phosphate (PLP) enzymes. Since a comprehensive structural analysis of the MocR regulators is still missing, a comparisons of Firmicutes MocR sequences was undertook to contribute to the understanding of the structural characteristics of the C-terminal domain of these proteins, and to shed light on the structural and functional relationship with fold type-I PLP enzymes. Results of this work suggest the presence of at least three subgroups within the MocR sequences and provide a guide for rational site-directed mutagenesis studies aimed at deciphering the structure-function relationships in this new protein family.
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      PubDate: 2015-07-03T15:42:05Z
       
  • Computational prediction and biochemical characterization of novel RNA
           aptamers to Rift Valley fever virus nucleocapsid protein
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Mary Ellenbecker , Jeremy St. Goddard , Alec Sundet , Jean-Marc Lanchy , Douglas Raiford , J.Stephen Lodmell
      Rift Valley fever virus (RVFV) is a potent human and livestock pathogen endemic to sub-Saharan Africa and the Arabian Peninsula that has potential to spread to other parts of the world. Although there is no proven effective and safe treatment for RVFV infections, a potential therapeutic target is the virally encoded nucleocapsid protein (N). During the course of infection, N binds to viral RNA, and perturbation of this interaction can inhibit viral replication. To gain insight into how N recognizes viral RNA specifically, we designed an algorithm that uses a distance matrix and multidimensional scaling to compare the predicted secondary structures of known N-binding RNAs, or aptamers, that were isolated and characterized in previous in vitro evolution experiment. These aptamers did not exhibit overt sequence or predicted structure similarity, so we employed bioinformatic methods to propose novel aptamers based on analysis and clustering of secondary structures. We screened and scored the predicted secondary structures of novel randomly generated RNA sequences in silico and selected several of these putative N-binding RNAs whose secondary structures were similar to those of known N-binding RNAs. We found that overall the in silico generated RNA sequences bound well to N in vitro. Furthermore, introduction of these RNAs into cells prior to infection with RVFV inhibited viral replication in cell culture. This proof of concept study demonstrates how the predictive power of bioinformatics and the empirical power of biochemistry can be jointly harnessed to discover, synthesize, and test new RNA sequences that bind tightly to RVFV N protein. The approach would be easily generalizable to other applications.
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      PubDate: 2015-07-03T15:42:05Z
       
  • Binding energies of tyrosine kinase inhibitors: Error assessment of
           computational methods for imatinib and nilotinib binding
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Clifford W. Fong
      The binding energies of imatinib and nilotinib to tyrosine kinase have been determined by quantum mechanical (QM) computations, and compared with literature binding energy studies using molecular mechanics (MM). The potential errors in the computational methods include these critical factors: • Errors in X-ray structures such as structural distortions and steric clashes give unrealistically high van der Waals energies, and erroneous binding energies. • MM optimization gives a very different configuration to the QM optimization for nilotinib, whereas the imatinib ion gives similar configurations • Solvation energies are a major component of the overall binding energy. The QM based solvent model (PCM/SMD) gives different values from those used in the implicit PBSA solvent MM models. A major error in inhibitor—kinase binding lies in the non-polar solvation terms. • Solvent transfer free energies and the required empirical solvent accessible surface area factors for nilotinib and imatinib ion to give the transfer free energies have been reverse calculated. These values differ from those used in the MM PBSA studies. • An intertwined desolvation—conformational binding selectivity process is a balance of thermodynamic desolvation and intramolecular conformational kinetic control. • The configurational entropies (TΔS) are minor error sources.
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      PubDate: 2015-07-03T15:42:05Z
       
  • Scaffold assembly based on genome rearrangement analysis
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Sergey Aganezov , Nadia Sitdykova , Max A. Alekseyev
      Advances in DNA sequencing technology over the past decade have increased the volume of raw sequenced genomic data available for further assembly and analysis. While there exist many algorithms for assembly of sequenced genomic material, they often experience difficulties in constructing complete genomic sequences. Instead, they produce long genomic subsequences (scaffolds), which then become a subject to scaffold assembly aimed at reconstruction of their order along genome chromosomes. The balance between reliability and cost for scaffold assembly is not there just yet, which inspires one to seek for new approaches to address this problem. We present a new method for scaffold assembly based on the analysis of gene orders and genome rearrangements in multiple related genomes (some or even all of which may be fragmented). Evaluation of the proposed method on artificially fragmented mammalian genomes demonstrates its high reliability. We also apply our method for incomplete anophelinae genomes, which expose high fragmentation, and further validate the assembly results with referenced-based scaffolding. While the two methods demonstrate consistent results, the proposed method is able to identify more assembly points than the reference-based scaffolding.


      PubDate: 2015-07-03T15:42:05Z
       
  • Systems biology approach reveals possible evolutionarily conserved
           moonlighting functions for enolase
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Gabriela Prado Paludo , Karina Rodrigues Lorenzatto , Diego Bonatto , Henrique Bunselmeyer Ferreira
      Glycolytic enzymes, such as enolase, have been described as multifunctional complex proteins that also display non-glycolytic activities, termed moonlighting functions. Although enolase multifunctionality has been described for several organisms, the conservation of enolase alternative functions through different phyla has not been explored with more details. A useful strategy to investigate moonlighting functions is the use of systems biology tools, which allow the prediction of protein functions/interactions by graph design and analysis. In this work, available information from protein–protein interaction (PPI) databases were used to design enolase PPI networks for four eukaryotic organisms, namely Homo sapiens, Drosophila melanogaster, Caenorhabditis elegans, and Saccharomyces cerevisiae, covering a wide spectrum of this domain of life. PPI networks with number of nodes ranging from 140 to 411 and up to 15,855 connections were generated, and modularity and centrality analyses, and functional enrichment were performed for all of them. The performed analyses showed that enolase is a central node within the networks, and that, in addition to its canonical interactions with proteins related to glycolysis and energetic metabolism, it is also part of protein clusters related to different biological processes, like transcription, development, and apoptosis, among others. Some of these non-glycolytic clusters, are partially conserved between networks, in terms of overall sharing of orthologs, overall cluster structure, and/or at the levels of key regulatory proteins within clusters. Overall, our results provided evidences of enolase multifunctionality and evolutionary conservation of enolase PPIs at all these levels.
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      PubDate: 2015-07-03T15:42:05Z
       
  • The functional landscape bound to the transcription factors of Escherichia
           coli K-12
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Ernesto Pérez-Rueda , Silvia Tenorio-Salgado , Alejandro Huerta-Saquero , Yalbi I. Balderas-Martínez , Gabriel Moreno-Hagelsieb
      Motivated by the experimental evidences accumulated in the last ten years and based on information deposited in RegulonDB, literature look up, and sequence analysis, we analyze the repertoire of 304 DNA-binding Transcription factors (TFs) in Escherichia coli K-12. These regulators were grouped in 78 evolutionary families and are regulating almost half of the total genes in this bacterium. In structural terms, 60% of TFs are composed by two-domains, 30% are monodomain, and 10% three- and four-structural domains. As previously noticed, the most abundant DNA-binding domain corresponds to the winged helix-turn-helix, with few alternative DNA-binding structures, resembling the hypothesis of successful protein structures with the emergence of new ones at low scales. In summary, we identified and described the characteristics associated to the DNA-binding TF in E. coli K-12. We also identified twelve functional modules based on a co-regulated gene matrix. Finally, diverse regulons were predicted based on direct associations between the TFs and potential regulated genes. This analysis should increase our knowledge about the gene regulation in the bacterium E. coli K-12, and provide more additional clues for comprehensive modelling of transcriptional regulatory networks in other bacteria.
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      PubDate: 2015-07-03T15:42:05Z
       
  • GroupTracker: Video tracking system for multiple animals under severe
           occlusion
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Tsukasa Fukunaga , Shoko Kubota , Shoji Oda , Wataru Iwasaki
      Quantitative analysis of behaviors shown by interacting multiple animals can provide a key for revealing high-order functions of their nervous systems. To resolve these complex behaviors, a video tracking system that preserves individual identity even under severe overlap in positions, i.e., occlusion, is needed. We developed GroupTracker, a multiple animal tracking system that accurately tracks individuals even under severe occlusion. As maximum likelihood estimation of Gaussian mixture model whose components can severely overlap is theoretically an ill-posed problem, we devised an expectation–maximization scheme with additional constraints on the eigenvalues of the covariance matrix of the mixture components. Our system was shown to accurately track multiple medaka (Oryzias latipes) which freely swim around in three dimensions and frequently overlap each other. As an accurate multiple animal tracking system, GroupTracker will contribute to revealing unexplored structures and patterns behind animal interactions. The Java source code of GroupTracker is available at https://sites.google.com/site/fukunagatsu/software/group-tracker.
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      PubDate: 2015-07-03T15:42:05Z
       
  • Laplacian normalization and random walk on heterogeneous networks for
           disease-gene prioritization
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Zhi-Qin Zhao , Guo-Sheng Han , Zu-Guo Yu , Jinyan Li
      Random walk on heterogeneous networks is a recently emerging approach to effective disease gene prioritization. Laplacian normalization is a technique capable of normalizing the weight of edges in a network. We use this technique to normalize the gene matrix and the phenotype matrix before the construction of the heterogeneous network, and also use this idea to define the transition matrices of the heterogeneous network. Our method has remarkably better performance than the existing methods for recovering known gene–phenotype relationships. The Shannon information entropy of the distribution of the transition probabilities in our networks is found to be smaller than the networks constructed by the existing methods, implying that a higher number of top-ranked genes can be verified as disease genes. In fact, the most probable gene–phenotype relationships ranked within top 3 or top 5 in our gene lists can be confirmed by the OMIM database for many cases. Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene–phenotype relationships. All Matlab codes can be available upon email request.


      PubDate: 2015-07-03T15:42:05Z
       
  • Characterization and distribution of repetitive elements in association
           with genes in the human genome
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Kai-Chiang Liang , Joseph T. Tseng , Shaw-Jenq Tsai , H. Sunny Sun
      Repetitive elements constitute more than 50% of the human genome. Recent studies implied that the complexity of living organisms is not just a direct outcome of a number of coding sequences; the repetitive elements, which do not encode proteins, may also play a significant role. Though scattered studies showed that repetitive elements in the regulatory regions of a gene control gene expression, no systematic survey has been done to report the characterization and distribution of various types of these repetitive elements in the human genome. Sequences from 5′ and 3′ untranslated regions and upstream and downstream of a gene were downloaded from the Ensembl database. The repetitive elements in the neighboring of each gene were identified and classified using cross-matching implemented in the RepeatMasker. The annotation and distribution of distinct classes of repetitive elements associated with individual gene were collected to characterize genes in association with different types of repetitive elements using systems biology program. We identified a total of 1,068,400 repetitive elements which belong to 37-class families and 1235 subclasses that are associated with 33,761 genes and 57,365 transcripts. In addition, we found that the tandem repeats preferentially locate proximal to the transcription start site (TSS) of genes and the major function of these genes are involved in developmental processes. On the other hand, interspersed repetitive elements showed a tendency to be accumulated at distal region from the TSS and the function of interspersed repeat-containing genes took part in the catabolic/metabolic processes. Results from the distribution analysis were collected and used to construct a gene-based repetitive element database (GBRED; http://www.binfo.ncku.edu.tw/GBRED/index.html). A user-friendly web interface was designed to provide the information of repetitive elements associated with any particular gene(s). This is the first study focusing on the gene-associated repetitive elements in the human genome. Our data showed distinct genes associated with different kinds of repetitive element and implied such combination may shape the function of these genes. Aside from the conventional view of these elements in genome evolution, results from this study offer a systemic review to facilitate exploitation of these elements in genome function.


      PubDate: 2015-07-03T15:42:05Z
       
  • IFC Editorial Board
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56




      PubDate: 2015-07-03T15:42:05Z
       
  • Title page
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56




      PubDate: 2015-07-03T15:42:05Z
       
  • Principles for the Organization of Gene-Sets
    • Abstract: Publication date: Available online 10 June 2015
      Source:Computational Biology and Chemistry
      Author(s): Wentian Li , Jan Freudenberg , Michaela Oswald
      A gene-set, an important concept in microarray expression analysis and systems biology, is a collection of genes and/or their products (i.e. proteins) that have some features in common. There are many different ways to construct gene-sets, but a systematic organization of these ways is lacking. Gene-sets are mainly organized ad hoc in current public-domain databases, with group header names often determined by practical reasons (such as the types of technology in obtaining the gene-sets, or a balanced number of gene-sets under a header, etc). Here we aim at providing a gene-set organization principle according to the level at which genes are connected: homology, physical map proximity, chemical interaction, biological, and phenotypic-medical levels. We also distinguish two types of connections between genes: actual connection versus sharing of a label. Actual connections denote direct biological interactions, whereas shared label connection denote shared membership in a group. Some extensions of the framework are also addressed such as overlapping of gene-sets, modules, and the incorporation of other non-protein-coding entities such as microRNAs.


      PubDate: 2015-07-03T15:42:05Z
       
  • Reconstructing gene regulatory networks from knock-out data using Gaussian
           noise model and pearson correlation coefficient
    • Abstract: Publication date: Available online 17 June 2015
      Source:Computational Biology and Chemistry
      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.
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      PubDate: 2015-07-03T15:42:05Z
       
  • In silico identification of novel IL-1β inhibitors to target
           protein–protein interfaces
    • Abstract: Publication date: Available online 30 June 2015
      Source:Computational Biology and Chemistry
      Author(s): Sobia Ahsan Halim , Muhammad Jawad , Muhammad Ilyas , Zulfiqar Mir , Atif Anwar Mirza , Tayyab Husnain
      Interleukin-1β is a drug target in rheumatoid arthritis and several auto-immune disorders. In this study, a set of 48 compounds with the determined IC50 values were used for QSAR analysis by MOE. The QSAR model was developed by using training set of 41 compounds, based on 12 unique descriptors. Model was validated by predicting the IC50 values for a test set of seven compounds. A correlation analysis was carried out comparing the statistics of the measured IC50 values with predicted ones. Subsequently, model was used for the screening of a large data set of 7,397,957 compounds obtained from “Drugs Now” category of ZINC database. The activities of those compounds were predicted by developed model. 708,960 compounds that showed best predicted activities were chosen for further studies. Additionally this set of 708,960 compounds was screened by pharmacophore modelling that led to the retrieval of 1809 molecules. Finally docking of 1809 molecules was conducted at the IL-1β receptor binding site using MOE and FRED docking program. Several new compounds were predicted as IL-1β inhibitors in silico. This study provides valuable insight for designing more potent and selective inhibitors for the treatment of inflammatory diseases.
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      PubDate: 2015-07-03T15:42:05Z
       
  • Acknowledgment to reviewers
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57




      PubDate: 2015-07-03T15:42:05Z
       
  • Identification of potential Tpx inhibitors against pathogen-host
           interactions
    • Abstract: Publication date: Available online 2 July 2015
      Source:Computational Biology and Chemistry
      Author(s): Utku Deniz , Kutlu O. Ulgen , Elif Ozkirimli
      Yersinia organisms cause many infectious diseases by invading human cells and delivering their virulence factors via the type three secretion system (T3SS). One alternative strategy in the fight against these pathogenic organisms is to interfere with their T3SS. Previous studies demonstrated that thiol peroxidase, Tpx is functional in the assembly of T3SS and its inhibition by salicylidene acylhydrazides prevents the secretion of pathogenic effectors. In this study, the aim was to identify potential inhibitors of Tpx using an integrated approach starting with high throughput virtual screening and ending with molecular dynamics simulations of selected ligands. Virtual screening of ZINC database of 500 000 compounds via ligand-based and structure-based pharmacophore models retrieved 10 000 hits. The structure-based pharmacophore model was validated using high-throughput virtual screening (HTVS). After multistep docking (SP and XP), common scaffolds were used to find common substructures and the ligand binding poses were optimized using induced fit docking. The stability of the protein – ligand complex was examined with molecular dynamics simulations and the binding free energy of the complex was calculated. As a final outcome eight compounds with different chemotypes were proposed as potential inhibitors for Tpx. The eight ligands identified by a detailed virtual screening protocol can serve as leads in future drug design efforts against the destructive actions of pathogenic bacteria.
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      PubDate: 2015-07-03T15:42:05Z
       
  • Tumor stratification by a novel graph-regularized bi-clique finding
           algorithm
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Amin Ahmadi Adl , Xiaoning Qian
      Due to involved disease mechanisms, many complex diseases such as cancer, demonstrate significant heterogeneity with varying behaviors, including different survival time, treatment responses, and recurrence rates. The aim of tumor stratification is to identify disease subtypes, which is an important first step towards precision medicine. Recent advances in profiling a large number of molecular variables such as in The Cancer Genome Atlas (TCGA), have enabled researchers to implement computational methods, including traditional clustering and bi-clustering algorithms, to systematically analyze high-throughput molecular measurements to identify tumor subtypes as well as their corresponding associated biomarkers. In this study we discuss critical issues and challenges in existing computational approaches for tumor stratification. We show that the problem can be formulated as finding densely connected sub-graphs (bi-cliques) in a bipartite graph representation of genomic data. We propose a novel algorithm that takes advantage of prior biology knowledge through a gene–gene interaction network to find such sub-graphs, which helps simultaneously identify both tumor subtypes and their corresponding genetic markers. Our experimental results show that our proposed method outperforms current state-of-the-art methods for tumor stratification.


      PubDate: 2015-07-03T15:42:05Z
       
  • BagReg: Protein inference through machine learning
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Can Zhao , Dao Liu , Ben Teng , Zengyou He
      Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms.
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      PubDate: 2015-07-03T15:42:05Z
       
  • A semi-supervised learning approach for RNA secondary structure prediction
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Haruka Yonemoto , Kiyoshi Asai , Michiaki Hamada
      RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited.


      PubDate: 2015-07-03T15:42:05Z
       
  • DNA entropy reveals a significant difference in complexity between
           housekeeping and tissue specific gene promoters
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): David Thomas , Chris Finan , Melanie J. Newport , Susan Jones
      Background The complexity of DNA can be quantified using estimates of entropy. Variation in DNA complexity is expected between the promoters of genes with different transcriptional mechanisms; namely housekeeping (HK) and tissue specific (TS). The former are transcribed constitutively to maintain general cellular functions, and the latter are transcribed in restricted tissue and cells types for specific molecular events. It is known that promoter features in the human genome are related to tissue specificity, but this has been difficult to quantify on a genomic scale. If entropy effectively quantifies DNA complexity, calculating the entropies of HK and TS gene promoters as profiles may reveal significant differences. Results Entropy profiles were calculated for a total dataset of 12,003 human gene promoters and for 501 housekeeping (HK) and 587 tissue specific (TS) human gene promoters. The mean profiles show the TS promoters have a significantly lower entropy (p <2.2e−16) than HK gene promoters. The entropy distributions for the 3 datasets show that promoter entropies could be used to identify novel HK genes. Conclusion Functional features comprise DNA sequence patterns that are non-random and hence they have lower entropies. The lower entropy of TS gene promoters can be explained by a higher density of positive and negative regulatory elements, required for genes with complex spatial and temporary expression.
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      PubDate: 2015-07-03T15:42:05Z
       
  • A novel biological role for nsLTP2 from Oriza sativa: Potential
           incorporation with anticancer agents, nucleosides and their analogues
    • Abstract: Publication date: October 2015
      Source:Computational Biology and Chemistry, Volume 58
      Author(s): Mojtaba Tousheh , Fatemeh Zahra Darvishi , Mehran Miroliaei
      Development of a protein-based drug delivery system has major impact on the efficacy and bioavailability of unstable and water insoluble drugs. In the present study, the binding modes of a nonspecific lipid transfer protein (nsLTP2) from Oryza sativa with various nucleosides and analogous molecules were identified. The 3-D structure of the protein was designed and validated using modeler 9.13, Molegro virtual docker and procheck tool, respectively. The binding affinity and strength of interactions, key contributing residues and specificity toward the substrates were accomplished by computational docking and model prediction. The protein presented high affinity to acyclovir and vidarabine as purine-analogous drugs. Binding affinity is influenced by the core template and functional groups of the ligands which are structurally different cause the variation of interaction energies with nsLTP2. Nonetheless, all the evaluated analogous drugs occupy the proximity space at the nsLTP active site with high similarity in their binding modes. Our findings hold great promise for the future applications of nsLTPs in various aspects of pharmaceutical science and molecular biology.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • A time and space complexity reduction for coevolutionary analysis of trees
           generated under both a Yule and Uniform model
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Benjamin Drinkwater , Michael A. Charleston
      The topology or shape of evolutionary trees and their unbalanced nature has been a long standing area of interest in the field of phylogenetics. Coevolutionary analysis, which considers the evolutionary relationships between a pair of phylogenetic trees, has to date not considered leveraging this unbalanced nature as a means to reduce the complexity of coevolutionary analysis. In this work we apply previous analyses of tree shapes to improve the efficiency of inferring coevolutionary events. In particular, we use this prior research to derive a new data structure for inferring coevolutionary histories. Our new data structure is proven to provide a reduction in the time and space required to infer coevolutionary events. It is integrated into an existing framework for coevolutionary analysis and has been validated using both synthetic and previously published biological data sets. This proposed data structure performs twice as fast as algorithms implemented using existing data structures with no degradation in the algorithm's accuracy. As the coevolutionary data sets increase in size so too does the running time reduction provided by the newly proposed data structure. This is due to our data structure offering a logarithmic time and space complexity improvement. As a result, the proposed update to existing coevolutionary analysis algorithms outlined herein should enable the inference of larger coevolutionary systems in the future.
      Graphical abstract image Highlights

      PubDate: 2015-07-03T15:42:05Z
       
  • A new vision of evaluating gene expression signatures
    • Abstract: Publication date: August 2015
      Source:Computational Biology and Chemistry, Volume 57
      Author(s): Hung-Ming Lai , Celal Özturk , Andreas Albrecht , Kathleen Steinhöfel
      Gene expression profiles based on high-throughput technologies contribute to molecular classifications of different cell lines and consequently to clinical diagnostic tests for cancer types and other diseases. Statistical techniques and dimension reduction methods have been devised for identifying minimal gene subset with maximal discriminative power. For sets of in silico candidate genes, assuming a unique gene signature or performing a parsimonious signature evaluation seems to be too restrictive in the context of in vitro signature validation. This is mainly due to the high complexity of largely correlated expression measurements and the existence of various oncogenic pathways. Consequently, it might be more advantageous to identify and evaluate multiple gene signatures with a similar good predictive power, which are referred to as near-optimal signatures, to be made available for biological validation. For this purpose we propose the bead-chain-plot approach originating from swarm intelligence techniques, and a small scale computational experiment is conducted in order to convey our vision. We simulate the acquisition of candidate genes by using a small pool of differentially expressed genes derived from microarray-based CNS tumour data. The application of the bead-chain-plot provides experimental evidence for improved classifications by using near-optimal signatures in validation procedures.
      Graphical abstract image Highlights

      PubDate: 2015-07-03T15:42:05Z
       
  • Computational Analysis of CRTh2 receptor antagonist: A Ligand-based CoMFA
           and CoMSIA approach
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Sathya Babu , Honglae Sohn , Thirumurthy Madhavan
      CRTh2 receptor is an important mediator of inflammatory effects and has attracted much attention as a therapeutic target for the treatment of conditions such as asthma, COPD, allergic rhinitis and atopic dermatitis. In pursuit of better CRTh2 receptor antagonist agents, 3D-QSAR studies were performed on a series of 2-(2-(benzylthio)-1H-benzo[d]imidazol-1-yl) acetic acids. There is no crystal structure information available on this protein; hence in this work, ligand-based comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed by atom by atom matching alignment using systematic search and simulated annealing methods. The 3D-QSAR models were generated with 10 different combinations of test and training set molecules, since the robustness and predictive ability of the model is very important. We have generated 20 models for CoMFA and 100 models for CoMSIA based on two different alignments. Each model was validated with statistical cut off values such as q 2 >0.4, r 2 >0.5 and r 2 pred >0.5. Based on better q 2 and r 2 pred values, the best predictions were obtained for the CoMFA (model 5 q 2 =0.488, r 2 pred =0.732), and CoMSIA (model 45 q 2 =0.525, r 2 pred =0.883) from systematic search conformation alignment. The high correlation between the cross-validated/predicted and experimental activities of a test set revealed that the CoMFA and CoMSIA models were robust. Statistical parameters from the generated QSAR models indicated the data is well fitted and have high predictive ability. The generated models suggest that steric, electrostatic, hydrophobic, H-bond donor and acceptor parameters are important for activity. Our study serves as a guide for further experimental investigations on the synthesis of new CRTh2 antagonist.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Genomic distribution and possible functional roles of putative
           G-quadruplex motifs in two subspecies of Oryza sativa
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Yu Wang , Minglang Zhao , Qingyan Zhang , Guo-Fei Zhu , Fei-Fan Li , Lin-Fang Du
      G-quadruplex is a stable, four-stranded DNA or RNA structure formed from guanine-rich regions and implicated in telomere maintenance, replication, gene regulation at transcription level or translation level, etc. Based on bioinformatics methods, we analyzed different putative G-quadruplex motifs (PGQMs) patterns in various genomic regions of two subspecies (indica and japonica) of Oryza sativa and the whole genomes of other 8 species. In total, in the 10 species we discussed, the PGQMs densities in monocots were higher than dicots. 40,483 and 31,795 PGQMs were identified with a density of 108.46 and 84.89 PGQMs/Mb, respectively, in japonica and indica genomes, 10,655 and 5420 loci were found to contain at least one PGQM in their gene bodies (with a percentage of 19% and 14%) indicating a wide distribution of G-quadruplex motifs in O. sativa genome. They preferred to locate in transcription start sites proximal regions and 5′-UTR with relative high enrichment. This phenomenon supports the hypothesis that PGQMs are involved in gene transcription and translation. In addition, we analyzed the distribution of different loop length in G-quadruplex and found the density of long loop PGQMs was less than short loop in indica’s intron but it was similar in japonica. Meanwhile, we focused on the loci with PGQMs and conducted gene ontology (GO) analysis of them. As a result, many GO terms were identified and significantly correlated with the loci containing at least one PGQM. The GO analysis in the two subspecies of rice may be helpful for elucidating the functional roles of G-quadruplexes.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Systematic investigation of sequence and structural motifs that recognize
           ATP
    • Abstract: Publication date: June 2015
      Source:Computational Biology and Chemistry, Volume 56
      Author(s): Ke Chen , Dacheng Wang , Lukasz Kurgan
      Interaction between ATP, a multifunctional and ubiquitous nucleotide, and proteins initializes phosphorylation, polypeptide synthesis and ATP hydrolysis which supplies energy for metabolism. However, current knowledge concerning the mechanisms through which ATP is recognized by proteins is incomplete, scattered, and inaccurate. We systemically investigate sequence and structural motifs of proteins that recognize ATP. We identified three novel motifs and refined the known p-loop and class II aminoacyl-tRNA synthetase motifs. The five motifs define five distinct ATP–protein interaction modes which concern over 5% of known protein structures. We demonstrate that although these motifs share a common GXG tripeptide they recognize ATP through different functional groups. The p-loop motif recognizes ATP through phosphates, class II aminoacyl-tRNA synthetase motif targets adenosine and the other three motifs recognize both phosphates and adenosine. We show that some motifs are shared by different enzyme types. Statistical tests demonstrate that the five sequence motifs are significantly associated with the nucleotide binding proteins. Large-scale test on PDB reveals that about 98% of proteins that include one of the structural motifs are confirmed to bind ATP.
      Graphical abstract image

      PubDate: 2015-07-03T15:42:05Z
       
  • Ancestral population genomics using coalescence hidden Markov models and
           heuristic optimisation algorithms
    • Abstract: Publication date: Available online 4 March 2015
      Source:Computational Biology and Chemistry
      Author(s): Jade Cheng , Thomas Mailund
      With full genome data from several closely related species now readily available, we have the ultimate data for demographic inference. Exploiting these full genomes, however, requires models that can explicitly model recombination along alignments of full chromosomal length. Over the last decade a class of models, based on the sequential Markov coalescence model combined with hidden Markov models, has been developed and used to make inference in simple demographic scenarios. To move forward to more complex demographic modelling we need better and more automated ways of specifying these models and efficient optimisation algorithms for inferring the parameters in complex and often high-dimensional models. In this paper we present a framework for building such coalescence hidden Markov models for pairwise alignments and present results for using heuristic optimisation algorithms for parameter estimation. We show that we can build more complex demographic models than our previous frameworks and that we obtain more accurate parameter estimates using heuristic optimisation algorithms than when using our previous gradient based approaches. Our new framework provides a flexible way of constructing coalescence hidden Markov models almost automatically. While estimating parameters in more complex models is still challenging we show that using heuristic optimisation algorithms we still get a fairly good accuracy.


      PubDate: 2015-03-08T19:03:40Z
       
 
 
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