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  Subjects -> ENGINEERING (Total: 2070 journals)
    - CHEMICAL ENGINEERING (171 journals)
    - CIVIL ENGINEERING (161 journals)
    - ELECTRICAL ENGINEERING (88 journals)
    - ENGINEERING (1148 journals)
    - ENGINEERING MECHANICS AND MATERIALS (320 journals)
    - HYDRAULIC ENGINEERING (52 journals)
    - INDUSTRIAL ENGINEERING (52 journals)
    - MECHANICAL ENGINEERING (78 journals)

CHEMICAL ENGINEERING (171 journals)                  1 2     

ACS Combinatorial Science     Full-text available via subscription   (Followers: 9)
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: 21)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 5)
Advanced Chemical Engineering Research     Open Access   (Followers: 9)
Advanced Powder Technology     Hybrid Journal   (Followers: 12)
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: 4)
Applied Petrochemical Research     Open Access   (Followers: 3)
Asia-Pacific Journal of Chemical Engineering     Hybrid Journal   (Followers: 6)
Biochemical Engineering Journal     Hybrid Journal   (Followers: 9)
Biomass Conversion and Biorefinery     Partially Free   (Followers: 5)
BMC Chemical Biology     Open Access   (Followers: 4)
Brazilian Journal of Chemical Engineering     Open Access   (Followers: 2)
Bulletin of the Chemical Society of Ethiopia     Open Access   (Followers: 1)
Carbohydrate Polymers     Hybrid Journal   (Followers: 9)
Catalysts     Open Access   (Followers: 7)
Chemical and Engineering News     Free   (Followers: 4)
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: 30)
Chemical Engineering & Technology     Hybrid Journal   (Followers: 24)
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: 20)
Chemical Engineering Research and Design     Hybrid Journal   (Followers: 16)
Chemical Engineering Research Bulletin     Open Access  
Chemical Engineering Science     Hybrid Journal   (Followers: 13)
Chemical Geology     Hybrid Journal   (Followers: 10)
Chemical Papers     Hybrid Journal   (Followers: 3)
Chemical Product and Process Modeling     Full-text available via subscription   (Followers: 3)
Chemical Reviews     Full-text available via subscription   (Followers: 342)
Chemical Society Reviews     Full-text available via subscription   (Followers: 31)
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: 250)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 6)
ChemSusChem     Hybrid Journal   (Followers: 7)
Chinese Chemical Letters     Full-text available via subscription   (Followers: 2)
Chinese Journal of Chemical Engineering     Full-text available via subscription   (Followers: 3)
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: 7)
CORROSION     Full-text available via subscription   (Followers: 1)
Corrosion Reviews     Full-text available via subscription   (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: 41)
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)
Geochemistry International     Hybrid Journal  
Handbook of Powder Technology     Full-text available via subscription   (Followers: 2)
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: 12)
Industrial & Engineering Chemistry     Full-text available via subscription   (Followers: 9)
Industrial & Engineering Chemistry Research     Full-text available via subscription   (Followers: 19)
Industrial Chemistry Library     Full-text available via subscription   (Followers: 4)
Info Chimie Magazine     Full-text available via subscription   (Followers: 3)
International Journal of Chemical and Petroleum Sciences     Open Access   (Followers: 2)
International Journal of Chemical Engineering     Open Access   (Followers: 7)
International Journal of Chemical Reactor Engineering     Full-text available via subscription   (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)
ISRN Chemical Engineering     Open Access   (Followers: 4)
ISRN Polymer Science     Open Access   (Followers: 11)
Journal of Applied Crystallography     Hybrid Journal   (Followers: 4)
Journal of Applied Electrochemistry     Hybrid Journal   (Followers: 13)
Journal of Applied Polymer Science     Hybrid Journal   (Followers: 220)
Journal of Biomaterials Science, Polymer Edition     Hybrid Journal   (Followers: 8)
Journal of Chemical & Engineering Data     Full-text available via subscription   (Followers: 11)
Journal of Chemical Ecology     Hybrid Journal   (Followers: 2)
Journal of Chemical Engineering     Open Access   (Followers: 4)
Journal of Chemical Engineering and Materials Science     Open Access  
Journal of Chemical Science and Technology     Open Access   (Followers: 3)

        1 2     

Journal Cover   Computational Biology and Chemistry
  [SJR: 0.558]   [H-I: 39]   [10 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1476-9271
   Published by Elsevier Homepage  [2589 journals]
  • Structural properties and interaction energies affecting drug design. An
           approach combining molecular simulations, statistics, interaction energies
           and neural networks
    • Abstract: Publication date: Available online 25 February 2015
      Source:Computational Biology and Chemistry
      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 sulphur atoms of the ligand.
      Graphical abstract image

      PubDate: 2015-02-28T23:07:29Z
       
  • Characterization and distribution of repetitive elements in association
           with genes in the human genome
    • Abstract: Publication date: Available online 27 February 2015
      Source:Computational Biology and Chemistry
      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 neighbouring 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 1068,400 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: 2015-02-28T23:07:29Z
       
  • A new vision of evaluating gene expression signatures
    • Abstract: Publication date: Available online 28 February 2015
      Source:Computational Biology and Chemistry
      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: 2015-02-28T23:07:29Z
       
  • Determinism and randomness in the evolution of introns and sine inserts in
           mouse and human mitochondrial solute carrier and cytokine receptor genes
    • Abstract: Publication date: April 2015
      Source:Computational Biology and Chemistry, Volume 55
      Author(s): Antonia Cianciulli , Rosa Calvello , Maria A. Panaro
      In the homologous genes studied, the exons and introns alternated in the same order in mouse and human. We studied, in both species: corresponding short segments of introns, whole corresponding introns and complete homologous genes. We considered the total number of nucleotides and the number and orientation of the SINE inserts. Comparisons of mouse and human data series showed that at the level of individual relatively short segments of intronic sequences the stochastic variability prevails in the local structuring, but at higher levels of organization a deterministic component emerges, conserved in mouse and human during the divergent evolution, despite the ample re-editing of the intronic sequences and the fact that processes such as SINE spread had taken place in an independent way in the two species. Intron conservation is negatively correlated with the SINE occupancy, suggesting that virus inserts interfere with the conservation of the sequences inherited from the common ancestor.
      Graphical abstract image

      PubDate: 2015-02-28T23:07:29Z
       
  • GroupTracker: Video tracking system for multiple animals under severe
           occlusion
    • Abstract: Publication date: Available online 18 February 2015
      Source:Computational Biology and Chemistry
      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.
      Graphical abstract image Highlights

      PubDate: 2015-02-28T23:07:29Z
       
  • A semi-supervised learning approach for RNA secondary structure prediction
    • Abstract: Publication date: Available online 20 February 2015
      Source:Computational Biology and Chemistry
      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: 2015-02-28T23:07:29Z
       
  • Study of early stages of amyloid Aβ13-23 formation using molecular
           dynamics simulation in implicit environments
    • Abstract: Publication date: Available online 25 February 2015
      Source:Computational Biology and Chemistry
      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.
      Graphical abstract image

      PubDate: 2015-02-28T23:07:29Z
       
  • Molecular characterization, modeling and docking of CYP107CB2 from
           Bacillus lehensis G1, an alkaliphile
    • Abstract: Publication date: Available online 25 February 2015
      Source:Computational Biology and Chemistry
      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: 2015-02-28T23:07:29Z
       
  • 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.
      Graphical abstract image

      PubDate: 2015-02-28T23:07:29Z
       
  • Tri-peptide reference structures for the calculation of relative solvent
           accessible surface area in protein amino acid residues
    • Abstract: Publication date: February 2015
      Source:Computational Biology and Chemistry, Volume 54
      Author(s): Christopher M. Topham , Jeremy C. Smith
      Relative amino acid residue solvent accessibility values allow the quantitative comparison of atomic solvent-accessible surface areas in different residue types and physical environments in proteins and in protein structural alignments. Geometry-optimised tri-peptide structures in extended solvent-exposed reference conformations have been obtained for 43 amino acid residue types at a high level of quantum chemical theory. Significant increases in side-chain solvent accessibility, offset by reductions in main-chain atom solvent exposure, were observed for standard residue types in partially geometry-optimised structures when compared to non-minimised models built from identical sets of proper dihedral angles abstracted from the literature. Optimisation of proper dihedral angles led most notably to marked increases of up to 54% in proline main-chain atom solvent accessibility compared to literature values. Similar effects were observed for fully-optimised tri-peptides in implicit solvent. The relief of internal strain energy was associated with systematic variation in N, Cα and Cβ atom solvent accessibility across all standard residue types. The results underline the importance of optimisation of ‘hard’ degrees of freedom (bond lengths and valence bond angles) and improper dihedral angle values from force field or other context-independent reference values, and impact on the use of standardised fixed internal co-ordinate geometry in sampling approaches to the determination of absolute values of protein amino acid residue solvent accessibility. Quantum chemical methods provide a useful and accurate alternative to molecular mechanics methods to perform energy minimisation of peptides containing non-standard (chemically modified) amino acid residues frequently present in experimental protein structure data sets, for which force field parameters may not be available. Reference tri-peptide atomic co-ordinate sets including hydrogen atoms are made freely available.
      Graphical abstract image

      PubDate: 2015-02-13T09:48:36Z
       
  • A time and space complexity reduction for coevolutionary analysis of trees
           generated under both a Yule and Uniform model
    • Abstract: Publication date: Available online 11 February 2015
      Source:Computational Biology and Chemistry
      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: 2015-02-13T09:48:36Z
       
  • Molecular phylogenetic study and expression analysis of ATP-binding
           cassette transporter gene family in Oryza sativa in response to salt
           stress
    • Abstract: Publication date: February 2015
      Source:Computational Biology and Chemistry, Volume 54
      Author(s): Jayita Saha , Atreyee Sengupta , Kamala Gupta , Bhaskar Gupta
      ATP-binding cassette (ABC) transporter is a large gene superfamily that utilizes the energy released from ATP hydrolysis for transporting myriad of substrates across the biological membranes. Although many investigations have been done on the structural and functional analysis of the ABC transporters in Oryza sativa, much less is known about molecular phylogenetic and global expression pattern of the complete ABC family in rice. In this study, we have carried out a comprehensive phylogenetic analysis constructing neighbor-joining and maximum-likelihood trees based on various statistical methods of different ABC protein subfamily of five plant lineages including Chlamydomonas reinhardtii (green algae), Physcomitrella patens (moss), Selaginella moellendorffii (lycophyte), Arabidopsis thaliana (dicot) and O. sativa (monocot) to explore the origin and evolutionary patterns of these ABC genes. We have identified several conserved motifs in nucleotide binding domain (NBD) of ABC proteins among all plant lineages during evolution. Amongst the different ABC protein subfamilies, ‘ABCE’ has not yet been identified in lower plant genomes (algae, moss and lycophytes). The result indicated that gene duplication and diversification process acted upon these genes as a major operative force creating new groups and subgroups and functional divergence during evolution. We have demonstrated that rice ABCI subfamily consists of only half size transporters that represented highly dynamic members showing maximum sequence variations among the other rice ABC subfamilies. The evolutionary and the expression analysis contribute to a deep insight into the evolution and diversity of rice ABC proteins and their roles in response to salt stress that facilitate our further understanding on rice ABC transporters.
      Graphical abstract image

      PubDate: 2015-02-13T09:48:36Z
       
  • The frequency of poly(G) tracts in the human genome and their use as a
           sensor of DNA damage
    • Abstract: Publication date: February 2015
      Source:Computational Biology and Chemistry, Volume 54
      Author(s): Vincent Murray
      Tandem repeats of short DNA sequences are commonly found in human DNA. These simple sequence repeats or microsatellites are highly polymorphic in the human genome. Since the anti-tumour agent cisplatin preferentially forms DNA adducts at runs of consecutive guanine nucleotides (poly(G)), the position and frequency of occurrence of poly(G) sequences in the updated human genome was investigated. There are more runs of consecutive guanines than would be expected by random chance. This especially true for poly(G) sequences longer than approximately n =9. A plot of poly(G) length against log(observed/expected) frequency produced a straight line for n >9. A similar observation was also found for poly(A) DNA sequence repeats. This data implied that the increase in observed/expected frequency is directly related to length of DNA repeat. It was proposed that long runs of consecutive guanine nucleotides could be a sensitive sensor of cellular DNA damage since a number of DNA damaging agents cause lesions at poly(G) sequences.
      Graphical abstract image

      PubDate: 2015-02-13T09:48:36Z
       
  • BagReg: Protein Inference through Machine Learning
    • Abstract: Publication date: Available online 7 February 2015
      Source:Computational Biology and Chemistry
      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.
      Graphical abstract image Highlights

      PubDate: 2015-02-13T09:48:36Z
       
  • A balance-evolution artificial bee colony algorithm for protein structure
           optimization based on a three-dimensional AB off-lattice model
    • Abstract: Publication date: February 2015
      Source:Computational Biology and Chemistry, Volume 54
      Author(s): Bai Li , Raymond Chiong , Mu Lin
      Protein structure prediction is a fundamental issue in the field of computational molecular biology. In this paper, the AB off-lattice model is adopted to transform the original protein structure prediction scheme into a numerical optimization problem. We present a balance-evolution artificial bee colony (BE-ABC) algorithm to address the problem, with the aim of finding the structure for a given protein sequence with the minimal free-energy value. This is achieved through the use of convergence information during the optimization process to adaptively manipulate the search intensity. Besides that, an overall degradation procedure is introduced as part of the BE-ABC algorithm to prevent premature convergence. Comprehensive simulation experiments based on the well-known artificial Fibonacci sequence set and several real sequences from the database of Protein Data Bank have been carried out to compare the performance of BE-ABC against other algorithms. Our numerical results show that the BE-ABC algorithm is able to outperform many state-of-the-art approaches and can be effectively employed for protein structure optimization.
      Graphical abstract image

      PubDate: 2015-02-13T09:48:36Z
       
  • Title page
    • Abstract: Publication date: February 2015
      Source:Computational Biology and Chemistry, Volume 54




      PubDate: 2015-02-13T09:48:36Z
       
  • IFC Editorial Board
    • Abstract: Publication date: February 2015
      Source:Computational Biology and Chemistry, Volume 54




      PubDate: 2015-02-13T09:48:36Z
       
  • Laplacian Normalization and Random Walk on Heterogeneous Networks for
           Disease-gene Prioritization
    • Abstract: Publication date: Available online 7 February 2015
      Source:Computational Biology and Chemistry
      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: 2015-02-13T09:48:36Z
       
  • Identifying microRNAs involved in cancer pathway using support vector
           machines
    • Abstract: Publication date: April 2015
      Source:Computational Biology and Chemistry, Volume 55
      Author(s): Ram Kothandan , Sumit Biswas
      Since Ambros’ discovery of small non-protein coding RNAs in the early 1990s, the past two decades have seen an upsurge in the number of reports of predicted microRNAs (miR), which have been implicated in various functions. The correlation of miRs with cancer has spurred the usage of this class of non-coding RNAs in various cancer therapies, although most of them are at trial stages. However, the experimental identification of a miR to be associated with cancer is still an elaborate, time-consuming process. To aid this process of miR association, we undertook an in-silico study involving the identification of global signatures in experimentally validated microRNAs associated with cancer. Subsequently, a support vector machine based two-step binary classifier system has been trained and modeled from the features extracted from the above study. A total of 60 distinguishing features were selected and ranked to form the feature set for classification – 26 of these extracted from the miR sequence itself, and the remainder from the thermodynamics of folding and the hybridized miRNA–mRNA structure. The two step classifier model – miRSEQ and miRINT had reasonably good performance measures with fairly high values of Matthew’s correlation coefficient (MCC) values ranging from 0.72 to 0.82 (availability: https://sites.google.com/site/sumitslab/tools).
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      PubDate: 2015-02-13T09:48:36Z
       
  • Inferring biological basis about psychrophilicity by interpreting the
           rules generated from the correctly classified input instances by a
           classifier
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part B
      Author(s): Abhigyan Nath , Karthikeyan Subbiah
      Organisms thriving at extreme cold surroundings are called as psychrophiles and they present a wealth of knowledge about sequence adjustments in proteins that had occurred during the adaptation to low temperatures. In this paper, we propose a new cascading model to investigate the basis for psychrophilicity. In this model, a superior classifier was used to discriminate psychrophilic from mesophilic protein sequences, and then the PART rule generating algorithm was applied on the input instances that are correctly classified by the classifier, to generate human interpretable rules. These derived rules were further validated on a structural dataset and finally analyzed to discover the underlying biological basis about the psychrophilicity. In this study, we have used one of the key features of psychrophilic proteins accountable for remaining functional in extreme cold temperature surroundings i.e., global patterns of amino acid composition as the input features. The rotation forest classifier outperformed all the other classifiers with maximum accuracy of 70.5% and maximum AUC of 0.78. The effect of sequence length on the classification accuracy was also investigated. The analysis of the derived rules and interpretation of the analyzed results had revealed some interesting phenomena such as the amino acids A, D, G, F, and S are over-represented, and T is under-represented in psychrophilic proteins. These findings augment the existing domain knowledge for psychrophilic sequence features.
      Graphical abstract image

      PubDate: 2015-02-13T09:48:36Z
       
  • IDENTIFICATION OF INHIBITORS AGAINST THE POTENTIAL LIGANDABLE SITES IN THE
           ACTIVE CHOLERA TOXIN
    • Abstract: Publication date: Available online 7 February 2015
      Source:Computational Biology and Chemistry
      Author(s): Aditi Gangopadhyay , Abhijit Datta
      The active cholera toxin responsible for the massive loss of water and ions in cholera patients via its ADP ribosylation activity is a heterodimer of the A1 subunit of the bacterial holotoxin and the human cytosolic ARF6 (ADP Ribosylation Factor 6). The active toxin is a potential target for the design of inhibitors against cholera. In this study we identified the potential ligandable sites of the active cholera toxin which can serve as binding sites for drug-like molecules. By employing an energy-based approach to identify ligand binding sites, and comparison with the results of computational solvent mapping, we identified two potential ligandable sites in the active toxin which can be targeted during structure-based drug design against cholera. Based on the probe affinities of the identified ligandable regions, docking-based virtual screening was employed to identify probable inhibitors against these sites. Several indole-based alkaloids and phosphates showed strong interactions to the important residues of the ligandable region at the A1 active site. On the other hand, 26 top scoring hits were identified against the ligandable region at the A1-ARF6 interface which showed strong hydrogen bonding interactions, including guanidines, phosphates, Leucopterin and Aristolochic acid VIa. This study has important implications in the application of hybrid structure-based and ligand-based methods against the identified ligandable sites using the identified inhibitors as reference ligands, for drug design against the active cholera toxin.
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      PubDate: 2015-02-13T09:48:36Z
       
  • Tumor stratification by a novel graph-regularized bi-clique finding
           algorithm
    • Abstract: Publication date: Available online 7 February 2015
      Source:Computational Biology and Chemistry
      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 personalized medicine. Recent advances in profiling a large number of molecular variables such as in The Cancer Genome Atlas (TCGA), have enabled researchers to use 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: 2015-02-13T09:48:36Z
       
  • A novel quantitative model of cell cycle progression based on
           cyclin-dependent kinases activity and population balances
    • Abstract: Publication date: April 2015
      Source:Computational Biology and Chemistry, Volume 55
      Author(s): Massimo Pisu , Alessandro Concas , Giacomo Cao
      Cell cycle regulates proliferative cell capacity under normal or pathologic conditions, and in general it governs all in vivo/in vitro cell growth and proliferation processes. Mathematical simulation by means of reliable and predictive models represents an important tool to interpret experiment results, to facilitate the definition of the optimal operating conditions for in vitro cultivation, or to predict the effect of a specific drug in normal/pathologic mammalian cells. Along these lines, a novel model of cell cycle progression is proposed in this work. Specifically, it is based on a population balance (PB) approach that allows one to quantitatively describe cell cycle progression through the different phases experienced by each cell of the entire population during its own life. The transition between two consecutive cell cycle phases is simulated by taking advantage of the biochemical kinetic model developed by Gérard and Goldbeter (2009) which involves cyclin-dependent kinases (CDKs) whose regulation is achieved through a variety of mechanisms that include association with cyclins and protein inhibitors, phosphorylation–dephosphorylation, and cyclin synthesis or degradation. This biochemical model properly describes the entire cell cycle of mammalian cells by maintaining a sufficient level of detail useful to identify check point for transition and to estimate phase duration required by PB. Specific examples are discussed to illustrate the ability of the proposed model to simulate the effect of drugs for in vitro trials of interest in oncology, regenerative medicine and tissue engineering.
      Graphical abstract image

      PubDate: 2015-02-13T09:48:36Z
       
  • Investigating long range correlation in DNA sequences using significance
           tests of conditional mutual information
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part A
      Author(s): Maria Papapetrou , Dimitris Kugiumtzis
      This study exploits the use of Markov chain order estimation from symbol sequences of systems exhibiting long memory or long range correlations (LRC), such as DNA sequences. In the presence of limited sequence length, LRC chain can be approximated by a high order Markov chain. For the order estimation, the parametric significance test of conditional mutual information I C (m) is applied, found in an earlier work to be suitable for high order estimation. Here, it is computationally optimized applying an iterative algorithm for calculating I C (m) at increasing order m, enabling the analysis of long symbol sequences of high Markov chain order or LRC. The simulation study shows that when the true order is reasonably small, the estimated order saturates at the true order with the increase of the symbol sequence length, while when the true order is very large or the chain has LRC, the estimated order increases logarithmically with the symbol sequence length. The order estimation shows a different dependence on the DNA sequence length for bacteria, the plant Arabidopsis thaliana and the human chromosome, indicating a different long memory structure in their DNA.


      PubDate: 2015-02-13T09:48:36Z
       
  • IFC Editorial Board
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part A




      PubDate: 2015-02-13T09:48:36Z
       
  • IFC Editorial Board
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part B




      PubDate: 2015-02-13T09:48:36Z
       
  • Title page
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part B




      PubDate: 2015-02-13T09:48:36Z
       
  • Inter-domain linker prediction using amino acid compositional index
    • Abstract: Publication date: April 2015
      Source:Computational Biology and Chemistry, Volume 55
      Author(s): Maad Shatnawi , Nazar Zaki
      Protein chains are generally long and consist of multiple domains. Domains are distinct structural units of a protein that can evolve and function independently. The accurate and reliable prediction of protein domain linkers and boundaries is often considered to be the initial step of protein tertiary structure and function predictions. In this paper, we introduce CISA as a method for predicting inter-domain linker regions solely from the amino acid sequence information. The method first computes the amino acid compositional index from the protein sequence dataset of domain-linker segments and the amino acid composition. A preference profile is then generated by calculating the average compositional index values along the amino acid sequence using a sliding window. Finally, the protein sequence is segmented into intervals and a simulated annealing algorithm is employed to enhance the prediction by finding the optimal threshold value for each segment that separates domains from inter-domain linkers. The method was tested on two standard protein datasets and showed considerable improvement over the state-of-the-art domain linker prediction methods.
      Graphical abstract image Highlights

      PubDate: 2015-02-13T09:48:36Z
       
  • Structure-based grafting and identification of kinase–inhibitors to
           target mTOR signaling pathway as potential therapeutics for glioblastoma
    • Abstract: Publication date: February 2015
      Source:Computational Biology and Chemistry, Volume 54
      Author(s): Yu-Hui Cui , Jiong Chen , Tao Xu , Heng-Li Tian
      Mammalian target of rapamycin (mTOR), a key mediator of PI3K/Akt/mTOR signaling pathway, has recently emerged as a compelling molecular target in glioblastoma. The mTOR is a member of serine/threonine protein kinase family that functions as a central controller of growth, proliferation, metabolism and angiogenesis, but its signaling is dysregulated in various human diseases especially in certain solid tumors including the glioblastoma. Here, considering that there are various kinase inhibitors being approved or under clinical or preclinical development, it is expected that some of them can be re-exploited as new potent agents to target mTOR for glioblastoma therapy. To achieve this, a synthetic pipeline that integrated molecular grafting, consensus scoring, virtual screening, kinase assay and structure analysis was described to systematically profile the binding potency of various small-molecule inhibitors deposited in the protein kinase–inhibitor database against the kinase domain of mTOR. Consequently, a number of structurally diverse compounds were successfully identified to exhibit satisfactory inhibition profile against mTOR with IC50 values at nanomolar level. In particular, few sophisticated kinase–inhibitors as well as a flavonoid myricetin showed high inhibitory activities, which could thus be considered as potential lead compounds to develop new potent, selective mTOR–inhibitors. Structural examination revealed diverse nonbonded interactions such as hydrogen bonds, hydrophobic forces and van der Waals contacts across the complex interface of mTOR with myricetin, conferring both stability and specificity for the mTOR–inhibitor binding.
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      PubDate: 2015-02-13T09:48:36Z
       
  • Entropy and long-range correlations in DNA sequences
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part A
      Author(s): S.S. Melnik , O.V. Usatenko
      We analyze the structure of DNA molecules of different organisms by using the additive Markov chain approach. Transforming nucleotide sequences into binary strings, we perform statistical analysis of the corresponding “texts”. We develop the theory of N-step additive binary stationary ergodic Markov chains and analyze their differential entropy. Supposing that the correlations are weak we express the conditional probability function of the chain by means of the pair correlation function and represent the entropy as a functional of the pair correlator. Since the model uses two point correlators instead of probability of block occurring, it makes possible to calculate the entropy of subsequences at much longer distances than with the use of the standard methods. We utilize the obtained analytical result for numerical evaluation of the entropy of coarse-grained DNA texts. We believe that the entropy study can be used for biological classification of living species.


      PubDate: 2015-02-13T09:48:36Z
       
  • Molecular dynamics study-based mechanism of nefiracetam-induced NMDA
           receptor potentiation
    • Abstract: Publication date: April 2015
      Source:Computational Biology and Chemistry, Volume 55
      Author(s): Olaposi I. Omotuyi , Hiroshi Ueda
      Plastic changes in the brain required for memory formation and long-term learning are dependent on N-methyl-d-aspartic acid (NMDA) receptor signaling. Nefiracetam reportedly boosts NMDA receptor functions as a basis for its nootropic properties. Previous studies suggest that nefiracetam potentiates the NMDA receptor activation, as a more potent co-agonist for glycine binding site than glycine, though the underlying mechanisms remain elusive. Here, using BSP-SLIM method, a novel binding site within the core of spiral β-strands-1-5 of LBD-GLUN1 has been predicted in glycine-bound GLUN1 conformation in addition to the glycine pocket in Apo-GLUN1. Within the core of spiral β-strands-1-5 of LBD-GLUN1 pocket, all-atom molecular dynamics simulation revealed that nefiracetam disrupts Arg523-glycine-Asp732 interaction resulting in open GLUN1 conformation and ultimate diffusion of glycine out of the clamshell cleft. Open GLUN1 conformation coerces other intra-chain domains and proximal inter-chain domains to sample inactivate conformations resulting in closure of the transmembrane gate via a novel gauche trap on threonine 647 (chi-1 dihedral (χ 1)=−45° instead of +45°). Docking of nefiracetam into the glycine pocket reversed the gauche trap and meditates partial opening of the TMD gate within a time-scale of 100ns as observed in glycine-only state. All these results suggest that nefiracetam can favorably complete with glycine for GLUN1-LBD in a two-step process, first by binding to a novel site of GLUN1-LBD-NMDA receptor followed by disruption of glycine-binding dynamics then replacing glycine in the GLUN1-LBD cleft.
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      PubDate: 2015-02-13T09:48:36Z
       
  • An improved poly(A) motifs recognition method based on decision level
           fusion
    • Abstract: Publication date: February 2015
      Source:Computational Biology and Chemistry, Volume 54
      Author(s): Shanxin Zhang , Jiuqiang Han , Jun Liu , Jiguang Zheng , Ruiling Liu
      Polyadenylation is the process of addition of poly(A) tail to mRNA 3′ ends. Identification of motifs controlling polyadenylation plays an essential role in improving genome annotation accuracy and better understanding of the mechanisms governing gene regulation. The bioinformatics methods used for poly(A) motifs recognition have demonstrated that information extracted from sequences surrounding the candidate motifs can differentiate true motifs from the false ones greatly. However, these methods depend on either domain features or string kernels. To date, methods combining information from different sources have not been found yet. Here, we proposed an improved poly(A) motifs recognition method by combing different sources based on decision level fusion. First of all, two novel prediction methods was proposed based on support vector machine (SVM): one method is achieved by using the domain-specific features and principle component analysis (PCA) method to eliminate the redundancy (PCA–SVM); the other method is based on Oligo string kernel (Oligo-SVM). Then we proposed a novel machine-learning method for poly(A) motif prediction by marrying four poly(A) motifs recognition methods, including two state-of-the-art methods (Random Forest (RF) and HMM-SVM), and two novel proposed methods (PCA–SVM and Oligo-SVM). A decision level information fusion method was employed to combine the decision values of different classifiers by applying the DS evidence theory. We evaluated our method on a comprehensive poly(A) dataset that consists of 14,740 samples on 12 variants of poly(A) motifs and 2750 samples containing none of these motifs. Our method has achieved accuracy up to 86.13%. Compared with the four classifiers, our evidence theory based method reduces the average error rate by about 30%, 27%, 26% and 16%, respectively. The experimental results suggest that the proposed method is more effective for poly(A) motif recognition.
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      PubDate: 2015-02-13T09:48:36Z
       
  • Evaluation of the effect of c.2946+1G>T mutation on splicing in the
           SCN1A gene
    • Abstract: Publication date: February 2015
      Source:Computational Biology and Chemistry, Volume 54
      Author(s): Afif Ben Mahmoud , Riadh Ben Mansour , Fatma Driss , Siwar Baklouti-Gargouri , Olfa Siala , Emna Mkaouar-Rebai , Faiza Fakhfakh
      Mutations in the SCN1A gene have commonly been associated with a wide range of mild to severe epileptic syndromes. They generate a wide spectrum of phenotypes ranging from the relatively mild generalized epilepsy with febrile seizures plus (GEFS+) to other severe epileptic encephalopathies, including myoclonic epilepsy in infancy (SMEI), cryptogenic focal epilepsy (CFE), cryptogenic generalized epilepsy (CGE) and a distinctive subgroup termed as severe infantile multifocal epilepsy (SIMFE). The present study was undertaken to investigate the potential effects of a transition in the first nucleotide at the donor splice site of intron 15 of the SCN1A gene leading to CGES. Functional analyses using site-directed mutagenesis by PCR and subsequent ex-vivo splicing assays, revealed that the c.2946+1G>T mutation lead to a total skipping of exon 15. The exclusion of this exon did not alter the reading frame but induced the deletion of the amino acids (853 Leu −971 Val) which are a major part in the fourth, fifth and sixth transmembrane segments of the SCN1A protein. The theoretical implications of the splice site mutations predicted with the bioinformatic tool human splice finder were investigated and compared with the results obtained by the cellular assay.
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      PubDate: 2015-02-13T09:48:36Z
       
  • Identification of miR159s and their target genes and expression analysis
           under drought stress in potato
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part B
      Author(s): Jiangwei Yang , Ning Zhang , Xiaoxiao Mi , Liangliang Wu , Rui Ma , Xi Zhu , Lei Yao , Xin Jin , Huaijun Si , Di Wang
      The MYB proteins comprise one of the largest families of plant transcription factors (TFs) and many of MYB families, which play essential roles in plant growth, development and respond to environmental stresses, and have yet been identified in plant. Previous research has shown that miR159 family members repressed the conserved plant R2R3 MYB domain TFs in model plants. In the present research, we identified three potato novel miR159 family members named as stu-miR159a, stu-miR159b and stu-miR159c based on bioinformatics analysis. Target prediction showed that they have a bite sit on the three GAMyb-like genes (StGAMyb-like1, StGAMyb-like2.1 and StGAMyb-like2.2) of potato. Those GAMyb-like genes also have been selected and cloned from potato, which belong to R2R3 MYB domain TFs. We further measured expressional levels of stu-miR159s and potato GAMyb-like genes during the different periods of drought treated samples using quantitative real-time PCR (qRT-PCR). The results showed that they had a opposite expression pattern, briefly, three stu-miR159 members showed similar expressional trends which were significantly decreased expression after experiencing 25 days of drought stress treatment, while the potato GAMyb-like family members were greatly increased. Therefore, we suggested that stu-miR159s negatively regulated the expression of potato GAMyb-like genes which responsible for drought stress. The findings can facilitate functional studies of miRNAs in plants and provide molecular evidence for involvement process of drought tolerance in potato.
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      PubDate: 2015-02-13T09:48:36Z
       
  • Hierarchical closeness efficiently predicts disease genes in a directed
           signaling network
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part B
      Author(s): Tien-Dzung Tran , Yung-Keun Kwon
      Background Many structural centrality measures were proposed to predict putative disease genes on biological networks. Closeness is one of the best-known structural centrality measures, and its effectiveness for disease gene prediction on undirected biological networks has been frequently reported. However, it is not clear whether closeness is effective for disease gene prediction on directed biological networks such as signaling networks. Results In this paper, we first show that closeness does not significantly outperform other well-known centrality measures such as Degree, Betweenness, and PageRank for disease gene prediction on a human signaling network. In addition, we observed that prediction accuracy by the closeness measure was worse than that by a reachability measure, but closeness could efficiently predict disease genes among a set of genes with the same reachability value. Based on this observation, we devised a novel structural measure, hierarchical closeness, by combining reachability and closeness such that all genes are first ranked by the degree of reachability and then the tied genes are further ranked by closeness. We discovered that hierarchical closeness outperforms other structural centrality measures in disease gene prediction. We also found that the set of highly ranked genes in terms of hierarchical closeness is clearly different from that of hub genes with high connectivity. More interestingly, these findings were consistently reproduced in a random Boolean network model. Finally, we found that genes with relatively high hierarchical closeness are significantly likely to encode proteins in the extracellular matrix and receptor proteins in a human signaling network, supporting the fact that half of all modern medicinal drugs target receptor-encoding genes. Conclusion Taken together, hierarchical closeness proposed in this study is a novel structural measure to efficiently predict putative disease genes in a directed signaling network.
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      PubDate: 2015-02-13T09:48:36Z
       
  • Bacterial genomes lacking long-range correlations may not be modeled by
           low-order Markov chains: The role of mixing statistics and frame shift of
           neighboring genes
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part A
      Author(s): Germinal Cocho , Pedro Miramontes , Ricardo Mansilla , Wentian Li
      We examine the relationship between exponential correlation functions and Markov models in a bacterial genome in detail. Despite the well known fact that Markov models generate sequences with correlation function that decays exponentially, simply constructed Markov models based on nearest-neighbor dimer (first-order), trimer (second-order), up to hexamer (fifth-order), and treating the DNA sequence as being homogeneous all fail to predict the value of exponential decay rate. Even reading-frame-specific Markov models (both first- and fifth-order) could not explain the fact that the exponential decay is very slow. Starting with the in-phase coding-DNA-sequence (CDS), we investigated correlation within a fixed-codon-position subsequence, and in artificially constructed sequences by packing CDSs with out-of-phase spacers, as well as altering CDS length distribution by imposing an upper limit. From these targeted analyses, we conclude that the correlation in the bacterial genomic sequence is mainly due to a mixing of heterogeneous statistics at different codon positions, and the decay of correlation is due to the possible out-of-phase between neighboring CDSs. There are also small contributions to the correlation from bases at the same codon position, as well as by non-coding sequences. These show that the seemingly simple exponential correlation functions in bacterial genome hide a complexity in correlation structure which is not suitable for a modeling by Markov chain in a homogeneous sequence. Other results include: use of the (absolute value) second largest eigenvalue to represent the 16 correlation functions and the prediction of a 10–11 base periodicity from the hexamer frequencies.


      PubDate: 2015-02-13T09:48:36Z
       
  • Complexity measures for the evolutionary categorization of organisms
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part A
      Author(s): A. Provata , C. Nicolis , G. Nicolis
      Complexity measures are used to compare the genomic characteristics of five organisms belonging to distinct classes spanning the evolutionary tree: higher eukaryotes, amoebae, unicellular eukaryotes and bacteria. The comparisons are undertaken using the full four-letter alphabet and the coarse grained two-letter alphabets AG-CT and AT-CG. We show that the conditional probability matrix for the four-letter and AT-CG alphabet is markedly asymmetric in eukaryotes while it is nearly symmetric in bacterial genomes. Spatial asymmetry is revealed in the four-letter alphabet, signifying that the probability fluxes are nonvanishing and thus the reading sense of a sequence is irreversible for all organisms. Calculations of the block entropy and excess entropy demonstrate that the human genome accommodates better all possible block configurations, especially for long blocks. With respect to point-to-point details and to spatial arrangement of blocks the exit distance distributions from a particular letter demonstrate long distance characteristics in the eukaryotic sequences for all three alphabets, while the bacterial (prokaryotic) genomes deviate indicating short range characteristics. Overall, the conditional probability, the fluxes, the block entropy content and the exit distance distributions can be used as markers, discriminating between eukaryotic and prokaryotic DNA, allowing in many cases to discern details related to finer classes. In all cases the reduction from four letters to two masks some important statistical and spatial properties, with the AT-CG alphabet having higher ability of discrimination than the AG-CT one. In particular, the AT-CG alphabet reduction accentuates the CpG related properties (conditional probabilities w 32 , long ranged exit distance distribution for A and T nucleotides), but masks sequence asymmetry and irreversibility in all examined organisms.


      PubDate: 2015-02-13T09:48:36Z
       
  • Molecular simulation investigation on the interaction between
           barrier-to-autointegration factor dimer or its Gly25Glu mutant and LEM
           domain of emerin
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part B
      Author(s): Yu-Dong Shang , Ji-Long Zhang , Yan Wang , Hong-Xing Zhang , Qing-Chuan Zheng
      The interaction between barrier-to-autointegration factor dimer (BAF2) and LEM domain of emerin (EmLEM) was studied by molecular simulation methods. Nonspecific fragment of double-strand DNA molecule was docked with each chain of BAF2 by ZDOCK program. The model of DNA2:BAF2:EmLEM was thus constructed. The mutant Gly25Glu of BAF2 was manually constructed to explore the detailed effect of the mutation on the binding of BAF2 and EmLEM. It has been experimentally suggested that point mutation Gly25Glu can disturb the binding between BAF2 and EmLEM. Then, molecular dynamics (MD) simulations were performed on DNA2:BAF2(WT):EmLEM and DNA2:BAF2(MT):EmLEM complexes. 30ns trajectories revealed that the trajectory fluctuations of MT complex are more violent than that of the WT complex. Further, the binding free energy analysis showed that the electronegative residues Asp57, Glu61 and Asp65 from chain A, glu36 from chain B of BAF2 mainly contribute to interact with EmLEM. Besides, a stable π–π stack between trp62 and phe39 from BAF2(WT) chain B is destroyed by Glu25 in BAF2(MT). As a result, trp62 forms an interaction with glu25, and phe39 converts to strengthen affinity to EmLEM. On the other hand, Trp62 from chain A also forms a strong interaction with MT Glu25. Thus, with the docking of DNA, BAF2(MT) has higher affinity with EmLEM than BAF2(WT).
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      PubDate: 2015-02-13T09:48:36Z
       
  • Title page
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part A




      PubDate: 2015-02-13T09:48:36Z
       
  • Editorial: Complexity in genomes
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part A
      Author(s): Yannis Almirantis , Peter Arndt , Wentian Li , Astero Provata



      PubDate: 2015-02-13T09:48:36Z
       
  • Identification of gene knockout strategies using a hybrid of an ant colony
           optimization algorithm and flux balance analysis to optimize microbial
           strains
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part B
      Author(s): Shi Jing Lu , Abdul Hakim Mohamed Salleh , Mohd Saberi Mohamad , Safaai Deris , Sigeru Omatu , Michifumi Yoshioka
      Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker’s yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms.
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      PubDate: 2015-02-13T09:48:36Z
       
  • Comparative analysis of periodicity search methods in DNA sequences
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part A
      Author(s): Yulia M. Suvorova , Maria A. Korotkova , Eugene V. Korotkov
      To determine the periodicity of a DNA sequence, different spectral approaches are applied (discrete Fourier transform (DFT), autocorrelation (CORR), information decomposition (ID), hybrid method (HYB), concept of spectral envelope for spectral analysis (SE), normalized autocorrelation (CORR_N) and profile analysis (PA). In this work, we investigated the possibility of finding the true period length, by depending on the average number of accumulated changes in DNA bases (PM) for the methods stated above. The results show that for periods with short length (≤4 b.p), it is possible to use the hybrid method (HYB), which combines properties of autocorrelation, Fourier transform, and information decomposition (ID). For larger period lengths (>4) with values of point mutation (PM) equal to 1.0 or more per one nucleotide, it is preferable to use information of decomposition method (ID), as the other spectral approaches cannot achieve correct determination of the period length present in the analyzed sequence.


      PubDate: 2015-02-13T09:48:36Z
       
  • Disruption of murine Tcte3-3 induces tissue specific apoptosis via
           co-expression of Anxa5 and Pebp1
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part B
      Author(s): Zahida Parveen , Zohra Bibi , Nousheen Bibi , Juergen Neesen , Sajid Rashid
      Programmed cell death or apoptosis plays a vital physiological role in the development and homeostasis. Any discrepancy in apoptosis may trigger testicular and neurodegenerative diseases, ischemic damage, autoimmune disorders and many types of cancer. Tcte3 (T-complex testis expressed 3) is an accessory component of axonemal and cytoplasmic dynein which expresses predominantly in meiotic and postmeiotic germ cells. It plays an essential role during spermatogenesis; however, to explore its diverse and complex functioning in male germ cell apoptosis, requires further prosecution. Here, 2D-gel electrophoresis, mass spectrometry and qRT–PCR analyses were performed to elucidate the differential expression of genes, in both wild-type and homozygous Tcte3-3 mice. We observed an increased expression of Tcte3 in homozygotes as compared to wild-type testes. Perpetually, an increased expression of Anxa5 and Pebp1, while a lower expression of Rsph1 was detected in Tcte3-3 −/− mice. We propose that over-expression of Pebp1 and Anxa5 in Tcte3-3 −/− testes might be due to increased apoptosis. To evaluate this possibility, testes specific microarray data set extracted from NCBI gene ontology omnibus (GEO) was used to cluster the possible co-expression partners of Tcte3. Further functional coherence of compiled candidate genes was monitored computationally by studying the common TFBS overlapped at the regulatory regions. Differential expression of Tcte3-3 and its involvement in apoptosis may provide a basis for the investigation of transcriptional specificities of other Tcte3 paralogs (Tcte3-1 and Tcte3-2). A complete understanding of controlling factors which have implications in regulating tissue-specific Tcte3 expression would provide additional insights into the gene control events. The collective knowledge may prove useful for the development of novel therapeutic regimen and would open new avenues in defining selective roles of Tcte3 in germ cell development.
      Graphical abstract image

      PubDate: 2015-02-13T09:48:36Z
       
  • Analysis of correlation structures in the Synechocystis PCC6803 genome
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part A
      Author(s): Zuo-Bing Wu
      Transfer of nucleotide strings in the Synechocystis sp. PCC6803 genome is investigated to exhibit periodic and non-periodic correlation structures by using the recurrence plot method and the phase space reconstruction technique. The periodic correlation structures are generated by periodic transfer of several substrings in long periodic or non-periodic nucleotide strings embedded in the coding regions of genes. The non-periodic correlation structures are generated by non-periodic transfer of several substrings covering or overlapping with the coding regions of genes. In the periodic and non-periodic transfer, some gaps divide the long nucleotide strings into the substrings and prevent their global transfer. Most of the gaps are either the replacement of one base or the insertion/reduction of one base. In the reconstructed phase space, the points generated from two or three steps for the continuous iterative transfer via the second maximal distance can be fitted by two lines. It partly reveals an intrinsic dynamics in the transfer of nucleotide strings. Due to the comparison of the relative positions and lengths, the substrings concerned with the non-periodic correlation structures are almost identical to the mobile elements annotated in the genome. The mobile elements are thus endowed with the basic results on the correlation structures.


      PubDate: 2015-02-13T09:48:36Z
       
  • How do the protonation states of E296 and D312 in OmpF and D299 and D315
           in homologous OmpC affect protein structure and dynamics' Simulation
           studies
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part B
      Author(s): Prapasiri Pongprayoon
      In this study, the structural and dynamic properties of two major porins (OmpF and OmpC) in Escherichia coli are investigated using molecular dynamics (MD) simulations. Both porins have the extracellular loop L3 folded halfway through the pore to form a constriction area. The solute influx and efflux are controlled by the L3 movement. E296 and D312 in OmpF and homologous D299 and D315 in OmpC located on the barrel wall are found to play a key role in L3 gating activity. All possible charged states of both E296(D299) and D312(315) are applied in this study to observe changes in overall structure and especially L3 movement. The results show that different protonation states of both residues cause the large-scale deviations in structure and pore cavity especially in OmpF. Fully charged E296(D299) and D312(315) increase the protein flexibility significantly. Deprotonating at least one of E296(D299) and D312(315) helps to fasten L3 to the barrel wall and maintain pore size. Lacking of interactions with D312(315) can lead to the pore closure in OmpF. Comparing with OmpC, not only is OmpF less stable, but it is also more sensitive to the charge states of both E296(D299) and D312(315).
      Graphical abstract image

      PubDate: 2015-02-13T09:48:36Z
       
  • Computational analysis of 3′UTR region of CASP3 with respect to
           miRSNPs and SNPs in targetting miRNAs
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part B
      Author(s): Sercan Ergun , Serdar Oztuzcu
      Apoptosis is a strictly organized course which keeps the healthy survival/death equilibrium. Disregulation in apoptosis may lead autoimmunity or cancer, but increased apoptosis can lead degenerative diseases. Studies during the last several years have identified numerous affected miRNAs in association with apoptosis, their target genes and biological functions, and possible drug interventions. Polymorphisms in miRNA genes or miRNA target sites (miRSNPs) can modify miRNA action. While polymorphisms in miRNA genes are relatively rare, SNPs in miRNA-binding sites in target genes are more frequent. Several studies have shown that SNPs in miRNA target sites enhance or weaken the interaction between miRNA and its target transcripts and are associated with cancers and other diseases. We aimed to identify miRSNPs on executioner caspase, CASP3 gene (caspase-3) and SNPs in miRNA genes targeting 3′UTR of CASP3 and assessing the impact of these miRSNPs and SNPs of miRNA genes targeting 3′UTR of CASP3 with respect to apoptosis. We identified 89 different miRNA binding sites (for 43 different miRNAs) and 16 different SNPs in binding sites of miRNA in the 3′UTR of the CASP3 gene. Also, 2 SNPs (rs372435266 and rs190144655) were found on this miRNA′ genomic sequence. One of them crossmatched with a SNP in the 3′UTR of CASP3 that we found formerly. This miRNA was miR-4802-3p. Besides, miR-4802-3p targets three other apoptosis related genes, XIAP, IL1A and SOX2. This means that miR-4802-3p may also have a critical effect on apoptosis via different pathways other than caspase-3. We can therefore conclude that this is the first study proving a strong association between miR-4802-3p and apoptosis upon computational targetting analysis.
      Graphical abstract image

      PubDate: 2015-02-13T09:48:36Z
       
  • DNA clustering and genome complexity
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part A
      Author(s): Francisco Dios , Guillermo Barturen , Ricardo Lebrón , Antonio Rueda , Michael Hackenberg , José L. Oliver
      Early global measures of genome complexity (power spectra, the analysis of fluctuations in DNA walks or compositional segmentation) uncovered a high degree of complexity in eukaryotic genome sequences. The main evolutionary mechanisms leading to increases in genome complexity (i.e. gene duplication and transposon proliferation) can all potentially produce increases in DNA clustering. To quantify such clustering and provide a genome-wide description of the formed clusters, we developed GenomeCluster, an algorithm able to detect clusters of whatever genome element identified by chromosome coordinates. We obtained a detailed description of clusters for ten categories of human genome elements, including functional (genes, exons, introns), regulatory (CpG islands, TFBSs, enhancers), variant (SNPs) and repeat (Alus, LINE1) elements, as well as DNase hypersensitivity sites. For each category, we located their clusters in the human genome, then quantifying cluster length and composition, and estimated the clustering level as the proportion of clustered genome elements. In average, we found a 27% of elements in clusters, although a considerable variation occurs among different categories. Genes form the lowest number of clusters, but these are the longest ones, both in bp and the average number of components, while the shortest clusters are formed by SNPs. Functional and regulatory elements (genes, CpG islands, TFBSs, enhancers) show the highest clustering level, as compared to DNase sites, repeats (Alus, LINE1) or SNPs. Many of the genome elements we analyzed are known to be composed of clusters of low-level entities. In addition, we found here that the clusters generated by GenomeCluster can be in turn clustered into high-level super-clusters. The observation of ‘clusters-within-clusters’ parallels the ‘domains within domains’ phenomenon previously detected through global statistical methods in eukaryotic sequences, and reveals a complex human genome landscape dominated by hierarchical clustering.


      PubDate: 2015-02-13T09:48:36Z
       
  • Metabolic network motifs can provide novel insights into evolution: The
           evolutionary origin of Eukaryotic organelles as a case study
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part B
      Author(s): Erin R. Shellman , Yu Chen , Xiaoxia Lin , Charles F. Burant , Santiago Schnell
      Phylogenetic trees are typically constructed using genetic and genomic data, and provide robust evolutionary relationships of species from the genomic point of view. We present an application of network motif mining and analysis of metabolic pathways that when used in combination with phylogenetic trees can provide a more complete picture of evolution. By using distributions of three-node motifs as a proxy for metabolic similarity, we analyze the ancestral origin of Eukaryotic organelles from the metabolic point of view to illustrate the application of our motif mining and analysis network approach. Our analysis suggests that the hypothesis of an early proto-Eukaryote could be valid. It also suggests that a δ- or ϵ-Proteobacteria may have been the endosymbiotic partner that gave rise to modern mitochondria. Our evolutionary analysis needs to be extended by building metabolic network reconstructions of species from the phylum Crenarchaeota, which is considered to be a possible archaeal ancestor of the eukaryotic cell. In this paper, we also propose a methodology for constructing phylogenetic trees that incorporates metabolic network signatures to identify regions of genomically-estimated phylogenies that may be spurious. We find that results generated from our approach are consistent with a parallel phylogenetic analysis using the method of feature frequency profiles.
      Graphical abstract image

      PubDate: 2015-02-13T09:48:36Z
       
  • Evidence of a cancer type-specific distribution for consecutive somatic
           mutation distances
    • Abstract: Publication date: December 2014
      Source:Computational Biology and Chemistry, Volume 53, Part A
      Author(s): Jose M. Muiño , Ercan E. Kuruoğlu , Peter F. Arndt
      Specific molecular mechanisms may affect the pattern of mutation in particular regions, and therefore leaving a footprint or signature in the DNA of their activity. The common approach to identify these signatures is studying the frequency of substitutions. However, such an analysis ignores the important spatial information, which is important with regards to the mutation occurrence statistics. In this work, we propose that the study of the distribution of distances between consecutive mutations along the DNA molecule can provide information about the types of somatic mutational processes. In particular, we have found that specific cancer types show a power-law in interoccurrence distances, instead of the expected exponential distribution dictated with the Poisson assumption commonly made in the literature. Cancer genomes exhibiting power-law interoccurrence distances were enriched in cancer types where the main mutational process is described to be the activity of the APOBEC protein family, which produces a particular pattern of mutations called Kataegis. Therefore, the observation of a power-law in interoccurence distances could be used to identify cancer genomes with Kataegis.


      PubDate: 2015-02-13T09:48:36Z
       
  • Human-chimpanzee alignment: Ortholog Exponentials and Paralog Power Laws
    • Abstract: Publication date: Available online 2 October 2014
      Source:Computational Biology and Chemistry
      Author(s): Kun Gao , Jonathan Miller
      Genomic subsequences conserved between closely related species such as human and chimpanzee exhibit an exponential length distribution, in contrast to the algebraic length distribution observed for sequences shared between distantly related genomes. We find that the former exponential can be further decomposed into an exponential component primarily composed of orthologous sequences, and a truncated algebraic component primarily composed of paralogous sequences.


      PubDate: 2014-10-06T23:45:30Z
       
 
 
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