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CHEMICAL ENGINEERING (188 journals)                     

Showing 1 - 0 of 0 Journals sorted alphabetically
AATCC Journal of Research     Full-text available via subscription   (Followers: 3)
ACS Sustainable Chemistry & Engineering     Hybrid Journal  
Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials     Hybrid Journal   (Followers: 4)
Acta Polymerica     Hybrid Journal   (Followers: 7)
Additives for Polymers     Full-text available via subscription   (Followers: 20)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 5)
Advanced Chemical Engineering Research     Open Access   (Followers: 12)
Advanced Powder Technology     Hybrid Journal   (Followers: 12)
Advances in Applied Ceramics     Hybrid Journal   (Followers: 4)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 20)
Advances in Chemical Engineering and Science     Open Access   (Followers: 31)
Advances in Polymer Technology     Hybrid Journal   (Followers: 12)
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: 8)
Anti-Corrosion Methods and Materials     Hybrid Journal   (Followers: 5)
Applied Petrochemical Research     Open Access   (Followers: 2)
Asia-Pacific Journal of Chemical Engineering     Hybrid Journal   (Followers: 7)
Biochemical Engineering Journal     Hybrid Journal   (Followers: 12)
Biofuel Research Journal     Open Access   (Followers: 3)
Biomass Conversion and Biorefinery     Partially Free   (Followers: 8)
Brazilian Journal of Chemical Engineering     Open Access   (Followers: 3)
Bulletin of Chemical Reaction Engineering & Catalysis     Open Access  
Bulletin of the Chemical Society of Ethiopia     Open Access   (Followers: 3)
Carbohydrate Polymers     Hybrid Journal   (Followers: 7)
Catalysts     Open Access   (Followers: 5)
ChemBioEng Reviews     Full-text available via subscription  
Chemical and Engineering News     Free   (Followers: 10)
Chemical and Materials Engineering     Open Access   (Followers: 3)
Chemical and Petroleum Engineering     Hybrid Journal   (Followers: 9)
Chemical and Process Engineering     Open Access   (Followers: 5)
Chemical and Process Engineering Research     Open Access   (Followers: 7)
Chemical Communications     Full-text available via subscription   (Followers: 62)
Chemical Engineering & Technology     Hybrid Journal   (Followers: 30)
Chemical Engineering and Processing: Process Intensification     Hybrid Journal   (Followers: 14)
Chemical Engineering and Science     Open Access   (Followers: 5)
Chemical Engineering Communications     Hybrid Journal   (Followers: 11)
Chemical Engineering Journal     Hybrid Journal   (Followers: 21)
Chemical Engineering Research and Design     Hybrid Journal   (Followers: 19)
Chemical Engineering Research Bulletin     Open Access   (Followers: 2)
Chemical Engineering Science     Hybrid Journal   (Followers: 19)
Chemical Geology     Hybrid Journal   (Followers: 12)
Chemical Papers     Hybrid Journal   (Followers: 2)
Chemical Product and Process Modeling     Hybrid Journal   (Followers: 3)
Chemical Reviews     Full-text available via subscription   (Followers: 123)
Chemical Society Reviews     Full-text available via subscription   (Followers: 36)
Chemical Technology     Open Access   (Followers: 5)
ChemInform     Hybrid Journal   (Followers: 4)
Chemistry & Industry     Hybrid Journal   (Followers: 2)
Chemistry Central Journal     Open Access   (Followers: 5)
Chemistry of Materials     Full-text available via subscription   (Followers: 135)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 14)
ChemSusChem     Hybrid Journal   (Followers: 6)
Chinese Chemical Letters     Full-text available via subscription   (Followers: 3)
Chinese Journal of Chemical Engineering     Full-text available via subscription   (Followers: 3)
Chinese Journal of Chemical Physics     Hybrid Journal   (Followers: 1)
Coke and Chemistry     Hybrid Journal   (Followers: 1)
Coloration Technology     Hybrid Journal  
Computational Biology and Chemistry     Hybrid Journal   (Followers: 10)
Computer Aided Chemical Engineering     Full-text available via subscription   (Followers: 1)
Computers & Chemical Engineering     Hybrid Journal   (Followers: 9)
CORROSION     Full-text available via subscription   (Followers: 19)
Corrosion Engineering, Science and Technology     Hybrid Journal   (Followers: 35)
Corrosion Reviews     Hybrid Journal   (Followers: 3)
Crystal Research and Technology     Hybrid Journal   (Followers: 5)
Current Opinion in Chemical Engineering     Open Access   (Followers: 4)
Education for Chemical Engineers     Hybrid Journal   (Followers: 4)
Eksergi     Open Access  
Emerging Trends in Chemical Engineering     Full-text available via subscription  
European Polymer Journal     Hybrid Journal   (Followers: 40)
Fibers and Polymers     Full-text available via subscription   (Followers: 3)
Fluorescent Materials     Open Access   (Followers: 1)
Focusing on Modern Food Industry     Open Access   (Followers: 2)
Frontiers of Chemical Science and Engineering     Hybrid Journal   (Followers: 1)
Gels     Open Access  
Geochemistry International     Hybrid Journal   (Followers: 1)
Handbook of Powder Technology     Full-text available via subscription   (Followers: 3)
Heat Exchangers     Open Access   (Followers: 1)
High Performance Polymers     Hybrid Journal  
Hungarian Journal of Industry and Chemistry     Open Access  
Indian Chemical Engineer     Hybrid Journal   (Followers: 4)
Indian Journal of Chemical Technology (IJCT)     Open Access   (Followers: 9)
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: 3)
Info Chimie Magazine     Full-text available via subscription   (Followers: 2)
International Journal of Chemical and Petroleum Sciences     Open Access   (Followers: 2)
International Journal of Chemical Engineering     Open Access   (Followers: 6)
International Journal of Chemical Reactor Engineering     Hybrid Journal   (Followers: 2)
International Journal of Chemical Technology     Open Access   (Followers: 5)
International Journal of Chemoinformatics and Chemical Engineering     Full-text available via subscription   (Followers: 2)
International Journal of Food Science     Open Access   (Followers: 2)
International Journal of Industrial Chemistry     Open Access  
International Journal of Polymeric Materials     Hybrid Journal   (Followers: 5)
International Journal of Science and Engineering     Open Access   (Followers: 4)
International Journal of Waste Resources     Open Access   (Followers: 3)
Journal of Chemical Engineering & Process Technology     Open Access   (Followers: 4)
Journal of Applied Crystallography     Hybrid Journal   (Followers: 5)
Journal of Applied Electrochemistry     Hybrid Journal   (Followers: 10)
Journal of Applied Polymer Science     Hybrid Journal   (Followers: 101)
Journal of Biomaterials Science, Polymer Edition     Hybrid Journal   (Followers: 9)
Journal of Bioprocess Engineering and Biorefinery     Full-text available via subscription  
Journal of Chemical & Engineering Data     Full-text available via subscription   (Followers: 10)
Journal of Chemical and Biological Interfaces     Full-text available via subscription   (Followers: 1)
Journal of Chemical Ecology     Hybrid Journal   (Followers: 5)
Journal of Chemical Engineering     Open Access   (Followers: 6)
Journal of Chemical Engineering and Materials Science     Open Access   (Followers: 1)
Journal of Chemical Science and Technology     Open Access   (Followers: 4)
Journal of Chemical Sciences     Partially Free   (Followers: 17)
Journal of Chemical Technology & Biotechnology     Hybrid Journal   (Followers: 10)
Journal of Chemical Theory and Computation     Full-text available via subscription   (Followers: 13)
Journal of CO2 Utilization     Hybrid Journal   (Followers: 1)
Journal of Coatings     Open Access   (Followers: 4)
Journal of Crystallization Process and Technology     Open Access   (Followers: 5)
Journal of Environmental Chemical Engineering     Hybrid Journal   (Followers: 1)
Journal of Food Measurement and Characterization     Hybrid Journal  
Journal of Food Processing & Technology     Open Access  
Journal of Fuel Chemistry and Technology     Full-text available via subscription   (Followers: 4)
Journal of Fuels     Open Access  
Journal of Geochemical Exploration     Hybrid Journal  
Journal of Industrial and Engineering Chemistry     Hybrid Journal   (Followers: 1)
Journal of Information Display     Hybrid Journal  
Journal of Inorganic and Organometallic Polymers and Materials     Partially Free   (Followers: 6)
Journal of Modern Chemistry & Chemical Technology     Full-text available via subscription   (Followers: 2)
Journal of Molecular Catalysis A: Chemical     Hybrid Journal   (Followers: 5)
Journal of Non-Crystalline Solids     Hybrid Journal   (Followers: 7)
Journal of Ocean University of China (English Edition)     Hybrid Journal  
Journal of Organic Semiconductors     Open Access   (Followers: 4)
Journal of Physics and Chemistry of Solids     Hybrid Journal   (Followers: 5)
Journal of Polymer and Biopolymer Physics Chemistry     Open Access   (Followers: 4)
Journal of Polymer Engineering     Hybrid Journal   (Followers: 8)
Journal of Polymer Research     Hybrid Journal   (Followers: 6)
Journal of Polymer Science Part C : Polymer Letters     Hybrid Journal   (Followers: 5)
Journal of Polymers     Open Access   (Followers: 2)
Journal of Polymers and the Environment     Hybrid Journal   (Followers: 1)
Journal of Powder Technology     Open Access   (Followers: 1)
Journal of Pure and Applied Chemistry Research     Open Access   (Followers: 1)
Journal of the American Chemical Society     Full-text available via subscription   (Followers: 220)
Journal of the Bangladesh Chemical Society     Open Access  
Journal of the Brazilian Chemical Society     Open Access   (Followers: 2)
Journal of The Institution of Engineers (India) : Series E     Hybrid Journal   (Followers: 1)
Journal of the Pakistan Institute of Chemical Engineers     Open Access   (Followers: 1)
Journal of the Taiwan Institute of Chemical Engineers     Hybrid Journal   (Followers: 2)
Journal of Water Chemistry and Technology     Hybrid Journal   (Followers: 6)
Jurnal Inovasi Pendidikan Kimia     Open Access  
Jurnal Reaktor     Open Access  
Jurnal Teknologi Dan Industri Pangan     Open Access   (Followers: 1)
Korean Journal of Chemical Engineering     Hybrid Journal   (Followers: 3)
Main Group Metal Chemistry     Hybrid Journal   (Followers: 1)
Materials Chemistry and Physics     Full-text available via subscription   (Followers: 15)
Materials Sciences and Applied Chemistry     Full-text available via subscription  
Modern Chemistry & Applications     Open Access  
Molecular Imprinting     Open Access  
MRS Communications     Hybrid Journal  
Nanocontainers     Open Access  
Nanofabrication     Open Access  
Noise Control Engineering Journal     Full-text available via subscription   (Followers: 2)
Ochrona Srodowiska i Zasobów Naturalnych : Environmental Protection and Natural Resources     Open Access  
Petroleum Chemistry     Hybrid Journal   (Followers: 1)
Physics and Chemistry of Glasses - European Journal of Glass Science and Technology Part B     Full-text available via subscription   (Followers: 3)
Plasma Processes and Polymers     Hybrid Journal  
Plasmas and Polymers     Hybrid Journal  
Polymer     Hybrid Journal   (Followers: 82)
Polymer Bulletin     Hybrid Journal   (Followers: 7)
Polymer Composites     Hybrid Journal   (Followers: 13)
Powder Technology     Hybrid Journal   (Followers: 12)
Recyclable Catalysis     Open Access   (Followers: 1)
Research on Chemical Intermediates     Hybrid Journal  
Reviews in Chemical Engineering     Hybrid Journal   (Followers: 5)
Revista ION     Open Access  
Revista Mexicana de Ingeniería Química     Open Access  
Rubber Chemistry and Technology     Full-text available via subscription   (Followers: 2)
Russian Chemical Bulletin     Hybrid Journal   (Followers: 2)
Russian Journal of Applied Chemistry     Hybrid Journal   (Followers: 1)
Science and Engineering of Composite Materials     Hybrid Journal   (Followers: 54)
Solid Fuel Chemistry     Hybrid Journal  
South African Journal of Chemical Engineering     Full-text available via subscription   (Followers: 2)
South African Journal of Chemistry     Full-text available via subscription   (Followers: 2)
Surface Engineering and Applied Electrochemistry     Hybrid Journal   (Followers: 6)
Sustainable Chemical Processes     Open Access   (Followers: 1)
Synthesis Lectures on Chemical Engineering and Biochemical Engineering     Full-text available via subscription  
The Canadian Journal of Chemical Engineering     Hybrid Journal   (Followers: 3)
The Chemical Record     Hybrid Journal   (Followers: 1)
Theoretical Foundations of Chemical Engineering     Hybrid Journal   (Followers: 2)
Transition Metal Chemistry     Hybrid Journal   (Followers: 2)
Transylvanian Review of Systematical and Ecological Research     Open Access  
Visegrad Journal on Bioeconomy and Sustainable Development     Open Access   (Followers: 1)
Zeitschrift für Naturforschung B : A Journal of Chemical Sciences     Open Access   (Followers: 1)


Journal Cover Computational Biology and Chemistry
  [SJR: 0.688]   [H-I: 43]   [10 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1476-9271
   Published by Elsevier Homepage  [2805 journals]
  • Cryptic tRNAs in chaetognath mitochondrial genomes
    • Abstract: Publication date: Available online 25 April 2016
      Source:Computational Biology and Chemistry
      Author(s): Roxane-Marie Barthélémy, Hervé Seligmann
      The chaetognaths constitute a small and enigmatic phylum of little marine invertebrates. Both nuclear and mitochondrial genomes have numerous originalities, some phylum-specific. Until recently, their mitogenomes seemed containing only one tRNA gene (trnMet), but a recent study found in two chaetognath mitogenomes two and four tRNA genes. Moreover, apparently two conspecific mitogenomes have different tRNA gene numbers (one and two). Reanalyses by tRNAscan-SE and ARWEN softwares of the five available complete chaetognath mitogenomes suggest numerous additional tRNA genes from different types. Their total number never reaches the 22 found in most other invertebrates using that genetic code. Predicted error compensation between codon-anticodon mismatch and tRNA misacylation suggests translational activity by tRNAs predicted solely according to secondary structure for tRNAs predicted by tRNAscan-SE, not ARWEN. Numbers of predicted stop-suppressor (antitermination) tRNAs coevolve with predicted overlapping, frameshifted protein coding genes including stop codons. Sequence alignments in secondary structure prediction with non-chaetognath tRNAs suggest that the most likely functional tRNAs are in intergenic regions, as regular mt-tRNAs. Due to usually short intergenic regions, generally tRNA sequences partially overlap with flanking genes. Some tRNA pairs seem templated by sense-antisense strands. Moreover, 16S rRNA genes, but not 12S rRNAs, appear as tRNA nurseries, as previously suggested for multifunctional ribosomal-like protogenomes.

      PubDate: 2016-04-29T08:19:10Z
  • Designing an efficient multi-epitope peptide vaccine against Vibrio
           cholerae via combined immunoinformatics and protein interaction based
    • Abstract: Publication date: Available online 19 April 2016
      Source:Computational Biology and Chemistry
      Author(s): Navid Nezafat, Zeinab Karimi, Mahbobeh eslami, Milad Mohkam, Sanam Zandian, Younes Ghasemi
      Cholera continues to be a major global health concern. Among different Vibrio cholerae strains, only O1 and O139 cause acute diarrheal diseases that are related to epidemic and pandemic outbreaks. The currently available cholera vaccines are mainly lived and attenuated vaccines consisting of V. cholerae virulence factors such as toxin-coregulated pili (TCP), outer membrane proteins (Omps), and nontoxic cholera toxin B subunit (CTB). Nowadays, there is a great interest in designing an efficient epitope vaccine against cholera. Epitope vaccines consisting of immunodominant epitopes and adjuvant molecules enhance the possibility of inciting potent protective immunity. In this study, V. cholerae protective antigens (OmpW, OmpU, TcpA and TcpF) and the CTB, which is broadly used as an immunostimulatory adjuvant, were analyzed using different bioinformatics and immunoinformatics tools. The common regions between promiscuous epitopes, binding to various HLA-II supertype alleles, and B-cell epitopes were defined based upon the aforementioned protective antigens. The ultimately selected epitopes and CTB adjuvant were fused together using proper GPGPG linkers to enhance vaccine immunogenicity. A three-dimensional model of the thus constructed vaccine was generated using I-TASSER. The model was structurally validated using the ProSA-web error-detection software and the Ramachandran plot. The validation results indicated that the initial 3D model needed refinement. Subsequently, a high-quality model obtained after various refinement cycles was used for defining conformational B-cell epitopes. Several linear and conformational B-cell epitopes were determined within the epitope vaccine, suggesting likely antibody triggering features of our designed vaccine. Next, molecular docking was performed between the 3D vaccine model and the tertiary structure of the toll like receptor 2 (TLR2). To gain further insight into the interaction between vaccine and TLR2, molecular dynamics simulation was performed, corroborating stable vaccine-TLR2 binding. In sum, the results suggest that our designed epitope vaccine could incite robust long-term protective immunity against V. cholera.
      Graphical abstract image

      PubDate: 2016-04-21T08:04:44Z
  • Sequence-Based Analysis of 5′UTR and Coding Regions of CASP3 in
           terms of miRSNPs and SNPs in Targetting miRNAs
    • Abstract: Publication date: Available online 11 April 2016
      Source:Computational Biology and Chemistry
      Author(s): Sercan Ergun, Serdar Oztuzcu
      Apoptosis is described as a mechanism of cell death occurring after adequate cellular harm. Deregulation of apoptosis occurs in many human conditions such as autoimmune disorders, ischemic damage, neurodegenerative diseases and different cancer types. Information relating miRNAs to cancer is increasing. miRNAs can affect development of cancer via many different pathways, including apoptosis. Polymorphisms in miRNA genes or miRNA target sites (miRSNPs) can change miRNA activity. Although polymorphisms in miRNA genes are very uncommon, SNPs in miRNA-binding sites of target genes are quite common. Many researches have revelaed that SNPs in miRNA target sites improve or decrease the efficacy of the interaction between miRNAs and their target genes. Our aim was to specify miRSNPs on CASP3 gene (caspase-3) and SNPs in miRNA genes targeting 5'UTR and coding exons of CASP3, and evaluate the effect of these miRSNPs and SNPs of miRNA genes with respect to apoptosis. We detected 141 different miRNA binding sites (126 different miRNAs) and 7 different SNPs in binding sites of miRNA in 5′UTR and CDS of CASP3 gene. Intriguingly, miR-339-3p’s binding site on CASP3 has a SNP (rs35372903, G/A) on CASP3 5′UTR and its genomic sequence has a SNP (rs565188493, G/A) at the same nucleotide with rs35372903. Also, miR-339-3p has two other SNPs (rs373011663, C/T rs72631820, A/G) of which the first is positioned at the binding site. Here, miRSNP (rs35372903) at CASP3 5′UTR and SNP (rs565188493) at miR-339-3p genomic sequence cross-matches at the same site of binding region. Besides, miR-339-3p targets many apoptosis related genes (ZNF346, TAOK2, PIM2, HIP1, BBC3, TNFRSF25, CLCF1, IHPK2, NOL3) although it had no apoptosis related interaction proven before. This means that miR-339-3p may also have a critical effect on apoptosis via different pathways other than caspase-3. Hence, we can deduce that this is the first study demonstrating a powerful association between miR-339-3p and apoptosis upon computational analysis.
      Graphical abstract image

      PubDate: 2016-04-17T07:59:36Z
  • Guest Editorial for the 14th Asia Pacific Bioinformatics Conference
    • Abstract: Publication date: Available online 13 April 2016
      Source:Computational Biology and Chemistry
      Author(s): Lu Tian, Jijun Tang, Yi-Ping Phoebe Chen

      PubDate: 2016-04-17T07:59:36Z
  • Genome-wide identification and expression analysis of swi1 genes in
           Boechera species
    • Abstract: Publication date: Available online 13 April 2016
      Source:Computational Biology and Chemistry
      Author(s): Fatih Sezer, Gözde Yüzbaşioğlu, Aslıhan Özbilen, Kemal M. Taşkin
      As a mode of reproduction in plants, apomixis leads to the generation of clones via seeds. Apomictic plants form viable diploid female gametes without meiosis (apomeiosis) and produce embryos without fertilization (parthenogenesis). Apomeiosis, as a major component of apomixis, has recently been reported in some Arabidopsis thaliana mutants; dyad mutants of SWI1 showed developmental processes common to apomeiosis, such as producing functional diploid gametes. However, the orthologs of SWI1 genes in natural apomicts has not been previously reported. To identify the relationship between the SWI1 gene and the apomeiosis process, we isolated and sequenced SWI1 orthologs from Boechera species, including apomictic and sexual species. Boechera species are close relatives of A. thaliana and thus are advantageous model species for apomixis research. The SWI1 cDNAs were obtained by RT-PCR from apomictic and sexual Boechera young flower buds. We sequenced partial SWI1 transcripts that were 650bp for B. holboellii and 684bp for B. stricta. These SWI1-like sequences showed 86% similarity for B. holboellii and 92% for B. stricta to the A. thaliana SWI1 transcript. We also used available genome data and amplified genomic sequences for SWI1 orthologs in B. holboellii and B. stricta. The predicted proteins contain a phospholipase C domain and a nuclear localization signal. Sequence analysis did not show significant mutations related to apomixis, and phylogenetic analysis showed that SWI1-like sequences were common across plant families, regardless of the presence of a sexual or apomictic reproduction system. We also investigated the expression levels of SWI1 mRNA in the B. holboellii and B. stricta young unopened flower buds and found that relatively high levels of expression occurred in apomicts.
      Graphical abstract image

      PubDate: 2016-04-17T07:59:36Z
  • Analysis of cis-acting regulatory elements of Respiratory burst oxidase
           homolog (Rboh) gene families in Arabidopsis and rice provides clues for
           their diverse functions
    • Abstract: Publication date: Available online 13 April 2016
      Source:Computational Biology and Chemistry
      Author(s): Gurpreet Kaur, Pratap Kumar Pati
      NADPH oxidase (NOX) is a critical enzyme in the production of reactive oxygen species (ROS). It catalyzes the production of apoplastic superoxide (O2 −), that regulates a wide array of biological functions in different organisms. Plant Noxes are homologs of catalytic subunit of mammalian NADPH oxidase and are well-known as Respiratory burst oxidase homologs (Rbohs). In recent years, there has been growing interest to study plant Noxes due to their versatile roles in plant systems. In the present work, comprehensive analysis on upstream regions from 10 Rbohs from Arabidopsis thaliana and 9 from Oryza sativa japonica was conducted. The distribution of various cis-elements, CpG islands and tandem repeats were analyzed to uncover the 5′ regulatory region in wide array of functions from Rbohs. Information retrieved from cis-elements analysis was also correlated with the microarray data. Present study which involves uncovering transcription regulatory elements provided vital clues for diverse functions of plant Rbohs.
      Graphical abstract image

      PubDate: 2016-04-17T07:59:36Z
  • A Computational Method for Prediction of rSNPs in Human Genome
    • Abstract: Publication date: Available online 4 April 2016
      Source:Computational Biology and Chemistry
      Author(s): Rong Li, Qiuqiang Han, Jun Liu, Jiguang Zheng, Ruiling Liu
      Regulatory single nucleotide polymorphisms (rSNPs) in human genome are thought to be responsible for phenotypic differences, including susceptibility to diseases and treatment outcomes, even they do not change any gene product. However, a genome-wide search for rSNPs has not been properly addressed so far. In this work, a computational method for rSNP identification is proposed. As background SNPs far outnumber rSNPs, an ensemble method is applied to handle imbalanced data, which firstly converts an unbalanced dataset into several balanced ones and then models for every balanced dataset. Two major types of features are extracted, that are sequence based features and allele-specific based features. Then random forest is applied to build the recognition model for each balanced dataset. Finally, ensemble strategies are adopted to combine the result of each model together. We have tested our method on a set of experimentally verified rSNPs, and leave-one-out cross-validation result shows that our method can achieve accuracy with sensitivity of 73.8%, specificity of 71.8% and the area under ROC curve (AUC) is 0.756. In addition, our method is threshold free and doesn’t rely on data of regulatory elements, thus it will have better adaptability when facing different data scenarios. The original data and the source matlab codes involved are available at
      Graphical abstract image

      PubDate: 2016-04-05T05:12:06Z
  • Exploring the inhibitory potential of bioactive compound from Luffa
           acutangula against NF-κB − A molecular docking and dynamics
    • Abstract: Publication date: Available online 31 March 2016
      Source:Computational Biology and Chemistry
      Author(s): Ramar Vanajothi, Pappu Srinivasan
      Nuclear factor kappa B (NF-κB) is a transcription factor, plays a crucial role in the regulation of various physiological processes such as differentiation, cell proliferation and apoptosis. It also coordinates the expression of various soluble proinflammatory mediators like cytokines and chemokines. The 1, 8-dihydroxy-4-methylanthracene-9, 10-dione (DHMA) was isolated from Luffa acutangala and its in vitro cytotoxic activity against NCI-H460 cells was reported earlier. It also effectively induces apoptosis through suppressing the expression NF-κB protein. Based on experimental evidence, the binding affinity of compound 1 with NF-κB p50 (monomer) and NF-κB-DNA was investigated using molecular docking and its stability was confirmed through molecular dynamic simulation. The reactivity of the compound was evaluated using density functional theory (DFT) calculation. From the docking results, we noticed that the hydroxyl group of DHMA forms hydrogen bond interactions with polar and negatively charged amino acid Tyr57 and Asp239 and the protein-ligand complex was stabilized through pi-pi stacking with the help of polar amino acid His114, which plays a key role in binding of NF-κB to DNA at a site of DNA-binding region (DBR). The result indicates that the isolated bioactive compound DHMA might have altered the binding affinity between DNA and NF-κB. These findings suggest that potential use of DHMA in cancer chemoprevention and therapeutics.
      Graphical abstract image

      PubDate: 2016-04-01T05:06:40Z
  • SUMONA: A Supervised Method for Optimizing Network Alignment
    • Abstract: Publication date: Available online 30 March 2016
      Source:Computational Biology and Chemistry
      Author(s): Erhun Giray Tuncay, Tolga Can
      This study focuses on improving the multi-objective memetic algorithm for protein-protein interaction (PPI) network alignment, Optimizing Network Aligner - OptNetAlign, via integration with other existing network alignment methods such as SPINAL, NETAL and HubAlign. The output of this algorithm is an elite set of aligned networks all of which are optimal with respect to multiple user-defined criteria. However, OptNetAlign is an unsupervised genetic algorithm that initiates its search with completely random solutions and it requires substantial running times to generate an elite set of solutions that have high scores with respect to the given criteria. In order to improve running time, the search space of the algorithm can be narrowed down by focusing on the most desired criteria and trying to optimize other relevant criteria on a more limited set of solutions. The method presented in this study improves OptNetAlign in a supervised fashion by utilizing the alignment results of different network alignment algorithms with varying parameters that depend upon user preferences. Therefore, the user can prioritize certain objectives upon others and achieve better running time performance while optimizing the secondary objectives.
      Graphical abstract image Highlights

      PubDate: 2016-04-01T05:06:40Z
  • Experimental and molecular dynamics studies showed the CBP KIX mutation
           affects the stability of CBP:c-Myb complex
    • Abstract: Publication date: Available online 21 March 2016
      Source:Computational Biology and Chemistry
      Author(s): Anne Odoux, Darren Jindal, Tamara C. Tamas, Benjamin W.H. Lim, Drake Pollard, Wu Xu
      The coactivators CBP (CREBBP) and its paralog p300 (EP300), two conserved multi-domain proteins in eukaryotic organisms, regulate gene expression in part by binding DNA-binding transcription factors. It was previously reported that the CBP/p300 KIX domain mutant (Y650A, A654Q and Y658A) altered both c-Myb-dependent gene activation and repression, and the mice with these three point mutations had reduced numbers of platelets, B cells, T cells, and red blood cells. Here, our transient transfection assays demonstrated that the mouse embryonic fibroblast cells containing the same mutations in the KIX domain and without wild-type allele of either CBP or p300, showed a decreased c-Myb-mediated transcription. Dr. Wright’s group solved a 3-D structure of mouse CBP:c-Myb complex using NMR method. To take advantage of the experimental structure and function data, and improved theoretical calculation methods, we performed MD simulations of CBP KIX, CBP KIX with the mutations, and c-Myb, as well as binding energy analysis for both the wild-type and mutant complexes. The binding between CBP and c-Myb is mainly mediated by a shallow hydrophobic groove in the center where the side-chain of Leu302 of c-Myb plays an essential role and two salt bridges at the two ends. We found that the KIX mutations slightly decreased stability of the CBP:c-Myb complex as demonstrated by higher binding energy calculated using either MM/PBSA or MM/GBSA methods. More specifically, the KIX mutations affected the two salt bridges between CBP and c-Myb (CBP-R646 and c-Myb-E306; CBP-E665 and c-Myb-R294). Our studies also revealed differing dynamics of the hydrogen bonds between CBP-R646 and c-Myb-E306, and between CBP-E665 and c-Myb-R294 caused by the CBP KIX mutations. In the wild-type CBP:c-Myb complex, both the hydrogen bonds stayed relatively stable. In contrast, in the mutant CBP:c-Myb complex, hydrogen bonds between R646 and E306 showed an increasing trend followed by a decreasing trend and hydrogen bonds between E665:R294 pair exhibited a fast decreasing trend over time during MD simulations. In addition, our data showed that the KIX mutations attenuate CBP’s hydrophobic interaction with Leu302 of c-Myb. Furthermore, our 500-ns MD simulations showed that the CBP KIX with the mutations has slightly lower potential energy than the wild-type CBP. The CBP KIX structures with or without its interacting protein c-Myb are different for both the wild-type and mutant CBP KIX, and this is likewise the case for c-Myb with or without CBP, suggesting that presence of an interacting protein influences the structure of a protein. Taken together, these analyses will improve our understanding of the exact functions of CBP and its interaction with c-Myb.
      Graphical abstract image

      PubDate: 2016-03-24T04:37:44Z
  • A multilayer screening approach toward the discovery of novel Pf-DHFR
    • Abstract: Publication date: Available online 22 March 2016
      Source:Computational Biology and Chemistry
      Author(s): Sourav Bagchi, Manoj Kumar, Anuj Sharmaa
      A small yet diverse xanthone library was build and computationally docked against wild type Pf-DHFR by Molegro Virtual Docker (MolDock). For analysis of results an integrated approach based on re-ranking, scaling (based on heavy atom counts), pose clustering and visual inspection was implemented. Standard methods such as self-docking (for docking), EF analysis, average rank determinations (for size normalization), and cluster quality indices (for pose clustering) were used for validation of results. Three compounds X5, X113A and X164B displayed contact footprints similar to the known inhibitors with good scores. Finally, 16 compounds were extracted from ZINC data base by similarity based screening, docking score and drug/lead likeness. Out of these 16 compounds, 11 displayed very close contact footprints to experimentally known inhibitors, indicating there potential utility in further drug discovery efforts.
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      PubDate: 2016-03-24T04:37:44Z
  • Dynamic characterization of HLA-B*44 Alleles: A comparative molecular
           dynamics simulation study
    • Abstract: Publication date: Available online 18 March 2016
      Source:Computational Biology and Chemistry
      Author(s): Pemra Özbek
      Human Leukocyte Antigens (HLA) are highly polymorphic proteins that play a key role in the immune system. HLA molecule is present on the cell membrane of antigen-presenting cells of the immune system and presents short peptides, originating from the proteins of invading pathogens or self-proteins, to the T-cell Receptor (TCR) molecule of the T-cells. In this study, peptide-binding characteristics of HLA-B*44:02, 44:03, 44:05 alleles bound to three nonameric peptides were studied using molecular dynamics simulations. Polymorphisms among these alleles (Asp116Tyr and Asp156Leu) result in major differences in the allele characteristics. While HLA-B*44:02 (Asp116, Asp156) and HLA-B*44:03 (Asp116, Leu156) depend on tapasin for efficient peptide loading, HLA-B*44:05 (Tyr116, Asp156) is tapasin independent. On the other hand, HLA-B*44:02 and HLA-B*44:03 mismatch is closely related to transplant rejection and acute-graft-versus-host disease. In order to understand the dynamic characteristics, the simulation trajectories were analyzed by applying Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) calculations and hydrogen bonding analysis. Binding dynamics of the three HLA-B*44 alleles and peptide sequences are comparatively discussed. In general, peptide binding stability is found to depend on the peptide rather than the allele type for HLA-B*44 alleles.
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      PubDate: 2016-03-20T04:27:19Z
  • IFC Editorial Board
    • Abstract: Publication date: April 2016
      Source:Computational Biology and Chemistry, Volume 61

      PubDate: 2016-03-20T04:27:19Z
  • Title page
    • Abstract: Publication date: April 2016
      Source:Computational Biology and Chemistry, Volume 61

      PubDate: 2016-03-20T04:27:19Z
  • Zooming-in on cancer metabolic rewiring with tissue specific
           constraint-based models
    • Abstract: Publication date: Available online 14 March 2016
      Source:Computational Biology and Chemistry
      Author(s): Marzia Di Filippo, Riccardo Colombo, Chiara Damiani, Dario Pescini, Daniela Gaglio, Marco Vanoni, Lilia Alberghina, Giancarlo Mauri
      The metabolic rearrangements occurring in cancer cells can be effectively investigated with a Systems Biology approach supported by metabolic network modeling. We here present tissue-specific constraint-based core models for three different types of tumors (liver, breast and lung) that serve this purpose. The core models were extracted and manually curated from the corresponding genome-scale metabolic models in the Human Metabolic Atlas database with a focus on the pathways that are known to play a key role in cancer growth and proliferation. Along similar lines, we also reconstructed a core model from the original general human metabolic network to be used as a reference model. A comparative Flux Balance Analysis between the reference and the cancer models highlighted both a clear distinction between the two conditions and a heterogeneity within the three different cancer types in terms of metabolic flux distribution. These results emphasize the need for modeling approaches able to keep up with this tumoral heterogeneity in order to identify more suitable drug targets and develop effective treatments. According to this perspective, we identified key points able to reverse the tumoral phenotype towards the reference one or vice-versa.
      Graphical abstract image Highlights

      PubDate: 2016-03-16T04:17:04Z
  • Truncation, modification, and optimization of MIG6segment 2 peptide to
           target lung cancer-related EGFR
    • Abstract: Publication date: April 2016
      Source:Computational Biology and Chemistry, Volume 61
      Author(s): Xiao-Dong Yu, Rui Yang, Chang-Jun Leng
      Human epidermal growth factor receptor (EGFR) plays a central role in the pathological progression and metastasis of lung cancer; the development and clinical application of therapeutic agents that target the receptor provide important insights for new lung cancer therapies. The tumor-suppressor protein MIG6 is a negative regulator of EGFR, which can bind at the activation interface of asymmetric dimer of EGFR kinase domains to disrupt dimerization and then inactivate the kinase (Zhang X. et al. Nature 2007, 450: 741–744). The protein adopts two separated segments, i.e. MIG6segment 1 and MIG6segment 2, to directly interact with EGFR. Here, computational modeling and analysis of the intermolecular interaction between EGFR kinase domain and MIG6segment 2 peptide revealed that the peptide is folded into a two-stranded β-sheet composed of β-strand 1 and β-strand 2; only the β-strand 2 can directly interact with EGFR activation loop, while leaving β-strand 1 apart from the kinase. A C-terminal island within the β-strand 2 is primarily responsible for peptide binding, which was truncated from the MIG6segment 2 and exhibited weak affinity to EGFR kinase domain. Structural and energetic analysis suggested that phosphorylation at residues Tyr394 and Tyr395 of truncated peptide can considerably improve EGFR affinity, and mutation of other residues can further optimize the peptide binding capability. Subsequently, three derivative versions of the truncated peptide, including phosphorylated and dephosphorylated peptides as well as a double-point mutant were synthesized and purified, and their affinities to the recombinant protein of human EGFR kinase domain were determined by fluorescence anisotropy titration. As expected theoretically, the dephosphorylated peptide has no observable binding to the kinase, and phosphorylation and mutation can confer low and moderate affinities to the peptide, respectively, suggesting a good consistence between the computational analysis and experimental assay.
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      PubDate: 2016-03-12T03:51:18Z
  • Two highly similar LAEDDTNAQKT and LTDKIGTEI epitopes in G glycoprotein
           may be useful for effective epitope based vaccine design against
           pathogenic Henipavirus
    • Abstract: Publication date: Available online 3 March 2016
      Source:Computational Biology and Chemistry
      Author(s): Md. Masud Parvege, Monzilur Rahman, Yead Morshed Nibir, Mohammad Shahnoor Hossain
      Nipah virus and Hendra virus, two members of the genus Henipavirus, are newly emerging zoonotic pathogens which cause acute respiratory illness and severe encephalitis in human. Lack of the effective antiviral therapy endorses the urgency for the development of vaccine against these deadly viruses. In this study, we employed various computational approaches to identify epitopes which has the potential for vaccine development. By analyzing the immune parameters of the conserved sequences of G glycoprotein using various databases and bioinformatics tools, we identified two potential epitopes which may be used as peptide vaccines. Using different B cell epitope prediction servers, four highly similar B cell epitopes were identified. Immunoinformatics analyses revealed that LAEDDTNAQKT is a highly flexible and accessible B-cell epitope to antibody. Highly similar putative CTL epitopes were analyzed for their binding with the HLA-C 12*03 molecule. Docking simulation assay revealed that LTDKIGTEI has significantly lower binding energy, which bolstered its potential as epitope-based vaccine design. Finally, cytotoxicity analysis has also justified their potential as promising epitope-based vaccine candidate. In sum, our computational analysis indicates that either LAEDDTNAQKT or LTDKIGTEI epitope holds a promise for the development of universal vaccine against all kinds of pathogenic Henipavirus. Further in vivo and in vitro studies are necessary to validate the obtained findings.
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      PubDate: 2016-03-08T03:38:24Z
  • Using propensity score adjustment method in genetic association studies
    • Abstract: Publication date: Available online 3 March 2016
      Source:Computational Biology and Chemistry
      Author(s): Amrita Sengupta Chattopadhyay, Ying-Chao Lin, Ai-Ru Hsieh, Chien-Ching Chang, Ie-Bin Lian, Cathy S.J. Fann
      Background The statistical tests for single locus disease association are mostly under-powered. If a disease associated causal single nucleotide polymorphism (SNP) operates essentially through a complex mechanism that involves multiple SNPs or possible environmental factors, its effect might be missed if the causal SNP is studied in isolation without accounting for these unknown genetic influences. In this study, we attempt to address the issue of reduced power that is inherent in single point association studies by accounting for genetic influences that negatively impact the detection of causal variant in single point association analysis. In our method we use Propensity Score (PS) to adjust for the effect of SNPs that influence the marginal association of a candidate marker. These SNPs might be in linkage disequilibrium (LD) and/or epistatic with the target-SNP and have a joint interactive influence on the disease under study. We therefore propose a Propensity Score Adjustment Method (PSAM) as a tool for dimension reduction to improve the power for single locus studies through an estimated PS to adjust for influence from these SNPs while regressing disease status on the target-genetic locus. The degree of freedom of such a test is therefore always restricted to 1. Results We assess PSAM under the null hypothesis of no disease association to affirm that it correctly controls for the type-I-error rate (<0.05). PSAM displays reasonable power (>70%) and shows an average of 15% improvement in power as compared with commonly-used logistic regression method and PLINK under most simulated scenarios. Using the open-access Multifactor Dimensionality Reduction dataset, PSAM displays improved significance for all disease loci. Through a whole genome study, PSAM was able to identify 21 less significant SNPs from the GAW16 NARAC dataset by reducing the original trend-test p-values from within 0.001 and 0.05 to less than 0.0009, and among which 6 SNPs were further found to be associated with immunity and inflammation. Conclusions PSAM improves the significance of single-locus association of causal SNPs which have had marginal single point association by adjusting for influence from other SNPs in a dataset. This would explain part of the missing heritability without increasing the complexity of the model due to huge multiple testing scenarios. The newly reported SNPs from GAW16 data would provide evidences for further research to elucidate the etiology of Rheumatoid Arthritis. PSAM is proposed as an exploratory tool that would be complementary to other existing methods. A downloadable user friendly program, PSAM, written in SAS, is available for public use.
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      PubDate: 2016-03-08T03:38:24Z
  • The modularity and dynamicity of miRNA-mRNA interactions in high-grade
           serous ovarian carcinomas and the prognostic implication
    • Abstract: Publication date: Available online 27 February 2016
      Source:Computational Biology and Chemistry
      Author(s): Wensheng Zhang, Andrea Edwards, Wei Fan, Erik K. Flemington, Kun Zhang
      Ovarian carcinoma is the fifth-leading cause of cancer death among women in the United States. Major reasons for this persistent mortality include the poor understanding of the underlying biology and a lack of reliable biomarkers. Previous studies have shown that aberrantly expressed MicroRNAs (miRNAs) are involved in carcinogenesis and tumor progression by post-transcriptionally regulating gene expression. However, the interference of miRNAs in tumorigenesis is quite complicated and far from being fully understood. In this work, by an integrative analysis of mRNA expression, miRNA expression and clinical data published by The Cancer Genome Atlas (TCGA), we studied the modularity and dynamicity of miRNA-mRNA interactions and the prognostic implications in high-grade serous ovarian carcinomas. With the top transcriptional correlations (Bonferroni-adjusted p-value<0.01) as inputs, we identified five miRNA-mRNA module pairs (MPs), each of which included one positive-connection (correlation) module and one negative-connection (correlation) module. The number of miRNAs or mRNAs in each module varied from 3 to 7 or from 2 to 873. Among the four major negative-connection modules, three fit well with the widely accepted miRNA-mediated post-transcriptional regulation theory. These modules were enriched with the genes relevant to cell cycle and immune response. Moreover, we proposed two novel algorithms to reveal the group or sample specific dynamic regulations between these two RNA classes. The obtained miRNA-mRNA dynamic network contains 3350 interactions captured across different cancer progression stages or tumor grades. We found that those dynamic interactions tended to concentrate on a few miRNAs (e.g. miRNA-936), and were more likely present on the miRNA-mRNA pairs outside the discovered modules. In addition, we also pinpointed a robust prognostic signature consisting of 56 modular protein-coding genes, whose co-expression patterns were predictive for the survival time of ovarian cancer patients in multiple independent cohorts.
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      PubDate: 2016-03-03T03:23:03Z
  • A computational model for predicting fusion peptide of retroviruses
    • Abstract: Publication date: Available online 2 March 2016
      Source:Computational Biology and Chemistry
      Author(s): Sijia Wu, Jiuqiang Han, Ruiling Liu, Jun Liu, Hongqiang Lv
      As a pivotal domain within envelope protein, Fusion peptide (FP) plays a crucial role in pathogenicity and therapeutic intervention. Taken into account the limited FP annotations in NCBI database and absence of FP prediction software, it is urgent and desirable to develop a bioinformatics tool to predict new putative FPs (np-FPs) in retroviruses. In this work, a sequence-based FP model was proposed by combining Hidden Markov Method with similarity comparison. The classification accuracies are 91.97% and 92.31% corresponding to 10-fold and leave-one-out cross-validation. After scanning sequences without FP annotations, this model discovered 53,946 np-FPs. The statistical results on FPs or np-FPs reveal that FP is a conserved and hydrophobic domain. The FP software programmed for windows environment is available at
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      PubDate: 2016-03-03T03:23:03Z
  • Structure-based design and confirmation of peptide ligands for neuronal
           polo-like kinase to promote neuroregeneration
    • Abstract: Publication date: Available online 27 February 2016
      Source:Computational Biology and Chemistry
      Author(s): He-Li Cao, Hao Chen, Yu-Hui Cui, Heng-Li Tian, Jiong Chen
      Neuronal polo-like kinase (nPLK) is an essential regular of cell cycle and differentiation in nervous system, and targeting nPLK has been established as a promising therapeutic strategy to treat neurological disorders and to promote neuroregeneration. The protein contains an N-terminal kinase domain (KD) and a C-terminal Polo-box domain (PBD) that are mutually inhibited by each other. Here, the intramolecular KD–PBD complex in nPLK was investigated at structural level via bioinformatics analysis, molecular dynamics (MD) simulation and binding affinity scoring. From the complex interface two regions representing separately two continuous peptide fragments in PBD domain were identified as the hot spots of KD–PBD interaction. Structural and energetic analysis suggested that one (PBD peptide 1) of the two peptides can bind tightly to a pocket nearby the active site of KD domain, which is thus potential as self-inhibitory peptide to target and suppress nPLK kinase activity. The knowledge harvesting from computational studies were then used to guide the structural optimization and mutation of PBD peptide 1. Consequently, two of three peptide mutants separately exhibited moderately and considerably increased affinity as compared to the native peptide. The computationally modeled complex structures of KD domain with these self-inhibitory peptides were also examined in detail to unravel the structural basis and energetic property of nPLK–peptide recognition and interaction.
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      PubDate: 2016-03-03T03:23:03Z
  • Comparative genomics for understanding the structure, function and
           sub-cellular localization of hypothetical proteins in Thermanerovibrio
           acidaminovorans DSM 6589 (tai)
    • Abstract: Publication date: Available online 24 February 2016
      Source:Computational Biology and Chemistry
      Author(s): Hitesh S. Thakre, Dilip B. Meshram, Chandrakant M. Jangam, Pawan Labhasetwar, Kunal Roychoudhary, Arun B. Ingle
      The Thermanerovibrio acidaminovorans DSM 6589 (tai) is a unique bacterium isolated from anaerobic sludge bed reactor from sugar refinery in Netherland. The comparative genomic studies for understanding the hypothetical proteins in Thermanerovibrio acidaminovorans DSM 6589 (tai) were carried out using different bioinformatic tools and web servers. In all 320 hypothetical proteins were screened from the total available genome. The Insilico function prediction for 320 hypothetical proteins was achieved by using different online servers like CDD-Blast, Interproscan and pfam whereas, the structure prediction for 202 hypothetical proteins were deciphered by using protein structure prediction server (PS2 server). The sub-cellular localization for the identified proteins was predicted by the use of cello v2.5 for 320. The study carried out has helped us to understand the structures and functions of unknown proteins available in Thermanerovibrio acidaminovorans DSM 6589 (tai) through comparative genomic approach.
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      PubDate: 2016-02-27T03:12:19Z
  • From NGS assembly challenges to instability of fungal mitochondrial
           genomes: a case study in genome complexity
    • Abstract: Publication date: Available online 21 February 2016
      Source:Computational Biology and Chemistry
      Author(s): Elizabeth Misas, José Fernando Mu-oz, Juan Esteban Gallo, Juan Guillermo McEwen, Oliver Keatinge Clay
      The presence of repetitive or non-unique DNA persisting over sizable regions of a eukaryotic genome can hinder the genome's successful de novo assembly from short reads: ambiguities in assigning genome locations to the non-unique subsequences can result in premature termination of contigs and thus overfragmented assemblies. Fungal mitochondrial (mtDNA) genomes are compact (typically less than 100 kb), yet often contain short non-unique sequences that can be shown to impede their successful de novo assembly in silico. Such repeats can also confuse processes in the cell in vivo. A well-studied example is ectopic (out-of-register, illegitimate) recombination associated with repeat pairs, which can lead to deletion of functionally important genes that are located between the repeats. Repeats that remain conserved over micro- or macroevolutionary timescales despite such risks may indicate functionally or structurally (e.g., for replication) important regions. This principle could form the basis of a mining strategy for accelerating discovery of function in genome sequences. We present here our screening of a sample of 11 fully sequenced fungal mitochondrial genomes by observing where exact k-mer repeats occurred several times; initial analyses motivated us to focus on 17-mers occurring more than three times. Based on the diverse repeats we observe, we propose that such screening may serve as an efficient expedient for gaining a rapid but representative first insight into the repeat landscapes of sparsely characterized mitochondrial chromosomes. Our matching of the flagged repeats to previously reported regions of interest supports the idea that systems of persisting, non-trivial repeats in genomes can often highlight features meriting further attention.
      Graphical abstract image Highlights Mitochondrial genomes can contain repeat landscapes ranging from notable absence of repeats, as in human and fission yeast, to rich and complex repeat systems as in baker's yeast. In this article we characterize exact repetitions of 17-mers in bona fide complete mitochondrial genome sequences of 11 fungi. This strategy allowed us to view and analyze a diversity of repeat landscapes, each with their own repeat ‘fauna and flora’. The Figure depicts three mitochondrial genomes that contain amply repeated 17-mers (horizontal axis: number of occurrences; vertical axis: number of instances of that number of occurrences); examples of repeats with strong presence in the mitochondrial genomes are represented by sequences in corresponding colors.

      PubDate: 2016-02-22T03:01:04Z
  • Gene expression variability in mammalian embryonic stem cells using single
           cell RNA-seq data
    • Abstract: Publication date: Available online 18 February 2016
      Source:Computational Biology and Chemistry
      Author(s): Anna Mantsoki, Guillaume Devailly, Anagha Joshi
      Background Gene expression heterogeneity contributes to development as well as disease progression. Due to technological limitations, most studies to date have focused on differences in mean expression across experimental conditions, rather than differences in gene expression variance. The advent of single cell RNA sequencing has now made it feasible to study gene expression heterogeneity and to characterise genes based on their coefficient of variation. Methods We collected single cell gene expression profiles for 32 human and 39 mouse embryonic stem cells and studied correlation between diverse characteristics such as network connectivity and coefficient of variation (CV) across single cells. We further systematically characterised properties unique to High CV genes. Results Highly expressed genes tended to have a low CV and were enriched for cell cycle genes. In contrast, High CV genes were co-expressed with other High CV genes, were enriched for bivalent (H3K4me3 and H3K27me3) marked promoters and showed enrichment for response to DNA damage and DNA repair. Conclusions Taken together, this analysis demonstrates the divergent characteristics of genes based on their CV. High CV genes tend to form co-expression clusters and they explain bivalency at least in part.

      PubDate: 2016-02-22T03:01:04Z
  • Guided macro-mutation in a graded energy based genetic algorithm for
           protein structure prediction
    • Abstract: Publication date: April 2016
      Source:Computational Biology and Chemistry, Volume 61
      Author(s): Mahmood A. Rashid, Sumaiya Iqbal, Firas Khatib, Md Tamjidul Hoque, Abdul Sattar
      Protein structure prediction is considered as one of the most challenging and computationally intractable combinatorial problem. Thus, the efficient modeling of convoluted search space, the clever use of energy functions, and more importantly, the use of effective sampling algorithms become crucial to address this problem. For protein structure modeling, an off-lattice model provides limited scopes to exercise and evaluate the algorithmic developments due to its astronomically large set of data-points. In contrast, an on-lattice model widens the scopes and permits studying the relatively larger proteins because of its finite set of data-points. In this work, we took the full advantage of an on-lattice model by using a face-centered-cube lattice that has the highest packing density with the maximum degree of freedom. We proposed a graded energy—strategically mixes the Miyazawa–Jernigan (MJ) energy with the hydrophobic-polar (HP) energy—based genetic algorithm (GA) for conformational search. In our application, we introduced a 2×2 HP energy guided macro-mutation operator within the GA to explore the best possible local changes exhaustively. Conversely, the 20×20 MJ energy model—the ultimate objective function of our GA that needs to be minimized—considers the impacts amongst the 20 different amino acids and allow searching the globally acceptable conformations. On a set of benchmark proteins, our proposed approach outperformed state-of-the-art approaches in terms of the free energy levels and the root-mean-square deviations.
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      PubDate: 2016-02-18T02:48:23Z
  • Animal inference on human mitochondrial diseases
    • Abstract: Publication date: Available online 16 February 2016
      Source:Computational Biology and Chemistry
      Author(s): Francesco Nardi, Francesco Frati, Pietro Liò
      Several pathological mutations in the human mitochondrial genome have been characterized based on medical, genetic and biochemical evidence. The observation that the structure and core functions of the mitochondrial genome are conserved from animals to man suggests that the analysis of animal variation may be informative to further characterize, and possibly predict, human pathological variants.

      PubDate: 2016-02-18T02:48:23Z
  • Identification of microRNA precursor based on gapped n-tuple structure
           status composition kernel
    • Abstract: Publication date: Available online 17 February 2016
      Source:Computational Biology and Chemistry
      Author(s): Bin Liu, Longyun Fang
      MicroRNAs (miRNAs) are small non-coding RNA molecules, functioning in transcriptional and post-transcriptional regulation of gene expression. The discrimination of the real premiRNAs from the false ones (such as hairpin sequences with similar stem-loops) is necessary for the understanding of miRNAs’ role in the control of cell life and death. Since both their small size and sequence specificity, it cannot be based on sequence information alone, but requires structure information about the miRNA precursor to get satisfactory performance. Kmers are convenient and widely used features for modeling the properties of miRNAs and other biological sequences. However, Kmers suffer from the inherent limitation that if the parameter K is increased to incorporate long range effects, some certain Kmer will appear rarely or even not appear, as a consequence, most Kmers absent and a few present once. Thus, the statistical learning approaches using Kmers as features become susceptible to noisy data once K becomes large. To address this problem, we introduce alternative feature sets using gapped n-tuple structure status composition, a new classifier, imiRNA-GSSC, and a general method for robust estimation of Kmer frequencies. To make the method applicable to large-scale genome wide applications, we adopted an efficient tree data structure for computing the kernel matrix. We show that compared to the original imiRNA-kmer and alternative approaches, our imiRNA-GSSC identifies miRNA precursors with significantly improved accuracy. We then show that, imiRNA-GSSC trained with human data can correctly predict 82.35% of the 4022 pre-miRNAs from 11 different species ranging from animals, plants and viruses. imiRNA-GSSC would be a useful high throughput tool for large-scale analysis of microRNA precursors.

      PubDate: 2016-02-18T02:48:23Z
  • IdealKnock: A framework for efficiently identifying knockout strategies
           leading to targeted overproduction
    • Abstract: Publication date: Available online 17 February 2016
      Source:Computational Biology and Chemistry
      Author(s): Deqing Gu, Cheng Zhang, Shengguo Zhou, Liujing Wei, Qiang Hua
      In recent years, computer aided redesigning methods based on genome-scale metabolic network models (GEMs) have played important roles in metabolic engineering studies; however, most of these methods are hindered by intractable computing times. In particular, methods that predict knockout strategies leading to overproduction of desired biochemical are generally unable to do high level prediction because the computational time will increase exponentially. In this study, we propose a new framework named IdealKnock, which is able to efficiently evaluate potentials of the production for different biochemical in a system by merely knocking out pathways. In addition, it is also capable of searching knockout strategies when combined with the OptKnock or OptGene framework. Furthermore, unlike other methods, IdealKnock suggests a series of mutants with targeted overproduction, which enables researchers to select the one of greatest interest for experimental validation. By testing the overproduction of a large number of native metabolites, IdealKnock showed its advantage in successfully breaking through the limitation of maximum knockout number in reasonable time and suggesting knockout strategies with better performance than other methods. In addition, gene–reaction relationship is well considered in the proposed framework.
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      PubDate: 2016-02-18T02:48:23Z
  • Copy Number Variants Calling for Single Cell Sequencing Data by
           Multi-constrained Optimization
    • Abstract: Publication date: Available online 17 February 2016
      Source:Computational Biology and Chemistry
      Author(s): Bo Xu, Hongmin Cai, Changsheng Zhang, Xi Yang, Guoqiang Han
      Variations in DNA copy number carry important information on genome evolution and regulation of DNA replication in cancer cells. The rapid development of single-cell sequencing technology allows one to explore gene expression heterogeneity among single-cells, thus providing important cancer cell evolution information. Single-cell DNA/RNA sequencing data usually have low genome coverage, which requires an extra step of amplification to accumulate enough samples. However, such amplification will introduce large bias and makes bioinformatics analysis challenging. Accurately modeling the distribution of sequencing data and effectively suppressing the bias influence is the key to success variations analysis. Recent advances demonstrate the technical noises by amplification are more likely to follow negative binomial distribution, a special case of Poisson distribution. Thus, we tackle the problem CNV detection by formulating it into a quadratic optimization problem involving two constraints, in which the underling signals are corrupted by Poisson distributed noises. By imposing the constraints of sparsity and smoothness, the reconstructed read depth signals from single-cell sequencing data are anticipated to fit the CNVs patterns more accurately. An efficient numerical solution based on the classical alternating direction minimization method (ADMM) is tailored to solve the proposed model. We demonstrate the advantages of the proposed method using both synthetic and empirical single-cell sequencing data. Our experimental results demonstrate that the proposed method achieves excellent performance and high promise of success with single-cell sequencing data.

      PubDate: 2016-02-18T02:48:23Z
  • Computational modeling of acrylodan-labeled cAMP dependent protein kinase
           catalytic subunit unfolding
    • Abstract: Publication date: Available online 11 February 2016
      Source:Computational Biology and Chemistry
      Author(s): Aleksei Kuznetsov, Rait Kivi, Jaak Järv
      Structure of the cAMP-dependent protein kinase catalytic subunit, where the asparagine residue 326 was replaced with acrylodan-cystein conjugate to implement this fluorescence reporter group into the enzyme, was modeled by molecular dynamics (MD) method and the positioning of the dye molecule in protein structure was characterized at temperatures 300 °K, 500 °K and 700 °K. It was found that the acrylodan moiety, which fluorescence is very sensitive to solvating properties of its microenvironment, was located on the surface of the native protein at 300 °K that enabled its partial solvation with water. At high temperatures the protein structure significantly changed, as the secondary and tertiary structure elements were unfolded and these changes were sensitively reflected in positioning of the dye molecule. At 700 °K complete unfolding of the protein occurred and the reporter group was entirely expelled into water. However, at 500 °K an intermediate of the protein unfolding process was formed, where the fluorescence reporter group was directed towards the protein interior and buried in the core of the formed molten globule state. This different positioning of the reporter group was in agreement with the two different shifts of emission spectrum of the covalently bound acrylodan, observed in the unfolding process of the protein.
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      PubDate: 2016-02-14T02:40:55Z
  • Computational identification of circular RNAs based on conformational and
           thermodynamic properties in the flanking introns
    • Abstract: Publication date: Available online 5 February 2016
      Source:Computational Biology and Chemistry
      Author(s): Ze Liu, Jiuqiang Han, Hongqiang Lv, Jun Liu, Ruiling Liu
      Circular RNAs (circRNAs) were found more than 30 years ago, but have been treated as molecular flukes in a long time. Combining deep sequencing studies with bioinformatics technique, thousands of endogenous circRNAs have been found in mammalian cells, and some researchers have proved that several circRNAs act as competing endogenous RNAs (ceRNAs) to regulate gene expression. However, the mechanism by which the precursor mRNA to be transformed into a circular RNA or a linear mRNA is largely unknown. In this paper, we attempted to bioinformatically identify shared genomic features that might further elucidate the mechanism of formation and proposed a SVM-based model to distinguish circRNAs from non-circularized, expressed exons. Firstly, conformational and thermodynamic dinucleotide properties in the flanking introns were extracted as potential features. Secondly, two feature selection methods were applied to gain the optimal feature subset. Our 10-fold cross-validation results showed that the model can be used to distinguish circRNAs from non-circularized, expressed exons with an Sn of 0.884, Sp of 0.900, ACC of 0.892, MCC of 0.784, respectively. The identification results suggest that conformational and thermodynamic properties in the flanking introns are closely related to the formation of circRNAs. Datasets and the tool involved in this paper are all available at
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      PubDate: 2016-02-10T02:35:30Z
  • In silico characterization of the interaction between LSKL peptide, a
           LAP-TGF-beta derived peptide, and ADAMTS1
    • Abstract: Publication date: Available online 31 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Marie-Amandine Laurent, Dominique Bonnier, Nathalie Théret, Pierre Tufféry, Gautier Moroy
      Metalloproteases involved in extracellular matrix remodeling play a pivotal role in cell response by regulating the bioavailability of cytokines and growth factors. Recently, the disintegrin and metalloprotease, ADAMTS1 has been demonstrated to be able to activate the transforming growth factor TGF-β, a major factor in fibrosis and cancer. The KTFR sequence from ADAMTS1 is responsible for the interaction with the LSKL peptide from the latent form of TGF-β, leading to its activation. While the atomic details of the interaction site can be the basis of the rational design of efficient inhibitory molecules, the binding mode of interaction is totally unknown. In this study, we show that recombinant fragments of human ADAMTS1 containing KTFR sequence keep the ability to bind the latent form of TGF-β. The recombinant fragment with the best affinity is modeled to investigate the binding mode of LSKL peptide with ADAMTS1 at the atomic level. Using a combined approach with molecular docking and multiple independent molecular dynamics (MD) simulations, we provide the binding mode of LSKL peptide with ADAMTS1. The MD simulations starting with the two lowest energy model predicted by molecular docking shows stable interactions characterized by 3 salt bridges (K3-NH3 + with E626-COO−; L4-COO− with K619-NH3 +; L1-NH3 + with E624-COO−) and 2 hydrogen bonds (S2-OH with E623-COO−; L4-NH with E623-COO−). The knowledge of this interaction mechanism paves the way to the design of more potent and more specific inhibitors against the inappropriate activation of TGF-β by ADAMTS1 in liver diseases.
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      PubDate: 2016-02-10T02:35:30Z
  • Model-guided metabolic gene knockout of gnd for enhanced succinate
           production in Escherichia coli from glucose and glycerol substrates
    • Abstract: Publication date: Available online 4 February 2016
      Source:Computational Biology and Chemistry
      Author(s): Bashir Sajo Mienda, Mohd Shahir Shamsir, Rosli Md Illias
      The metabolic role of 6-phosphogluconate dehydrogenase (gnd) under anaerobic conditions with respect to succinate production in E. coli remained largely unspecified. Herein we report what are to our knowledge the first metabolic gene knockout of gnd to have increased succinic acid production using both glucose and glycerol substrates in E. coli. Guided by a genome scale metabolic model, we engineered the E. coli host metabolism to enhance anaerobic production of succinic acid by deleting the gnd gene, considering its location in the boundary of oxidative and non-oxidative pentose phosphate pathway. This strategy induced either the activation of malic enzyme, causing up-regulation of phosphoenolpyruvate carboxylase (ppc) and down regulation of phosphoenolpyruvate carboxykinase (ppck) and/or prevents the decarboxylation of 6 phosphogluconate to increase the pool of glyceraldehyde-3-phosphate (GAP) that is required for the formation of phosphoenolpyruvate (PEP). This approach produced a mutant strain BMS2 with succinic acid production titers of 0.35g l−1 and 1.40g l−1 from glucose and glycerol substrates respectively. This work further clearly elucidates and informs other studies that the gnd gene, is a novel deletion target for increasing succinate production in E. coli under anaerobic condition using glucose and glycerol carbon sources. The knowledge gained in this study would help in E. coli and other microbial strains development for increasing succinate production and/or other industrial chemicals
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      PubDate: 2016-02-10T02:35:30Z
  • Design, synthesis and molecular modeling studies of few chalcone analogues
           of benzimidazole for epidermal growth factor receptor inhibitor in search
           of useful anticancer agent
    • Abstract: Publication date: Available online 6 February 2016
      Source:Computational Biology and Chemistry
      Author(s): Santosh S. Chhajed, Sandeep S. Sonawane, Chandrashekhar D. Upasani, Sanjay J. Kshirsagar, Pramodkumar P. Gupta
      In the present investigation, few 3-(substitutedphenyl)-1-[2-(1-hydroxy-ethyl)]-1H-benzimidazol-1-yl)prop-2-en-1-ones are EGFR antagonist are designed, by molecular docking analysis. The synthesized compounds were tested for their in vitro anticancer activity by propidium iodide fluorescent assay and Trypan blue viability assay against colorectal cancer cell lines (HCT116) and non-small cell lung cancer cell lines (H460). Human Epithelial Kidney cell lines (HEK) are used as normal cell lines for studying effect of drug on non-cancerous cells within human body. Evaluation of cytotoxic studies of synthesized compounds CHL(1–8) reveal that compound CHL1 [IC50 =7.31 and 10.16μM against HCT116 and H460 cell lines respectively, by PI assay] and CHL8 [IC50 =12.52 and 6.83 against HCT116 and H460μM cell lines respectively] possess promising cytotoxic activity.
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      PubDate: 2016-02-10T02:35:30Z
  • Predicting human intestinal absorption of diverse chemicals using ensemble
           learning based QSAR modeling approaches
    • Abstract: Publication date: Available online 29 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Nikita Basant, Shikha Gupta, Kunwar P. Singh
      Human intestinal absorption (HIA) of the drugs administered through the oral route constitutes an important criterion for the candidate molecules. The computational approach for predicting the HIA of molecules may potentiate the screening of new drugs. In this study, ensemble learning (EL) based qualitative and quantitative structure-activity relationship (SAR) models (gradient boosted tree, GBT and bagged decision tree, BDT) have been established for the binary classification and HIA prediction of the chemicals, using the selected molecular descriptors. The structural diversity of the chemicals and the nonlinear structure in the considered data were tested by the similarity index and Brock-Dechert-Scheinkman statistics. The external predictive power of the developed SAR models was evaluated through the internal and external validation procedures recommended in the literature. All the statistical criteria parameters derived for the performance of the constructed SAR models were above their respective thresholds suggesting for their robustness for future applications. In complete data, the qualitative SAR models rendered classification accuracy of  >99%, while the quantitative SAR models yielded correlation (R2) of >0.91 between the measured and predicted HIA values. The performances of the EL-based SAR models were also compared with the linear models (linear discriminant analysis, LDA and multiple linear regression, MLR). The GBT and BDT SAR models performed better than the LDA and MLR methods. A comparison of our models with the previously reported QSARs for HIA prediction suggested for their better performance. The results suggest for the appropriateness of the developed SAR models to reliably predict the HIA of structurally diverse chemicals and can serve as useful tools for the initial screening of the molecules in the drug development process.
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      PubDate: 2016-01-31T02:20:42Z
  • Structural Characterization of ANGPTL8 (Betatrophin) With its Interacting
           Partner Lipoprotein Lipase
    • Abstract: Publication date: Available online 25 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Amnah Siddiqa, Jamil Ahmad, Amjad Ali, Rehan Zafar Paracha, Zurah Shafi, Babar Aslam
      Angiopoietin-like protein 8 (ANGPTL8) (also known as betatrophin) is a newly identified secretory protein with a potential role in autophagy, lipid metabolism and pancreatic beta-cell proliferation. Its structural characterization is required to enhance our current understanding of its mechanism of action which could help in identifying its receptor and/or other binding partners. Based on the physiological significance and necessity of exploring structural features of ANGPTL8, the present study is conducted with a specific aim to model the structure of ANGPTL8 and study its possible interactions with Lipoprotein Lipase (LPL). To the best of our knowledge, this is the first attempt to predict 3-dimensional (3D) structure of ANGPTL8. Three different approaches were used for modeling of ANGPTL8 including homology modeling, de-novo structure prediction and their amalgam which is then proceeded by structure verification using ERRATT, PROSA, Qmean and Ramachandran plot scores. The selected models of ANGPTL8 were further evaluated for protein-protein interaction (PPI) analysis with LPL using CPORT and HADDOCK server. Our results have shown that the crystal structure of iSH2 domain of Phosphatidylinositol 3-kinase (PI3K) p85β subunit (PDB entry: 3mtt) is a good candidate for homology modeling of ANGPTL8. Analysis of inter-molecular interactions between the structure of ANGPTL8 and LPL revealed existence of several non covalent interactions. The residues of LPL involved in these interactions belong from its lid region, thrombospondin (TSP) region and heparin binding site which is suggestive of a possible role of ANGPTL8 in regulating the proteolysis, motility and localization of LPL. Besides, the conserved residues of SE1 region of ANGPTL8 formed interactions with the residues around the hinge region of LPL. Overall, our results support a model of inhibition of LPL by ANGPTL8 through the steric block of its catalytic site which will be further explored using wet lab studies in future.
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      PubDate: 2016-01-31T02:20:42Z
  • A multilevel ant colony optimization algorithm for classical and
           isothermic DNA sequencing by hybridization with multiplicity information
    • Abstract: Publication date: Available online 28 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Kamil Kwarciak, Marcin Radom, Piotr Formanowicz
      The classical sequencing by hybridization takes into account a binary information about sequence composition. A given element from an oligonucleotide library is or is not a part of the target sequence. However, the DNA chip technology has been developed and it enables to receive a partial information about multiplicity of each oligonucleotide the analyzed sequence consist of. Currently, it is not possible to assess the exact data of such type but even partial information should be very useful. Two realistic multiplicity information models are taken into consideration in this paper. The first one, called “one and many” assumes that it is possible to obtain information if a given oligonucleotide occurs in a reconstructed sequence once or more than once. According to the second model, called “one, two and many”, one is able to receive from biochemical experiment information if a given oligonucleotide is present in an analysed sequence once, twice or at least three times. An ant colony optimization algorithm has been implemented to verify the above models and to compare with existing algorithms for sequencing by hybridization which utilize the additional information. The proposed algorithm solves the problem with any kind of hybridization errors. Computational experiment results confirm that using even the partial information about multiplicity leads to increased quality of reconstructed sequences. Moreover, they also show that the more precise model enables to obtain better solutions and the ant colony optimization algorithm outperforms the existing ones. Test data sets and the proposed ant colony optimization algorithm are available on:
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      PubDate: 2016-01-31T02:20:42Z
  • Carcinogenicity prediction of noncongeneric chemicals by augmented top
           priority fragment classification
    • Abstract: Publication date: Available online 29 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Mosè Casalegno, Guido Sello
      Carcinogenicity prediction is an important process that can be performed to cut down experimental costs and save animal lives. The current reliability of the results is however disputed. Here, a blind exercise in carcinogenicity category assessment is performed using augmented top priority fragment classification. The procedure analyses the applicability domain of the dataset, allocates in clusters the compounds using a leading molecular fragment, and a similarity measure. The exercise is applied to three compound datasets derived from the Lois Gold Carcinogenic Database. The results, showing good agreement with experimental data, are compared with published ones. A final discussion on our viewpoint on the possibilities that the carcinogenicity modelling of chemical compounds offers is presented.
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      PubDate: 2016-01-31T02:20:42Z
  • On origin and evolution of carbonic anhydrase isozymes: A phylogenetic
           analysis from whole-enzyme to active site
    • Abstract: Publication date: Available online 23 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Srijoni Banerjee, Parag A. Deshpande
      Genetic evolution of carbonic anhydrase enzyme provides an interesting instance of functional similarity in spite of structural diversity of the members of a given family of enzymes. Phylogenetic analysis of α-, β- and γ-carbonic anhydrase was carried out to determine the evolutionary relationships among various members of the family with the enzyme marking its presence in a wide range of cellular and chromosomal locations. The presence of more than one classes of enzymes in a particular organism was revealed by phylogenetic time tree. The evolutionary relationships among the members of animal, plant and microbial kingdom were developed. The study revises a long-established notion of kingdom-specificity of the different classes of carbonic anhydrases and provides a new version of the presence of multiple classes of carbonic anhydrases in a single organism and the presence of a given class of carbonic anhydrase across different kingdoms.
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      PubDate: 2016-01-25T14:15:05Z
  • Identification of possible siRNA Molecules for TDP43 Mutants causing
           Amyotrophic Lateral Sclerosis: in silico design and molecular dynamics
    • Abstract: Publication date: Available online 23 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Vishwambhar Vishnu Bhandare, Amutha Ramaswamy
      The DNA binding protein, TDP43 is a major protein involved in Amyotrophic Lateral Sclerosis and other neurological disorders such as Frontotemporal dementia, Alzheimer disease, etc. In the present study, we have designed possible siRNAs for the glycine rich region of tardbp mutants causing ALS disorder based on a systematic theoretical approach including (i) identification of respective codons for all mutants (reported at the protein level) based on both minimum free energy and probabilistic approaches, (ii) rational design of siRNA, (iii) secondary structure analysis for the target accessibility of siRNA, (iii) determination of the ability of siRNA to interact with mRNA and the formation/stability of duplex via molecular dynamics study for a period of 15ns and (iv) characterization of mRNA-siRNA duplex stability based on thermo-physical analysis. The stable GC-rich siRNA expressed strong binding affinity towards mRNA and forms stable duplex in A-form. The linear dependence between the thermo-physical parameters such as Tm, GC content and binding free energy revealed the ability of the identified siRNAs to interact with mRNA in comparable to that of the experimentally reported siRNAs. Hence, this present study proposes few siRNAs as the possible gene silencing agents in RNAi therapy based on the in silico approach.
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      PubDate: 2016-01-25T14:15:05Z
  • Synthesis and In Silico Investigation of Thiazoles Bearing Pyrazoles
           Derivatives As Anti-Inflammatory Agents
    • Abstract: Publication date: Available online 23 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Rahul D. Kamble, Rohan J. Meshram, Shrikant V. Hese, Rahul A. More, Sonali S. Kamble, Rajesh N. Gacche, Bhaskar S. Dawane
      Searching novel, safe and effective anti-inflammatory agents has remained an evolving research enquiry in the mainstream of inflammatory disorders. In the present investigation series of thiazoles bearing pyrazole as a possible pharmacophore were synthesized and assessed for their anti inflammatory activity using in vitro and in vivo methods. In order to decipher the possible anti-inflammatory mechanism of action of the synthesized compounds, cyclooxygenase I and II (COX-I and COX-II) inhibition assays were also carried out. The results obtained clearly focus the significance of compounds 5d, 5 h and 5i as selective COX-II inhibitors. Moreover compound 5 h was also identified as a lead molecule for inhibition of the carrageenin induced rat paw edema in animal model studies. Molecular docking results revealed significant interactions of the test compounds with the active site of COX-II, which perhaps can be explored for design and development of novel COX-II selective anti-inflammatory agents.
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      PubDate: 2016-01-25T14:15:05Z
  • Insights into the functions of M-T hook structure in HIV fusion inhibitor
           using molecular modeling
    • Abstract: Publication date: Available online 23 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Jianjun Tan, Hongling Yuan, Chunhua Li, Xiaoyi Zhang, Cunxin Wang
      HIV-1 membrane fusion plays an important role in the process that HIV-1 entries host cells. As a treatment strategy targeting HIV-1 entry process, fusion inhibitors have been proposed. Nevertheless, development of a short peptide possessing high anti-HIV potency is considered a daunting challenge. He et al. found that two residues, Met626 and Thr627, located the upstream of the C-terminal heptad repeat of the gp41, formed a unique hook-like structure (M-T hook) that can dramatically improve the binding stability and anti-HIV activity of the inhibitors. In this work, we explored the molecular mechanism why M-T hook structure could improve the anti-HIV activity of inhibitors. Firstly, molecular dynamic simulation was used to obtain information on the time evolution between gp41 and ligands. Secondly, based on the simulations, molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and molecular mechanics Generalized Born surface area (MM-GBSA) methods were used to calculate the binding free energies. The binding free energy of the ligand with M-T hook was considerably higher than the other without M-T. Further studies showed that the hydrophobic interactions made the dominant contribution to the binding free energy. The numbers of Hydrogen bonds between gp41 and the ligand with M-T hook structure were more than the other. These findings should provide insights into the inhibition mechanism of the short peptide fusion inhibitors and be useful for the rational design of novel fusion inhibitors in the future.
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      PubDate: 2016-01-25T14:15:05Z
  • Investigation on the isoform selectivity of novel kinesin-like protein 1
           (KIF11) inhibitor using chemical feature based pharmacophore, molecular
           docking, and quantum mechanical studies
    • Abstract: Publication date: Available online 15 January 2016
      Source:Computational Biology and Chemistry
      Author(s): Subramanian Karunagaran, Subramaniyan Subhashchandrabose, Keun Woo Lee, Chandrasekaran Meganathan
      Kinesin-like protein (KIF11) is a molecular motor protein that is essential in mitosis. Removal of KIF11 prevents centrosome migration and causes cell arrest in mitosis. KIF11 defects are linked to the disease of microcephaly, lymph edema or mental retardation. The human KIF11 protein has been actively studied for its role in mitosis and its potential as a therapeutic target for cancer treatment. Pharmacophore modeling, molecular docking and density functional theory approaches was employed to reveal the structural, chemical and electronic features essential for the development of small molecule inhibitor for KIF11. Hence we have developed chemical feature based pharmacophore models using Discovery Studio v 2.5 (DS). The best hypothesis (Hypo1) consisting of four chemical features (two hydrogen bond acceptor, one hydrophobic and one ring aromatic) has exhibited high correlation co-efficient of 0.9521, cost difference of 70.63 and low RMS value of 0.9475. This Hypo1 is cross validated by Cat Scramble method; test set and decoy set to prove its robustness, statistical significance and predictability respectively. The well validated Hypo1 was used as 3Dquery to perform virtual screening. The hits obtained from the virtual screening were subjected to various scrupulous drug-like filters such as Lipinski's rule of five and ADMET properties. Finally, six hit compounds were identified based on the molecular interaction and its electronic properties. Our final lead compound could serve as a powerful tool for the discovery of potent inhibitor as KIF11 agonists.
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      PubDate: 2016-01-16T14:00:41Z
  • Dynamic conformational ensembles regulate casein kinase-1 isoforms:
           Insights from molecular dynamics and molecular docking studies
    • Abstract: Publication date: April 2016
      Source:Computational Biology and Chemistry, Volume 61
      Author(s): Surya Pratap Singh, Dwijendra K. Gupta
      Casein kinase-1 (CK1) isoforms actively participate in the down-regulation of canonical Wnt signaling pathway; however recent studies have shown their active roles in oncogenesis of various tissues through this pathway. Functional loss of two isoforms (CK1-α/ε) has been shown to activate the carcinogenic pathway which involves the stabilization of of cytoplasmic β-catenin. Development of anticancer therapeutics is very laborious task and depends upon the structural and conformational details of the target. This study focuses on, how the structural dynamics and conformational changes of two CK1 isoforms are synchronized in carcinogenic pathway. The conformational dynamics in kinases is the responsible for their action as has been supported by the molecular docking experiments.
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      PubDate: 2016-01-13T13:55:49Z
  • Comparison of Non-Sequential Sets of Protein Residues
    • Abstract: Publication date: Available online 25 December 2015
      Source:Computational Biology and Chemistry
      Author(s): Leonardo D. Garma, André H. Juffer
      A methodology for performing sequence-free comparison of functional sites in protein structures is introduced. The method is based on a new notion of similarity among superimposed groups of amino acid residues that evaluates both geometry and physico-chemical properties. The method is specifically designed to handle disconnected and sparsely distributed sets of residues. A genetic algorithm is employed to find the superimposition of protein segments that maximizes their similarity. The method was evaluated by performing an all-to-all comparison on two separate sets of ligand-binding sites, comprising 47 protein-FAD (Flavin-Adenine Dinucleotide) and 64 protein-NAD (Nicotinamide-Adenine Dinucleotide) complexes, and comparing the results with those of an existing sequence-based structural alignment tool (TM-Align). The quality of the two methodologies is judged by the methods’ capacity to, among other, correctly predict the similarities in the protein-ligand contact patterns of each pair of binding sites. The results show that using a sequence-free method significantly improves over the sequence-based one, resulting in 23 significant binding-site homologies being detected by the new method but ignored by the sequence-based one.

      PubDate: 2015-12-27T13:03:28Z
  • Estimation of kinetic parameters of transcription from temporal single-RNA
    • Abstract: Publication date: Available online 17 December 2015
      Source:Computational Biology and Chemistry
      Author(s): Christoph Zimmer, Antti Häkkinen, Andre S. Ribeiro
      Gene expression dynamics in prokaryotes is largely controlled by the multi-step process of transcription initiation whose kinetics is subject to regulation. Since the number and duration of these steps cannot be currently measured in vivo, we propose a novel method for estimating them from time series of RNA numbers in individual cells. We demonstrate the method's applicability on measurements of fluorescence-tagged RNA molecules in E. coli cells, and compare with a previous method. We show that the results of the two methods agree for equal data. We also show that, when incorporating additional data, the new method produces significantly different estimates, which are in closer agreement with qPCR measurements. Unlike the previous method, the new method requires no preprocessing of the RNA numbers, using maximal information from the RNA time series. In addition, it can use data outside of the observed RNA productions. Overall, the new method characterizes the transcription initiation process with enhanced detail.
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      PubDate: 2015-12-19T12:49:51Z
  • Molecular Design and Structural Optimization of Potent Peptide Hydroxamate
           Inhibitors to Selectively Target Human ADAM Metallopeptidase Domain 17
    • Abstract: Publication date: Available online 8 December 2015
      Source:Computational Biology and Chemistry
      Author(s): Zhengting Wang, Lei Wang, Rong Fan, Jie Zhou, Jie Zhong
      Human ADAMs (a disintegrin and metalloproteinases) have been established as an attractive therapeutic target of inflammatory disorders such as inflammatory bowel disease (IBD). The ADAM metallopeptidase domain 17 (ADAM17 or TACE) and its close relative ADAM10 are two of the most important ADAM members that share high conservation in sequence, structure and function, but exhibit subtle difference in regulation of downstream cell signaling events. Here, we described a systematic protocol that combined computational modeling and experimental assay to discover novel peptide hydroxamate derivatives as potent and selective inhibitors for ADAM17 over ADAM10. In the procedure, a virtual combinatorial library of peptide hydroxamate compounds was generated by exploiting intermolecular interactions involved in crystal and modeled structures. The library was examined in detail to identify few promising candidates with both high affinity to ADAM17 and low affinity to ADAM10, which were then tested in vitro with enzyme inhibition assay. Consequently, two peptide hydroxamates Hxm-Phe-Ser-Asn and Hxm-Phe-Arg-Gln were found to exhibit potent inhibition against ADAM17 (K i =92 and 47nM, respectively) and strong selectivity for ADAM17 over ADAM10 (∼7-fold and ∼5-fold, S =0.86 and 0.71, respectively). The structural basis and energetic property of ADAM17 and ADAM10 interactions with the designed inhibitors were also investigated systematically. It is found that the exquisite network of nonbonded interactions involving the side chains of peptide hydroxamates is primarily responsible for inhibitor selectivity, while the coordination interactions and hydrogen bonds formed by the hydroxamate moiety and backbone of peptide hydroxamates confer high affinity to inhibitor binding.
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      PubDate: 2015-12-12T12:42:10Z
  • Transcriptome-wide identification of Rauvolfia serpentina microRNAs and
           prediction of their potential targets
    • Abstract: Publication date: Available online 9 December 2015
      Source:Computational Biology and Chemistry
      Author(s): Pravin Prakash, Raja Rajakani, Vikrant Gupta
      MicroRNAs (miRNAs) are small non-coding RNAs of ∼19–24 nucleotides (nt) in length and considered as potent regulators of gene expression at transcriptional and post-transcriptional levels. Here we report the identification and characterization of 15 conserved miRNAs belonging to 13 families from Rauvolfia serpentina through in silico analysis of available nucleotide dataset. The identified mature R. serpentina miRNAs (rse-miRNAs) ranged between 20 and 22 nt in length, and the average minimal folding free energy index (MFEI) value of rse-miRNA precursor sequences was found to be –0.815kcal/mol. Using the identified rse-miRNAs as query their potential targets were predicted in R. serpentina and other plant species. Gene Ontology (GO) annotation showed that predicted targets of rse-miRNAs include transcription factors as well as genes involved in diverse biological processes such as primary and secondary metabolism, stress response, disease resistance, growth, and development. Few rse-miRNAs were predicted to target genes of pharmaceutically important secondary metabolic pathways such as alkaloids and anthocyanin biosynthesis. Phylogenetic analysis showed the evolutionary relationship of rse-miRNAs and their precursor sequences to homologous pre-miRNA sequences from other plant species. The findings under present study besides giving first hand information about R. serpentina miRNAs and their targets, also contributes towards the better understanding of miRNA-mediated gene regulatory processes in plants.
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      PubDate: 2015-12-12T12:42:10Z
  • Analysis of Molecular Structures and Mechanisms for Toxins Derived from
           Venomous Animals
    • Abstract: Publication date: Available online 2 December 2015
      Source:Computational Biology and Chemistry
      Author(s): L.F.O. Rocha
      As predominant component in the venom of many dangerous animal species, toxins have been thoroughly investigated for drug design or as pharmacologic tools. The present study demonstrated the use of size and hydrophobicity of amino acid residues for the purposes of quantifying the valuable sequence–structure relationship and performing further analysis of interactional mechanisms in secondary structure elements (SSEs) for toxin native conformations. First, we showed that the presence of large and hydrophobic residues varying in availability in the primary sequences correspondingly affects the amount of these residues being used in the SSEs in accordance with linear behavioral patterns from empirical assessments of experimentally derived toxins and non-toxins. Subsequent derivation of prediction rules was established with the aim of analyzing molecular structures and mechanisms by means of 114 residue compositions for venom toxins. The obtained results concerning the linear behavioral patterns demonstrated the nature of the information transfer occurring from the primary to secondary structures. A dual action mechanism was established, taking into account steric and hydrophobic interactions. Finally, a new residue composition prediction method for SSEs of toxins was suggested.
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      PubDate: 2015-12-04T12:32:11Z
  • Deceptive responsive genes in gel-based proteomics
    • Abstract: Publication date: Available online 3 December 2015
      Source:Computational Biology and Chemistry
      Author(s): Sara Hamzelou, Hossein Askari, Nona Abolfathi Nobari
      The standard method of the global quantitative analysis of gene expression at the protein level combines high-resolution two-dimensional gel electrophoresis (2DE) with mass spectrometric identification of protein spots. One of the major concerns with the application of gel-based proteomics is the need for the analytical and biological accuracy of the datasets. We mathematically and empirically simulated the possibility of the technical regulations of gene expression using 2DE. Our developed equation predicted a detectable alteration in the quantity of protein spots in response to a new protein added in, with various amounts. Testing the predictability of the developed equation, we observed that a new protein could form deceptive expression profiles, classified using prevalent tools for the analysis of 2DE results. In spite of the theoretically predicted overall reduction of proteins that resulted from adding the new protein, the empirical data revealed differential amount of proteins when various quantities of the new protein were added to the protein sample. The present work emphasize that employment of 2DE would not be a reliable approach for biological samples with extensive proteome alterations such as the developmental and differentiation stages of cells without depletion of high abundant proteins.
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      PubDate: 2015-12-04T12:32:11Z
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