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CHEMICAL ENGINEERING (191 journals)                     

Showing 1 - 191 of 191 Journals sorted alphabetically
AATCC Journal of Research     Full-text available via subscription   (Followers: 6)
ACS Sustainable Chemistry & Engineering     Hybrid Journal   (Followers: 5)
Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials     Hybrid Journal   (Followers: 5)
Acta Polymerica     Hybrid Journal   (Followers: 9)
Additives for Polymers     Full-text available via subscription   (Followers: 20)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 7)
Advanced Chemical Engineering Research     Open Access   (Followers: 30)
Advanced Powder Technology     Hybrid Journal   (Followers: 16)
Advances in Applied Ceramics     Hybrid Journal   (Followers: 5)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 24)
Advances in Chemical Engineering and Science     Open Access   (Followers: 53)
Advances in Polymer Technology     Hybrid Journal   (Followers: 13)
African Journal of Pure and Applied Chemistry     Open Access   (Followers: 7)
Annual Review of Analytical Chemistry     Full-text available via subscription   (Followers: 10)
Annual Review of Chemical and Biomolecular Engineering     Full-text available via subscription   (Followers: 12)
Anti-Corrosion Methods and Materials     Hybrid Journal   (Followers: 8)
Applied Petrochemical Research     Open Access   (Followers: 2)
Asia-Pacific Journal of Chemical Engineering     Hybrid Journal   (Followers: 7)
Biochemical Engineering Journal     Hybrid Journal   (Followers: 15)
Biofuel Research Journal     Open Access   (Followers: 4)
Biomass Conversion and Biorefinery     Partially Free   (Followers: 10)
Brazilian Journal of Chemical Engineering     Open Access   (Followers: 3)
Bulletin of Chemical Reaction Engineering & Catalysis     Open Access   (Followers: 2)
Bulletin of the Chemical Society of Ethiopia     Open Access   (Followers: 2)
Carbohydrate Polymers     Hybrid Journal   (Followers: 8)
Catalysts     Open Access   (Followers: 7)
ChemBioEng Reviews     Full-text available via subscription   (Followers: 1)
Chemical and Engineering News     Free   (Followers: 12)
Chemical and Materials Engineering     Open Access   (Followers: 12)
Chemical and Petroleum Engineering     Hybrid Journal   (Followers: 12)
Chemical and Process Engineering     Open Access   (Followers: 26)
Chemical and Process Engineering Research     Open Access   (Followers: 23)
Chemical Engineering & Technology     Hybrid Journal   (Followers: 32)
Chemical Engineering and Processing: Process Intensification     Hybrid Journal   (Followers: 17)
Chemical Engineering and Science     Open Access   (Followers: 18)
Chemical Engineering Communications     Hybrid Journal   (Followers: 14)
Chemical Engineering Education     Full-text available via subscription  
Chemical Engineering Journal     Hybrid Journal   (Followers: 34)
Chemical Engineering Research and Design     Hybrid Journal   (Followers: 23)
Chemical Engineering Research Bulletin     Open Access   (Followers: 11)
Chemical Engineering Science     Hybrid Journal   (Followers: 26)
Chemical Geology     Hybrid Journal   (Followers: 18)
Chemical Papers     Hybrid Journal   (Followers: 2)
Chemical Product and Process Modeling     Hybrid Journal   (Followers: 4)
Chemical Reviews     Full-text available via subscription   (Followers: 173)
Chemical Society Reviews     Full-text available via subscription   (Followers: 41)
Chemical Technology     Open Access   (Followers: 15)
ChemInform     Hybrid Journal   (Followers: 8)
Chemistry & Industry     Hybrid Journal   (Followers: 5)
Chemistry Central Journal     Open Access   (Followers: 4)
Chemistry of Materials     Full-text available via subscription   (Followers: 193)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 15)
ChemSusChem     Hybrid Journal   (Followers: 7)
Chinese Chemical Letters     Full-text available via subscription   (Followers: 2)
Chinese Journal of Chemical Engineering     Full-text available via subscription   (Followers: 4)
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: 12)
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: 20)
Corrosion Engineering, Science and Technology     Hybrid Journal   (Followers: 35)
Corrosion Reviews     Hybrid Journal   (Followers: 4)
Crystal Research and Technology     Hybrid Journal   (Followers: 6)
Current Opinion in Chemical Engineering     Open Access   (Followers: 7)
Education for Chemical Engineers     Hybrid Journal   (Followers: 5)
Eksergi     Open Access  
Emerging Trends in Chemical Engineering     Full-text available via subscription   (Followers: 3)
European Polymer Journal     Hybrid Journal   (Followers: 41)
Fibers and Polymers     Full-text available via subscription   (Followers: 6)
Fluorescent Materials     Open Access   (Followers: 1)
Focusing on Modern Food Industry     Open Access   (Followers: 2)
Frontiers of Chemical Science and Engineering     Hybrid Journal   (Followers: 2)
Gels     Open Access  
Geochemistry International     Hybrid Journal   (Followers: 2)
Handbook of Powder Technology     Full-text available via subscription   (Followers: 6)
Heat Exchangers     Open Access   (Followers: 3)
High Performance Polymers     Hybrid Journal   (Followers: 1)
Hungarian Journal of Industry and Chemistry     Open Access  
Indian Chemical Engineer     Hybrid Journal   (Followers: 5)
Indian Journal of Chemical Technology (IJCT)     Open Access   (Followers: 10)
Indonesian Journal of Chemical Science     Open Access   (Followers: 1)
Industrial & Engineering Chemistry     Full-text available via subscription   (Followers: 11)
Industrial & Engineering Chemistry Research     Full-text available via subscription   (Followers: 21)
Industrial Chemistry Library     Full-text available via subscription   (Followers: 3)
Industrial Gases     Open Access  
Info Chimie Magazine     Full-text available via subscription   (Followers: 3)
International Journal of Chemical and Petroleum Sciences     Open Access   (Followers: 3)
International Journal of Chemical Engineering     Open Access   (Followers: 7)
International Journal of Chemical Reactor Engineering     Hybrid Journal   (Followers: 3)
International Journal of Chemical Technology     Open Access   (Followers: 5)
International Journal of Chemoinformatics and Chemical Engineering     Full-text available via subscription   (Followers: 2)
International Journal of Food Science     Open Access   (Followers: 3)
International Journal of Industrial Chemistry     Open Access   (Followers: 1)
International Journal of Polymeric Materials     Hybrid Journal   (Followers: 5)
International Journal of Science and Engineering     Open Access   (Followers: 3)
International Journal of Waste Resources     Open Access   (Followers: 4)
Journal of Chemical Engineering & Process Technology     Open Access   (Followers: 5)
Journal of Applied Crystallography     Hybrid Journal   (Followers: 6)
Journal of Applied Electrochemistry     Hybrid Journal   (Followers: 12)
Journal of Applied Polymer Science     Hybrid Journal   (Followers: 122)
Journal of Biomaterials Science, Polymer Edition     Hybrid Journal   (Followers: 9)
Journal of Bioprocess Engineering and Biorefinery     Full-text available via subscription   (Followers: 1)
Journal of Chemical & Engineering Data     Full-text available via subscription   (Followers: 11)
Journal of Chemical and Biological Interfaces     Full-text available via subscription   (Followers: 1)
Journal of Chemical Ecology     Hybrid Journal   (Followers: 6)
Journal of Chemical Engineering     Open Access   (Followers: 19)
Journal of Chemical Engineering and Materials Science     Open Access   (Followers: 2)
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: 15)
Journal of CO2 Utilization     Hybrid Journal   (Followers: 2)
Journal of Combinatorial Chemistry     Full-text available via subscription  
Journal of Crystallization Process and Technology     Open Access   (Followers: 8)
Journal of Environmental Chemical Engineering     Hybrid Journal   (Followers: 5)
Journal of Food Measurement and Characterization     Hybrid Journal  
Journal of Food Processing & Technology     Open Access   (Followers: 1)
Journal of Fuel Chemistry and Technology     Full-text available via subscription   (Followers: 4)
Journal of Geochemical Exploration     Hybrid Journal   (Followers: 1)
Journal of Industrial and Engineering Chemistry     Hybrid Journal   (Followers: 1)
Journal of Information Display     Hybrid Journal   (Followers: 1)
Journal of Inorganic and Organometallic Polymers and Materials     Partially Free   (Followers: 9)
Journal of Modern Chemistry & Chemical Technology     Full-text available via subscription   (Followers: 2)
Journal of Molecular Catalysis A: Chemical     Hybrid Journal   (Followers: 6)
Journal of Non-Crystalline Solids     Hybrid Journal   (Followers: 8)
Journal of Organic Semiconductors     Open Access   (Followers: 5)
Journal of Physics and Chemistry of Solids     Hybrid Journal   (Followers: 5)
Journal of Polymer and Biopolymer Physics Chemistry     Open Access   (Followers: 5)
Journal of Polymer Engineering     Hybrid Journal   (Followers: 9)
Journal of Polymer Research     Hybrid Journal   (Followers: 6)
Journal of Polymer Science Part C : Polymer Letters     Hybrid Journal   (Followers: 6)
Journal of Polymers     Open Access   (Followers: 5)
Journal of Polymers and the Environment     Hybrid Journal   (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: 295)
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: 9)
Jurnal Bahan Alam Terbarukan     Open Access  
Jurnal Inovasi Pendidikan Kimia     Open Access   (Followers: 5)
Jurnal Reaktor     Open Access  
Jurnal Teknologi Dan Industri Pangan     Open Access   (Followers: 1)
Konversi     Open Access  
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: 16)
Materials Science and Applied Chemistry     Open Access  
Materials Sciences and Applied Chemistry     Full-text available via subscription  
Modern Chemistry & Applications     Open Access  
Molecular Imprinting     Open Access  
Nanochemistry Research     Open Access  
Nanocontainers     Open Access  
Nanofabrication     Open Access  
Noise Control Engineering Journal     Full-text available via subscription   (Followers: 4)
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: 4)
Plasma Processes and Polymers     Hybrid Journal   (Followers: 2)
Plasmas and Polymers     Hybrid Journal  
Polymer     Hybrid Journal   (Followers: 121)
Polymer Bulletin     Hybrid Journal   (Followers: 7)
Polymer Composites     Hybrid Journal   (Followers: 15)
Polyolefins Journal     Open Access  
Powder Technology     Hybrid Journal   (Followers: 13)
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: 60)
Solid Fuel Chemistry     Hybrid Journal  
South African Journal of Chemical Engineering     Open Access   (Followers: 2)
South African Journal of Chemistry     Open Access   (Followers: 2)
Surface Engineering and Applied Electrochemistry     Hybrid Journal   (Followers: 5)
Sustainable Chemical Processes     Open Access   (Followers: 2)
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: 4)
Transylvanian Review of Systematical and Ecological Research     Open Access  
Visegrad Journal on Bioeconomy and Sustainable Development     Open Access   (Followers: 2)
Zeitschrift für Naturforschung B : A Journal of Chemical Sciences     Open Access   (Followers: 1)


Journal Cover Computational Biology and Chemistry
  [SJR: 0.491]   [H-I: 47]   [12 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1476-9271
   Published by Elsevier Homepage  [3034 journals]
  • Sphingosine kinase 1 (SK1) allosteric inhibitors that target the
           dimerization site
    • Abstract: Publication date: August 2017
      Source:Computational Biology and Chemistry, Volume 69
      Author(s): Ozge Bayraktar, Elif Ozkirimli, Kutlu Ulgen
      The sphingosine kinase 1 (SK1)/sphingosine-1-phosphate (S1P) signaling pathway is a crucial target for numerous human diseases from cancer to cardiovascular diseases. However, available SK1 inhibitors that target the active site suffer from poor potency, selectivity and pharmacokinetic properties. The selectivity issue of the kinases, which share a highly-conserved ATP-pocket, can be overcome by targeting the less-conserved allosteric sites. SK1 is known to function minimally as a dimer; however, the crystal structure of the SK1 dimer has not been determined. In this study, a template-based algorithm implemented in PRISM was used to predict the SK1 dimer structure and then the possible allosteric sites at the dimer interface were determined via SiteMap. These sites were used in a virtual screening campaign that includes an integrated workflow of structure-based pharmacophore modeling, virtual screening, molecular docking, re-screening of common scaffolds to propose a series of compounds with different scaffolds as potential allosteric SK1 inhibitors. Finally, the stability of the SK1-ligand complexes was analyzed by molecular dynamics simulations. As a final outcome, ligand 7 having a 4,9-dihydro-1H-purine scaffold and ligand 12 having a 2,3,4,9-tetrahydro-1H-β-carboline scaffold were found to be potential selective inhibitors for SK1.
      Graphical abstract image

      PubDate: 2017-06-04T14:43:17Z
  • Differentiating the pre-hydrolysis states of wild-type and A59G mutant
           HRas: An insight through MD simulations
    • Abstract: Publication date: Available online 1 June 2017
      Source:Computational Biology and Chemistry
      Author(s): Neeru Sharma, Uddhavesh Sonavane, Rajendra Joshi
      The most representative member of the Ras subfamily is its HRas isoform. Ras proteins being GTPases, possess an intrinsic activity to hydrolyze the GTP molecule to GDP. During the transition phases, between active and inactive states, P-loop and switch regions show maximum variations. Various hot-spot Ras mutants (G12V, A59G, Q61L etc) have been reported, that limit the protein's conformation in the permanent active state. In the present study, we aim to explore the structural dynamics of one such crucial mutant of Ras namely A59G which belongs to the conserved Switch II region of the protein. Approximately ∼15μs of Classical Molecular Dynamics (CMD) simulations have been carried out on the mutant and wild-type complexes. Further, a metadynamics simulation of 500ns was also carried out, which suggests an energy barrier of ∼9.56kcal/mol between wild-type and mutant conformation. We demonstrate the role of water molecule in maintaining the required interaction networks in the pre-hydrolysis state, its impact on A59G mutation, distinct orientation of the Gln61 residue in two conformations, disruption of crucial Gly60 and γ phosphate and the change in the Switch II region. The outcome of our study captures the pre-hydrolysis state of the HRas protein. It also establishes the fact that this mutation makes the movement of Switch II region and the conserved DXXGQ motif highly constrained, which is known to be an important requirement for hydrolysis. This suggests that the A59G mutation may decrease the rate of intrinsic hydrolysis as well as GAP-mediated hydrolysis.
      Graphical abstract image

      PubDate: 2017-06-04T14:43:17Z
  • Modelling toxin effects on protein biosynthesis in eukaryotic cells
    • Abstract: Publication date: Available online 31 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Vladas Skakauskas, Pranas Katauskis
      We present a rather generic model for toxin (ricin) inhibition of protein biosynthesis in eukaryotic cells. We also study reduction of the ricin toxic effects with application of antibodies against the RTB subunit of ricin molecules. Both species initially are delivered extracellularly. The model accounts for the pinocytotic and receptor-mediated toxin endocytosis and the intact toxin exocytotic removal out of the cell. The model also includes the lysosomal toxin destruction, the intact toxin motion to the endoplasmic reticulum (ER) for separation of its molecules into the RTA and RTB subunits, and the RTA chain translocation into the cytosol. In the cytosol, one portion of the RTA undergoes degradation via the ERAD. The other its portion can inactivate ribosomes at a large rate. The model is based on a system of deterministic ODEs. The influence of the kinetic parameters on the protein concentration and antibody protection factor is studied in detail.
      Graphical abstract image Highlights

      PubDate: 2017-06-04T14:43:17Z
  • Improving Virtual Screening Predictive Accuracy of Human Kallikrein 5
           inhibitors using Machine Learning Models
    • Abstract: Publication date: Available online 29 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Xingang Fang, Sikha Bagui, Subhash Bagui
      The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets.
      Graphical abstract image

      PubDate: 2017-06-04T14:43:17Z
  • Codon usage bias and its influencing factors for Y-linked genes in human
    • Abstract: Publication date: Available online 27 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Monisha Nath Choudhury, Arif Uddin, Supriyo Chakraborty
      The non-uniform usage of synonymous codons during translation of a protein is the codon usage bias and is mainly influenced by natural selection and mutation pressure. We have used bioinformatic approaches to analyze codon usage bias of human Y-linked genes. Effective number of codon (ENC) suggested that the overall extent of codon usage bias of genes was low. The relative synonymous codon usage (RSCU) analysis revealed that AGA and CTG codons were over-represented in Y-linked genes. Compositional constraint under mutation pressure influenced the codon usage pattern as revealed by the correspondence analysis (COA). Parity plot suggests that both natural selection and mutation pressure might have influenced the codon usage bias of Y-linked genes.
      Graphical abstract image

      PubDate: 2017-05-29T15:59:05Z
  • In silico identification of vaccine candidates against Klebsiella oxytoca
    • Abstract: Publication date: Available online 22 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Sandipan Talukdar, Udeshna Bayan, Kandarpa Kr. Saikia
      Klebsiella oxytoca causes several diseases in immunocompromised as well as healthy individuals. Increasing resistance to a number of antibiotics makes treatment options limited. Prevention using vaccine could be an important solution to get rid of infections caused by Klebsiella oxytoca. In recent time, genome based approaches have contributed significantly in vaccine development. Our aim was to identify the most conserved and immunogenic antigens that can be considered as potential vaccine candidates. KEGG database was used to find out pathways unique to the bacteria. Subcellular localization of the protein sequences taken from the selected 36 pathways were predicted using PSORTb v3.0.2 and CELLO v2.5. Prediction of B cell epitope and the probability of the antigenicity were evaluated by using IEDB and Vaxijen respectively. BLASTp was done to find out the similarity of the selected proteins with the human proteome. Proteins failing to comply with the set parameters were filtered at each step. Finally, we identified 6 surface exposed proteins as potential vaccine candidates against Klebsiella oxytoca.
      Graphical abstract image

      PubDate: 2017-05-24T15:42:00Z
  • Extra precision docking, free energy calculation and molecular dynamics
           studies on glutamic acid derivatives as MurD inhibitors
    • Abstract: Publication date: Available online 22 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Mohammed Afzal Azam, Srikanth Jupudi
      The binding modes of well known MurD inhibitors have been studied using molecular docking and molecular dynamics (MD) simulations. The docking results of inhibitors 1-30 revealed similar mode of interaction with Escherichia coli-MurD. Further, residues Thr36, Arg37, His183, Lys319, Lys348, Thr321, Ser415 and Phe422 are found to be important for inhibitors and E. coli-MurD interactions. Our docking procedure precisely predicted crystallographic bound inhibitor 7 as evident from root mean square deviation (0.96Å). In addition inhibitors 2 and 3 have been successfully cross-docked within the MurD active site, which was pre-organized for the inhibitor 7. Induced fit best docked poses of 2, 3, 7 and 15/2Y1O complexes were subjected to 10ns MD simulations to determine the stability of the predicted binding conformations. Induce fit derived docked complexes were found to be in a state of near equilibrium as evident by the low root mean square deviations between the starting complex structure and the energy minimized final average MD complex structures. The results of molecular docking and MD simulations described in this study will be useful for the development of new MurD inhibitors with high potency.
      Graphical abstract image

      PubDate: 2017-05-24T15:42:00Z
  • A novel wearable device for continuous, non-invasion blood pressure
    • Abstract: Publication date: Available online 19 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Qin Xin, Jianping Wu
      In this paper, we have developed a wearable cuffless device for daily blood pressure (BP) measurement. We incorporated the light based sensor and other hard wares in a small volume for BP detection. With optimized algorithm, the real-time BP reading could be achieved, the data could be presented in the screen and be transmitted by internet of things (IoT) for history data comparison and multi-terminal viewing. Thus, further analysis provides the probability for diet or sports suggestion and alarm. We have measured BP from more than 60 subjects, compare to traditional mercury blood pressure meter, no obvious error in both systolic blood pressure (SBP) and diastolic blood pressure (DBP) are detected. Such device can be used for continues non-invasion BP detection, and further data docking and health analysis could be achieved.

      PubDate: 2017-05-24T15:42:00Z
  • IFC Editorial Board
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68

      PubDate: 2017-05-14T15:31:41Z
  • Title page
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68

      PubDate: 2017-05-14T15:31:41Z
  • Construction of new EST-SSRs for Fusarium resistant wheat breeding
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Aysen Yumurtaci, Hulya Sipahi, Ayed Al-Abdallat, Abdulqader Jighly, Michael Baum
      Surveying Fusarium resistance in wheat with easy applicable molecular markers such as simple sequence repeats (SSRs) is a prerequest for molecular breeding. Expressed sequence tags (ESTs) are one of the main sources for development of new SSR candidates. Therefore, 18.292 publicly available wheat ESTs were mined and genotyping of newly developed 55 EST-SSR derived primer pairs produced clear fragments in ten wheat cultivars carrying different levels of Fusarium resistance. Among the proved markers, 23 polymorphic EST-SSRs were obtained and related alleles were mostly found on B and D genome. Based on the fragment profiling and similarity analysis, a 327bp amplicon, which was a product of contig 1207 (chromosome 5BL), was detected only in Fusarium head blight (FHB) resistant cultivars (CM82036 and Sumai) and the amino acid sequences showed a similarity to pathogen related proteins. Another FHB resistance related EST-SSR, Contig 556 (chromosome 1BL) produced a 151bp fragment in Sumai and was associated to wax2-like protein. A polymorphic 204bp fragment, derived from Contig 578 (chromosome 1DL), was generated from root rot (FRR) resistant cultivars (2–49; Altay2000 and Sunco). A total of 98 alleles were displayed with an average of 1.8 alleles per locus and the polymorphic information content (PIC) ranged from 0.11 to 0.78. Dendrogram tree with two main and five sub-groups were displayed the highest genetic relationship between FRR resistant cultivars (2–49 and Altay2000), FRR sensitive cultivars (Seri82 and Scout66) and FHB resistant cultivars (CM82036 and Sumai). Thus, exploitation of these candidate EST-SSRs may help to genotype other wheat sources for Fusarium resistance.
      Graphical abstract image

      PubDate: 2017-05-14T15:31:41Z
  • Subcellular localization based comparative study on radioresistant
           bacteria: A novel approach to mine proteins involve in radioresistance
    • Abstract: Publication date: Available online 10 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Divya Vishambra, Malay Srivastava, Kamal Dev, Varun Jaiswal
      Radioresistant bacteria (RRB) are among the most radioresistant organisms and has a unique role in evolution. Along with the evolutionary role, radioresistant organisms play important role in paper industries, bioremediation, vaccine development and possibility in anti-ageing and anti-cancer treatment. The study of radiation resistance in RRB was mainly focused on cytosolic mechanisms such as DNA repair mechanism, cell cleansing activity and high antioxidant activity. Although it was known that protein localized on outer areas of cell play role in resistance towards extreme condition but the mechanisms/proteins localized on the outer area of cells are not studied for radioresistance. Considering the fact that outer part of cell is more exposed to radiations and proteins present in outer area of the cell may have role in radioresistance. Localization based comparative study of proteome from RRB and non-radio resistant bacteria was carried out. In RRB 20 unique proteins have been identified. Further domain, structural, and pathway analysis of selected proteins were carried out. Out of 20 proteins, 8 proteins were direct involvement in radioresistance and literature study strengthens this, however, 1 proteins had assumed relation in radioresistance. Selected radioresistant proteins may be helpful for optimal use of RRB in industry and health care.

      PubDate: 2017-05-14T15:31:41Z
  • 3D-QSAR studies of some reversible Acetyl cholinesterase inhibitors based
           on CoMFA and ligand protein interaction fingerprints using PC-LS-SVM and
    • Abstract: Publication date: Available online 10 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Hamidreza Ghafouri, Mohsen Ranjbar, Amirhossein Sakhteman
      A great challenge in medicinal chemistry is to develop different methods for structural design based on the pattern of the previously synthesized compounds. In this study two different QSAR methods were established and compared for a series of piperidine acetylcholinesterase inhibitors. In one novel approach, PC-LS-SVM and PLS-LS-SVM was used for modeling 3D interaction descriptors, and in the other method the same nonlinear techniques were used to build QSAR equations based on field descriptors. Different validation methods were used to evaluate the models and the results revealed the more applicability and predictive ability of the model generated by field descriptors (Q2 LOO-CV =1, R2 ext =0.97). External validation criteria revealed that both methods can be used in generating reasonable QSAR models. It was concluded that due to ability of interaction descriptors in prediction of binding mode, using this approach can be implemented in future 3D-QSAR softwares.
      Graphical abstract image

      PubDate: 2017-05-14T15:31:41Z
  • Molecular characterization of pigeon torque teno virus (PTTV) in Jiangsu
    • Abstract: Publication date: Available online 6 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Zhingcheng Zhang, Wei Dai, Dingzhen Dai
      The torque teno virus (TTV) is a recently discovered DNA virus that has been detected in many different hosts, including humans, livestock and poultry. To date, there is no report of pigeon TTV (PTTV) from anywhere in the world. To investigate the distribution of PTTV in pigeons from the eastern Chinese province of Jiangsu and characterize their genomes, we employed PCR to detect PTTV in 144 samples collected from 6 pigeon plants in Jiangsu province, amplify complete genomes from representative samples and analyze genetic characteristics using bioinformatics. The results demonstrated that 71.5% (103/144) of samples were PTTV positive. The rate of sequence homology among the six PTTV complete genomes obtained from Jiangsu province ranged from 99.7% to 100%. Phylogenetic analysis suggested that PTTV genomes had a high degree of genetic similarity and were similar to chicken anemia virus that also had poultry as a host. Although with the same host, PTTV shared distant relationship with PiCV in both complete genome, Rep and Cap genes. The results of this study provided evidence that PTTV could be detected in Chinese pigeons at a high level, the evolutionary process of complete genome, Rep and Cap genes of Anelloviridae family had obvious divergence.
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      PubDate: 2017-05-09T15:11:48Z
  • Discovering DNA methylation patterns for long non-coding RNAs associated
           with cancer subtypes
    • Abstract: Publication date: Available online 4 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Xiaoke Ma, Liang Yu, Peizhuo Wang, Xiaofei Yang
      Despite growing evidence demonstrates that the long non-coding ribonucleic acids (lncRNAs) are critical modulators for cancers, the knowledge about the DNA methylation patterns of lncRNAs is quite limited. We develop a systematic analysis pipeline to discover DNA methylation patterns for lncRNAs across multiple cancer subtypes from probe, gene and network levels. By using The Cancer Genome Atlas (TCGA) breast cancer methylation data, the pipeline discovers various DNA methylation patterns for lncRNAs across four major subtypes such as luminal A, luminal B, her2-enriched as well as basal-like. On the probe and gene level, we find that both differentially methylated probes and lncRNAs are subtype specific, while the lncRNAs are not as specific as probes. On the network level, the pipeline constructs differential co-methylation lncRNA network for each subtype. Then, it identifies both subtype specific and common lncRNA modules by simultaneously analyzing multiple networks. We show that the lncRNAs in subtype specific and common modules differ greatly in terms of topological structure, sequence conservation as well as expression. Furthermore, the subtype specific lncRNA modules serve as biomarkers to improve significantly the accuracy of breast cancer subtypes prediction. Finally, the common lncRNA modules associate with survival time of patients, which is critical for cancer therapy.
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      PubDate: 2017-05-09T15:11:48Z
  • PECC: correcting contigs based on paired-end read distribution
    • Abstract: Publication date: Available online 1 May 2017
      Source:Computational Biology and Chemistry
      Author(s): Min Li, Binbin Wu, Xiaodong Yan, Junwei Luo, Yi Pan, Fang-Xiang Wu, Jianxin Wang
      Motivation Cheap and fast next generation sequencing (NGS) technologies facilitate research of de novo assembly greatly. The reliability of contigs is critical to construct reliable scaffolding. However, contigs generated from most assemblers contain errors because of the limitation of assembly strategy and computation complexity. Among all these errors, the misassembly error is one of the most harmful types. Results In this paper, we propose a new method named “PECC” to identify and correct misassembly errors in contigs based on the paired-end read distribution. PECC extracts sequence regions with lower paired-end reads supports and verifies them based on the distribution of paired-end supports. To validate the effectiveness of PECC, we applied PECC to the contigs produced by five popular assemblers on four real datasets, and we also carried out experiments to analyze the influences of PECC on scaffolding. The results show that PECC can reduce misassembly errors and improve the performance of scaffolding results, which demonstrate the promising applications of PECC in de novo assembly.

      PubDate: 2017-05-04T14:41:48Z
  • Gene Expression Profiling of Tumor-Associated Macrophages after Exposure
           to Single-Dose Irradiation
    • Abstract: Publication date: Available online 30 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Wei-Hsiang Kung, Ching-Fang Yu, Andy Chi-Lung Lee, Chi-Dung Yang, Yu-Chen Liu, Fang-Hsin Chen, Hsien-Da Huang
      Radiotherapy (RT) is a common cancer treatment approach that accounts for nearly 50% of patient treatment; however, tumor relapse after radiotherapy is still a major issue. To study the crucial role of tumor-associated macrophages (TAMs) in the regulation of tumor progression post-RT, microarray experiments comparing TAM gene expression profiles between unirradiated and irradiated tumors were conducted to discover possible roles of TAMs in initiation or contribution to tumor recurrence following RT, taking into account the relationships among gene expression, tumor microenvironment, and immunology. A single dose of 25Gy was given to TRAMP C-1 prostate tumors established in C57/B6 mice. CD11b-positive macrophages were extracted from the tumors at one, two and three weeks post-RT. Gene ontology (GO) term analysis using the DAVID database revealed that genes that were differentially expressed at one and two weeks after irradiation were associated with biological processes such as morphogenesis of a branching structure, tube development, and cell proliferation. Analysis using Short Time-Series Expression Miner (STEM) revealed the temporal gene expression profiles and identified 13 significant patterns in four main groups of profiles. The genes in the upregulated temporal profile have diverse functions involved in the intracellular signaling cascade, cell proliferation, and cytokine-mediated signaling pathway. We show that tumor irradiation with a single 25-Gy dose can initiate a time-series of differentially expressed genes in TAMs, which are associated with the immune response, DNA repair, cell cycle arrest, and apoptosis. Our study helps to improve our understanding of the function of the group of genes whose expression changes temporally in an irradiated tumor microenvironment.

      PubDate: 2017-05-04T14:41:48Z
  • Identification of Novel Human Renin Inhibitors through a combined approach
           of Pharmacophore modelling, Molecular DFT analysis and in silico screening
    • Abstract: Publication date: Available online 27 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Dhrubajyoti Gogoi, Vishwa Jyoti Baruah, Amrita Kashyap Chaliha, Bibhuti Bhushan Kakoti, Diganta Sarma, Alak Kumar Buragohain
      Renin is an aspartyl protease of the renin–angiotensin system (RAS) and the first enzyme of the biochemical pathway for the generation of Angiotensin II- a potent vasoconstrictor involved in the maintenance of cardiovascular homeostasis and the regulation of blood pressure. High enzymatic specificity of renin and its involvement in the catalysis of the rate-limiting step of the RAS hormone system qualifies it as a good target for inhibition of hypertension and other associated diseases. Ligand-based pharmacophore model (Hypo1) was generated from a training set of 24 compounds with renin inhibitory activity. The best hypothesis consisted of one Hydrogen Bond Acceptor (HBA), three Hydrophobic Aliphatic (HY-Al) and one Ring Aromatic (AR) features. This well-validated pharmacophore hypothesis (correlation coefficient 0.95) was further utilized as a 3D query to screen database compounds, which included structures from two natural product repositories. These screened compounds were further analyzed for drug-likeness and ADMET studies. The compounds which satisfied the qualifying criteria were then subjected to molecular docking and Density Functional Theory (DFT) analysis in order to discern their atomic level interactions at the active site of the 3D structure of rennin. The pharmacophore-based modeling that has been used to generate the novel findings of the present study would be an avant-garde approach towards the development of potent inhibitors of renin.
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      PubDate: 2017-04-28T02:46:13Z
  • Identifying dynamic pathway interactions based on clinical information
    • Abstract: Publication date: Available online 24 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Shinuk Kim
      In this paper, we introduce approaches for inferring dynamic pathway interactions by converting static datasets into dynamic datasets using patients’ clinical information. One approach uses survival time–based dynamic datasets, and the other uses grade- and stage-based dynamic datasets. Based on cancer grades and stages, we generated six dynamic levels and obtained two pairs of significant pathways out of twelve enriched pathways. One pair of the pathways included CELL ADHESION MOLECULES CAMS and SYSTEMIC LUPUS ERYTHEMATOSUS (correlation coefficient = 1.00), in which CD28, CD86, HLA-DOA, and HLA-DOB were identified as common genes in the pathways. The other pair of the pathways included SPLICEOSOME and PRIMARY IMMUNODEFICIENCY (correlation coefficient = 0.94) with no common genes identified.

      PubDate: 2017-04-28T02:46:13Z
  • Cell-to-cell Modeling of the interface between Atrial and Sinoatrial
           Anisotropic Heterogeneous Nets
    • Abstract: Publication date: Available online 21 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Gabriel López Garza, Norma P. Castellanos, Rafael Godínez
      The transition between Sinoatrial cells and Atrial cells in the heart is not fully understood. Here we focus on cell-to-cell mathematical models involving typical Sinoatrial cells and Atrial cells connected with experimentally observed conductance values. We are interested mainly in the geometry of the microstructure of the conduction paths in the Sinoatrial Node. We show with some models that appropriate source-sink relationships between Atrial and Sinoatrial cells may occur according to certain geometric arrangements.
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      PubDate: 2017-04-28T02:46:13Z
  • Hidden Markov model and Chapman Kolmogrov for protein structures
           prediction from images
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Md. Sarwar Kamal, Linkon Chowdhury, Mohammad Ibrahim Khan, Amira S. Ashour, João Manuel R.S. Tavares, Nilanjan Dey
      Protein structure prediction and analysis are more significant for living organs to perfect asses the living organ functionalities. Several protein structure prediction methods use neural network (NN). However, the Hidden Markov model is more interpretable and effective for more biological data analysis compared to the NN. It employs statistical data analysis to enhance the prediction accuracy. The current work proposed a protein prediction approach from protein images based on Hidden Markov Model and Chapman Kolmogrov equation. Initially, a preprocessing stage was applied for protein images’ binarization using Otsu technique in order to convert the protein image into binary matrix. Subsequently, two counting algorithms, namely the Flood fill and Warshall are employed to classify the protein structures. Finally, Hidden Markov model and Chapman Kolmogrov equation are applied on the classified structures for predicting the protein structure. The execution time and algorithmic performances are measured to evaluate the primary, secondary and tertiary protein structure prediction.
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      PubDate: 2017-04-21T02:31:08Z
  • Comprehensive Comparison of Two Protein Family of P-ATPases (13A1 and
           13A3) in Insects
    • Abstract: Publication date: Available online 15 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Samin Seddigh
      The P- type ATPases (P-ATPases) are present in all living cells where they mediate ion transport across membranes on the expense of ATP hydrolysis. Different ions which are transported by these pumps are protons like calcium, sodium, potassium, and heavy metals such as manganese, iron, copper, and zinc. Maintenance of the proper gradients for essential ions across cellular membranes makes P-ATPases crucial for cell survival. In this study, characterization of two families of P-ATPases including P-ATPase 13A1 and P-ATPase 13A3 protein was compared in two different insect species from different orders. According to the conserved motifs found with MEME, nine motifs were shared by insects of 13A1 family but eight in 13A3 family. Seven different insect species from 13A1 and five samples from 13A3 family were selected as the representative samples for functional and structural analyses. The structural and functional analyses were performed with ProtParam, SOPMA, SignalP 4.1, TMHMM 2.0, ProtScale and ProDom tools in the ExPASy database. The tertiary structure of Bombus terrestris as a sample of each family of insects were predicted by the Phyre2 and TM-score servers and their similarities were verified by SuperPose server. The tertiary structures were predicted via the “c3b9bA” model (PDB Accession Code: 3B9B) in P-ATPase 13A1 family and “c2zxeA” model (PDB Accession Code: 2ZXE) in P-ATPase 13A3 family. A phylogenetic tree was constructed with MEGA 6.06 software using the Neighbor-joining method. According to the results, there was a high identity of P-ATPase families so that they should be derived from a common ancestor however they belonged to separate groups. In protein–protein interaction analysis by STRING 10.0, six common enriched pathways of KEGG were identified in B. terrestris in both families. The obtained data provide a background for bioinformatic studies of the function and evolution of other insects and organisms.
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      PubDate: 2017-04-21T02:31:08Z
  • Genome-wide predicting disease-related protein complexes by walking on the
           heterogeneous network based on data integration and laplacian
    • Abstract: Publication date: Available online 13 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Zhiming Liu, Jiawei Luo
      Background Associating protein complexes to human inherited diseases is critical for better understanding of biological processes and functional mechanisms of the disease. Many protein complexes have been identified and functionally annotated by computational and purification methods so far, however, the particular roles they were playing in causing disease have not yet been well determined. Results In this study, we present a novel method to identify associations between protein complexes and diseases. First, we construct a disease-protein heterogeneous network based on data integration and laplacian normalization. Second, we apply a random walk with restart on heterogeneous network (RWRH) algorithm on this network to quantify the strength of the association between proteins and the query disease. Third, we sum over the scores of member proteins to obtain a summary score for each candidate protein complex, and then rank all candidate protein complexes according to their scores. With a series of leave-one-out cross-validation experiments, we found that our method not only possesses high performance but also demonstrates robustness regarding the parameters and the network structure. We test our approach with breast cancer and select top 20 highly ranked protein complexes, 17 of the selected protein complexes are evidenced to be connected with breast cancer. Conclusions Our proposed method is effective in identifying disease-related protein complexes based on data integration and laplacian normalization.
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      PubDate: 2017-04-21T02:31:08Z
  • QSAR, docking studies of 1,3-thiazinan-3-yl isonicotinamide derivatives
           for antitubercular activity
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Trupti S. Chitre, Kalyani D. Asgaonkar, Shital M. Patil, Shiva Kumar, Vijay M. Khedkar, Dinesh R. Garud
      The enzyme – enoyl acyl carrier protein reductase (enoyl ACP reductase) is a validated target for antitubercular activity. Inhibition of this enzyme interferes with mycolic acid synthesis which is crucial for Mycobacterium tuberculosis cell growth. In the present work 2D and 3D quantitative structure activity relationship (QSAR) studies were carried out on a series of thiazinan–Isoniazid pharmacophore to design newer analogues. For 2D QSAR, the best statistical model was generated using SA-MLR method (r 2 =0.958, q 2 =0.922) while 3D QSAR model was derived using the SA KNN method (q 2 =0.8498). These studies could guide the topological, electrostatic, steric, hydrophobic substitutions around the nucleus based on which the NCEs were designed. Furthermore, molecular docking was performed to gauze the binding affinity of the designed analogues for enoyl ACP reductase enzyme. Amongst all the designed analogues the binding energies of SKS 01 and SKS 05 were found to be −5.267kcal/mol and −5.237kcal/mol respectively which was comparable with the binding energy of the standard Isoniazid (−6.254kcal/mol).
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      PubDate: 2017-04-13T20:03:39Z
  • Prediction and feature analysis of intron retention events in plant genome
    • Abstract: Publication date: Available online 13 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Ying Cui, Chao Zhang, Meng Cai
      Alternative splicing (AS) is a major contributor to increase the potential informational content of eukaryotic genomes by creating multiple mRNA species and proteins from a single gene. In plants, up to 60% genes are alternatively spliced and the most common type of AS is intron retention (IR). Genomic analyses of IR have illuminated its crucial role in shaping the evolution of genomes, in the control of developmental processes, and in the dynamic regulation of the transcriptome to influence phenotype. To explore the relationship between the sequence feature and the formation mechanism of IR, we statistically analyzed the retained introns and proposed an improved random forest-based hybrid method to predict intron retention events in plant genome. The results indicate that IR has significant relationship with individual introns which have weaker 5' splice sites, lower GC content and less termination codon occurrence. By the method we proposed, 93.48% retained introns can be correctly distinguished from constitutive introns. Strikingly, our study will facilitate a better understanding of underlying mechanisms involved in intron retention.
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      PubDate: 2017-04-13T20:03:39Z
  • Discovery of Potential Inhibitor against Human Acetylcholinesterase: A
           Molecular Docking and Molecular Dynamics Investigation
    • Abstract: Publication date: Available online 12 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Surya Pratap Singh, Dwijendra Gupta
      Alzheimer’s disease (AD) is a progressive neurodegenerative disease of central nervous system among elderly people. Human acetylcholinesterase (hAChE), an important enzyme in neuronal signaling, is responsible for the degradation of acetylcholine which in turn prevents the post synaptic signal transmissions. hAChE has been an attractive target of drug discovery for the search of therapeutics against AD. In the recent past hAChE has become hot target for the investigation of new potential therapeutics. We performed virtual screening of entire database against hAChE. Further, the extra precision molecular docking was carried out to refine the docking results and the best complex was passed for molecular dynamics simulations in order of understanding the hAChE dynamics and its behavior in complex with the ligand which corroborate the outcomes of virtual screening. This also provides binding free energy data that establishes the ligands efficiency for inhibiting hAChE. The computational findings discussed in this paper provide initial information of inhibitory effects of ligand, (drugbank entry DB00983), over hAChE.
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      PubDate: 2017-04-13T20:03:39Z
  • Structural space of intramolecular peptide disulfides: Analysis of peptide
           toxins retrieved from venomous peptide databases
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Panchada Ch. V. Govindu, Priyanka Chakraborty, Angshu Dutta, Konkallu Hanumae Gowd
      Structural space of intramolecular peptide disulfides is the combination of arrangement of even number of cysteine residues in single polypeptide and the disulfide isomers resulting from differential connectivity between cysteine residues. In the current report, we are documenting theoretical analysis and derivation of general formula [ 2 × 4 { ( n 2 ) − 1 } ] to predict possible distinct cysteine patterns for given ‘n’ even number of cysteine residues in a sequence. Combined formula of predicting distinct cysteine patterns and different disulfide isomers can be used to deduce the truly available structural space of intramolecular peptide disulfides, which may be used in structural analysis of disulfide rich peptides and proteins. In this report, we have also analyzed cysteine patterns and disulfide connectivities of peptide toxins, which is the largest group of intramolecular peptide disulfide natural products, retrieved from publically available animal toxin databases. Observed 29 distinct cysteine patterns of toxins exhibited 61 unique intramolecular disulfide folds, with limitation of having up to eight cysteine residues in a sequence, compared to theoretically available 170 different cysteine patterns generating 13,946 distinct intramolecular disulfide folds. Database analysis of peptide toxins has also revealed the features of presence of same intramolecular disulfide motif in functionally divergent peptide toxins and adaptation of the same disulfide fold with similar functions in different venomous species. Calculations of relative accessible surface area of cystine and average value of non-cysteine residues in the representative intramolecular disulfide folds of peptide toxins has revealed the feature of poor accessibility of cystine to external agents and their dependency on number of disulfide bonds in the sequence. Implementation of new generation sequencing methods and novel disulfide mapping techniques will unravel hidden diversity of intramolecular disulfide motifs of toxins and current report points to the selection of disulfide motifs in peptide toxins.
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      PubDate: 2017-04-06T19:51:34Z
  • DrugClust: A machine learning approach for drugs side effects prediction
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Giovanna Maria Dimitri, Pietro Lió
      Background Identification of underlying mechanisms behind drugs side effects is of extreme interest and importance in drugs discovery today. Therefore machine learning methodology, linking such different multi features aspects and able to make predictions, are crucial for understanding side effects. Methods In this paper we present DrugClust, a machine learning algorithm for drugs side effects prediction. DrugClust pipeline works as follows: first drugs are clustered with respect to their features and then side effects predictions are made, according to Bayesian scores. Biological validation of resulting clusters can be done via enrichment analysis, another functionality implemented in the methodology. This last tool is of extreme interest for drug discovery, given that it can be used as a validation of the clusters obtained, as well as for the study of new possible interactions between certain side effects and nontargeted pathways. Results Results were evaluated on a 5-folds cross validations procedure, and extensive comparisons were made with available datasets in the field: Zhang et al. (2015), Liu et al. (2012) and Mizutani et al. (2012). Results are promising and show better performances in most of the cases with respect to the available literature. Availability DrugClust is an R package freely available at:
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      PubDate: 2017-04-06T19:51:34Z
  • Node-based differential network analysis in genomics
    • Abstract: Publication date: Available online 4 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Xiao-Fei Zhang, Le Ou-Yang, Hong Yan
      Gene dependency networks often undergo changes in response to different conditions. Understanding how these networks change across two conditions is an important task in genomics research. Most previous differential network analysis approaches assume that the difference between two condition-specific networks is driven by individual edges. Thus, they may fail in detecting key players which might represent important genes whose mutations drive the change of network. In this work, we develop a node-based differential network analysis (N-DNA) model to directly estimate the differential network that is driven by certain hub nodes. We model each condition-specific gene network as a precision matrix and the differential network as the difference between two precision matrices. Then we formulate a convex optimization problem to infer the differential network by combing a D-trace loss function and a row-column overlap norm penalty function. Simulation studies demonstrate that N-DNA provides more accurate estimate of the differential network than previous competing approaches. We apply N-DNA to ovarian cancer and breast cancer gene expression data. The model rediscovers known cancer-related genes and contains interesting predictions.

      PubDate: 2017-04-06T19:51:34Z
  • GQSAR Modeling and Combinatorial library generation of
           4-phenylquinazoline-2-carboxamide derivatives as Antiproliferative Agents
           in Human Glioblastoma Tumors
    • Abstract: Publication date: Available online 4 April 2017
      Source:Computational Biology and Chemistry
      Author(s): Debolina Goswami, Sukriti Goyal, Salma Jamal, Ritu Jain, Divya Wahi, Abhinav Grover
      Background TSPO translocator protein, encoded in humans by the Tspo gene plays a crucial role in mitochondria mediated apoptosis and necrotic cell death through its association with Mitochondrial Permeability Transition pore (MPTP). It has been shown that this function can be exploited as a potential treatment for human Glioblastoma Multiforme. In this study, a novel robust fragment based QSAR model has been developed for a series of 4-phenylquinazoline-2-carboxamides experimentally known to be ligands for TSPO, thus triggering apoptotic mechanism cascade. Results Model developed showed satisfactory statistical parameters for the experimentally reported dataset (r2 =0.8259, q2 =0.6788, pred_r2 =0.8237 and F-test=37.9). Low standard error values (r2_se=0.253, q2_se=0.34, pred_r2_se=0.14) confirmed the accuracy of the generated model. The model obtained had 4 descriptors, namely, R1-Volume, R2-SsCH3E-index, R3-SsCH3count and R5-EpsilonR. Two of them had positive contribution while the other two had negative correlation. Conclusion The high binding affinity and the presence of essential structural features in these compounds make them an ideal choice for the consideration as potent anti-GBM drugs. Activity predicted by GQSAR model reinforces their potential as worthy candidates for drugs against GBM. The detailed analysis carried out in this study provides a substantial basis for the prospective design and development of novel 4-phenylquinazoline-2-carboxamide compounds as TSPO ligands capable of inducing apoptosis in cancer cells.

      PubDate: 2017-04-06T19:51:34Z
  • Dihydropyrazole and dihydropyrrole structures based design of Kif15
           inhibitors as novel therapeutic agents for cancer
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Jomon Sebastian
      Mitotic Kinesin motors, Eg5 and Kif15, have recently emerged as good targets for cancer as they play an inevitable role during mitosis. But, most of the Eg5 inhibitors were found ineffective when the cancer cells develop resistance to them by escalating the expression of Kif15 as alternative to Eg5. Therefore, the drugs that target Kif15 became necessary to be used either as a single or in combination with Eg5 inhibitors. The present study used 39 dihydropyrazole and 13 dihydropyrrole derivatives that were having in vitro inhibitory potential against kinesin motors to develop a common pharmacophore hypothesis AHRR and atom-based QSAR model. The model was used for virtual screening of ZINC database and the resultant hits were docked against Kif15. The four drug candidates with high docking score were examined for their activity and pharmacokinetic behaviour. Based on the results these drugs could be considered as lead candidates in further drug development for cancer.
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      PubDate: 2017-03-30T19:28:06Z
  • Pharmacoinformatics exploration of polyphenol oxidases leading to novel
           inhibitors by virtual screening and molecular dynamic simulation study
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Mubashir Hassan, Qamar Abbas, Zaman Ashraf, Ahmed A. Moustafa, Sung-Yum Seo
      Polyphenol oxidases (PPOs)/tyrosinases are metal-dependent enzymes and known as important targets for melanogenesis. Although considerable attempts have been conducted to control the melanin-associated diseases by using various inhibitors. However, the exploration of the best anti-melanin inhibitor without side effect still remains a challenge in drug discovery. In present study, protein structure prediction, ligand-based pharmacophore modeling, virtual screening, molecular docking and dynamic simulation study were used to screen the strong novel inhibitor to cure melanogenesis. The 3D structures of PPO1 and PPO2 were built through homology modeling, while the 3D crystal structures of PPO3 and PPO4 were retrieved from PDB. Pharmacophore modeling was performed using LigandScout 3.1 software and top five models were selected to screen the libraries (2601 of Aurora and 727, 842 of ZINC). Top 10 hit compounds (C1-10) were short-listed having strong binding affinities for PPO1-4. Drug and synthetic accessibility (SA) scores along with absorption, distribution, metabolism, excretion and toxicity (ADMET) assessment were employed to scrutinize the best lead hit. C4 (name) hit showed the best predicted SA score (5.75), ADMET properties and drug-likeness behavior among the short-listed compounds. Furthermore, docking simulations were performed to check the binding affinity of C1-C10 compounds against target proteins (PPOs). The binding affinity values of complex between C4 and PPOs were higher than those of other complexes (−11.70, −12.1, −9.90 and −11.20kcal/mol with PPO1, PPO2, PPO3, or PPO4, respectively). From comparative docking energy and binding analyses, PPO2 may be considered as better target for melanogenesis than others. The potential binding modes of C4, C8 and C10 against PPO2 were explored using molecular dynamics simulations. The root mean square deviation and fluctuation (RMSD/RMSF) graphs results depict the significance of C4 over the other compounds. Overall, bioactivity and ligand efficiency profiles suggested that the proposed hit may be more effective inhibitors for melanogenesis.
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      PubDate: 2017-03-24T19:15:31Z
  • Structural modeling of human organic cation transporters
    • Abstract: Publication date: June 2017
      Source:Computational Biology and Chemistry, Volume 68
      Author(s): Tikam Chand Dakal, Rajender Kumar, Dindial Ramotar
      Human organic cation transporters (hOCTs) belong to solute carriers (SLC) 22 family of membrane proteins that play a central role in transportation of chemotherapeutic drugs for several clinical and pathological conditions, including cancer and diabetes. These transporters mediate drug transport; however, the precise mechanism of drug-binding and transport by them is not fully uncovered yet, partly due to unavailability of any crystal structure record. In this work, we performed a multi-phasic approach to compute the 3D structural models of seven human organic cation transporters (hOCTs) starting from primary protein sequence. Our structure modeling approach included 1) I-TASSER based comparative sequence alignment, threading and ab-initio protein modeling; 2) models comparison with PSIPRED secondary structure prediction; 3) loop modeling for incongruent secondary structure in Chimera 1.10.1; 4) high resolution structure simulation, refinement, energy minimization using ModRefiner, and 5) validation of the structure models using PROCHECK at SAVEs. From structural point, the computed 3D structures of hOCTs consist of a typical major facilitator superfamily (MFS) fold of twelve α-transmembrane helix domains arranged in a manner rendering hOCTs a barrel shaped structure with a large cleft that opens in cytoplasm. The modeled 3D structure of all hOCTs closely resemble to human SLC2A3 (GLUT3) transporter (PDB ID: 5c65) and displayed an outward-open confirmation and putative cyclic C1 protein symmetry. In addition, hOCTs has a large (>100 amino acids) unique extracellular loop between TMH1 and TMH2 having potential glycosylation sites (Asn-Xaa-Ser/Thr) and cysteine residues, both features indicative of putative role in drug binding and uptake. There is an intracellular three/four-helix loop between TMH6 and TMH7 containing putative phosphorylation sites for precise regulation of hOCTs function as drug transporters. There are nine loops of 4 to 11 amino acids length that protrude from membrane, both intracellularly and extracellularly, and connect adjacent TMHs. The 2D structure prediction showed Nin-Cin topology of all hOCTs. In the unavailability of the crystal structures of hOCTs, the 3D structural models computed in-silico and presented herein can be used for studying the mechanism of drug binding and transport by hOCTs.
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      PubDate: 2017-03-24T19:15:31Z
  • Chemical Principles Additive Model Aligns Low Consensus DNA Targets of p53
           Tumor Suppressor Protein
    • Abstract: Publication date: Available online 22 March 2017
      Source:Computational Biology and Chemistry
      Author(s): Kelly M. Thayer, In Sub M. Han
      Computational prediction of the interaction between protein transcription factors and their cognate DNA binding sites in genomic promoters constitutes a formidable challenge in two situations: when the number of discriminatory interactions is small compared to the length of the binding site, and when DNA binding sites possess a poorly conserved consensus binding motif. The transcription factor p53 tumor suppressor protein and its target DNA exhibit both of these issues. From crystal structure analysis, only three discriminatory amino acid side chains contact the DNA for a binding site spanning ten base pairs. Furthermore, our analysis of a dataset of genome wide fragments binding to p53 revealed many sequences lacking the expected consensus. The low information content leads to an overestimation of binding sites, and the lack of conservation equates to a computational alignment problem. Within a fragment of DNA known to bind to p53, computationally locating the position of the site equates to aligning the DNA with the binding interface. From a molecular perspective, that alignment implies a specification of which DNA bases are interacting with which amino acid side chains, and aligning many sequences to the same protein interface concomitantly produces a multiple sequence alignment. From this vantage, we propose to cast prediction of p53 binding sites as an alignment to the protein binding surface with the novel approach of optimizing the alignment of DNA fragments to the p53 binding interface based on chemical principles. A scoring scheme based on this premise was successfully implemented to score a dataset of biological DNA fragments known to contain p53 binding sites. The results illuminate the mechanism of recognition for the protein-DNA system at the forefront of cancer research. These findings implicate that p53 may recognize its target binding sites via several different mechanisms which may include indirect readout.
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      PubDate: 2017-03-24T19:15:31Z
  • In Silico Analysis of Nonsynonymous Single Nucleotide Polymorphisms of the
           Human Adiponectin Receptor 2 (ADIPOR2) Gene
    • Abstract: Publication date: Available online 14 March 2017
      Source:Computational Biology and Chemistry
      Author(s): Md. Solayman, Md. Abu Saleh, Sudip Paul, Md. Ibrahim Khalil, Siew Hua Gan
      Polymorphisms of the ADIPOR2 gene are frequently linked to a higher risk of developing diseases including obesity, type 2 diabetes and cardiovascular diseases. Though mutations of the ADIPOR2 gene are detrimental, there is a lack of comprehensive in silico analyses of the functional and structural impacts at the protein level. Considering the involvement of ADIPOR2 in glucose uptake and fatty acid oxidation, an in silico functional analysis was conducted to explore the possible association between genetic mutations and phenotypic variations. A genomic analysis of 82 nonsynonymous SNPs in ADIPOR2 was initiated using SIFT followed by the SNAP2, nsSNPAnalyzer, PolyPhen-2, SNPs&GO, FATHMM and PROVEAN servers. A total of 10 mutations (R126W, L160Q, L195P, F201S, L235R, L235P, L256R, Y328H, E334K and Q349H) were predicted to have deleterious effects on the ADIPOR2 protein and were therefore selected for further analysis. Theoretical models of the variants were generated by comparative modelling via MODELLER 9.16. A protein structural analysis of these amino acid variants was performed using SNPeffect, I-Mutant, ConSurf, Swiss PDB viewer and NetSurfP to explore their solvent accessibility, molecular dynamics and energy minimization calculations. In addition, FTSite was used to predict the ligand binding sites, while NetGlycate, NetPhos2.0, UbPerd and SUMOplot were used to predict post-translational modification sites. All of the variants showed increased free energy, though F201S exhibited the highest energy increase. The root mean square deviation values of the modelled mutants strongly indicated likely pathogenicity. Remarkably, three binding sites were detected on ADIPOR2, and two mutations at positions 328 and 201 were found in the first and second binding pockets, respectively. Interestingly, no mutations were found at the post-translational modification sites. These genetic variants can provide a better understanding of the wide range of disease susceptibility associated with ADIPOR2 and aid the development of new molecular diagnostic markers for these diseases. The findings may also facilitate the development of novel therapeutic elements for associated diseases.
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      PubDate: 2017-03-24T19:15:31Z
  • A Bioinformatics approach to designing a Zika virus vaccine
    • Abstract: Publication date: Available online 10 March 2017
      Source:Computational Biology and Chemistry
      Author(s): Sumanta Dey, Ashesh Nandy, Subhash C. Basak, Papiya Nandy, Sukhen Das
      The Zika virus infections have reached epidemic proportions in the Latin American countries causing severe birth defects and neurological disorders. While several organizations have begun research into design of prophylactic vaccines and therapeutic drugs, computer assisted methods with adequate data resources can be expected to assist in these measures to reduce lead times through bioinformatics approaches. Using 60 sequences of the Zika virus envelope protein available in the GenBank database, our analysis with numerical characterization techniques and several web based bioinformatics servers identified four peptide stretches on the Zika virus envelope protein that are well conserved and surface exposed and are predicted to have reasonable epitope binding efficiency. These peptides can be expected to form the basis for a nascent peptide vaccine which, enhanced by incorporation of suitable adjuvants, can elicit immune response against the Zika virus infections.
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      PubDate: 2017-03-13T02:26:51Z
  • Pharmacophore based 3D-QSAR Modeling, Virtual Screening and Docking for
           Identification of Potential Inhibitors of β-secretase
    • Abstract: Publication date: Available online 6 March 2017
      Source:Computational Biology and Chemistry
      Author(s): Ravichand Palakurti, Ramakrishna Vadrevu
      The enzyme β-secretase-1 is responsible for the cleavage of the amyloid precursor protein, a vital step in the process of the formation of amyloid-β peptides which are known to lead to neurodegeneration causing Alzheimer’s disease. Challenges associated with toxicity and blood brain permeation inability of potential inhibitors, continue to evade a successful therapy, thus demanding the search and development of highly active and effective inhibitors. Towards these efforts, we used a ligand based pharmacophore model generation from a dataset of known inhibitors whose activities against β-secretase hovered in the nano molar range. The identified 5 feature pharmacophore model, AHHPR, was validated via three dimensional quantitative structure activity relationship as indicated by r2, q2 and Pearson R values of 0.9013, 0.7726 and 0.9041 respectively. For a dataset of compounds with nano molar activity, the important pharmacophore features present in the current model appear to be similar with those observed in the models resulting from much wider activity range of inhibitors. Virtual screening of the ChemBridge CNS-Set™, a database having compounds with a better suitability for central nervous system based disorders followed by docking and analysis of the ligand protein interactions resulted in the identification of eight prospective compounds with considerable diversity. The current pharmacophore model can thus be useful for the identification, design and development of potent β-secretase inhibitors which by optimization can be potential therapeutics for Alzheimer’s disease.
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      PubDate: 2017-03-10T02:21:39Z
  • Structure based design, synthesis and biological evaluation of amino
           phosphonate derivatives as human glucokinase activators
    • Abstract: Publication date: Available online 2 March 2017
      Source:Computational Biology and Chemistry
      Author(s): Nanda Kumar Yellapu, Raveendra Babu Kilaru, Nagaraju Chamarthi, PVGK Sarma, Bhaskar Matcha
      Glucokinase (GK) is a potential therapeutic target of type 2 diabetes and GK activators (GKAs) represent a promising class of small organic molecules which enhance GK activity. Based on the configuration and conformation of the allosteric site of GK, we have designed a novel class of amino phosphonate derivatives in order to develop potent GKAs. The QSAR model developed using numerous descriptors revealed its potential with the best effective statistical values of RMSE=1.52 and r 2 =0.30. Moreover, application of this model on the present test set GKAs proved to be worthy to predict their activities as a better linear relationship was observed with RMSE=0.14 and r 2 =0.88. ADME studies and Lipinski filters encouraged them as safer therapeutics. The molecular dynamics and docking studies against the GK allosteric site revealed that all GKAs bind with best affinities and the complexes are strengthened by H-bonding, phosphonate salt bridges, hydrophobic and arene cat ionic interactions. Finally, in vitro evaluation with human liver GK revealed their potential to increase the GK activity by different folds. The results from QSAR, ADME, molecular docking and in vitro assays strongly suggested that the present molecules could be used as effective and safer therapeutics to control and manage type 2 diabetes.
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      PubDate: 2017-03-05T08:43:57Z
  • Genome-wide identification and characterization of conserved and novel
           microRNAs in grass carp (Ctenopharyngodon idella) by deep sequencing
    • Abstract: Publication date: Available online 2 March 2017
      Source:Computational Biology and Chemistry
      Author(s): Wangbao Gong, Yong Huang, Jun Xie, Guangjun Wang, Deguang Yu, Xihong Sun
      MicroRNAs (miRNAs) are post-transcriptional regulators which bind to target to regulate protein expression by repressing translation or promoting degradation of the target mRNA. Studies have shown that deep sequencing is a powerful tool for the identification of miRNAs, and it is believed that may more miRNAs remain to be discovered in grass carp. In the present study, a pool of equal amounts of RNA obtained from 8 different adult grass carp tissues (spleen, liver, muscle, kidney, skin, testis, gut and heart) was sequenced using deep sequencing technology. A total of 16.579.334 raw reads were yielded from the pooled small RNA library. Using bioinformatics analysis, we identified 160 conserved miRNAs and 18 novel miRNAs in grass carp. Randomly selected 6 conserved and 2 novel miRNAs were confirmed their expression by stem-loop qRT-PCR assay. Furthermore, the 1212 potential targets of these miRNAs were predicted using miRNA target prediction tool. GO and KEGG pathway enrichment analyses indicated relevant biological processes. Our study provides the first genome-wide investigation of miRNAs from 8 mixed tissues of grass carp, and the data obtained expand the known grass carp miRNA inventory and provide a basis for further understanding functions of grass carp miRNAs.
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      PubDate: 2017-03-05T08:43:57Z
  • Pathway cross-talk network analysis identifies critical pathways in
           neonatal sepsis
    • Abstract: Publication date: Available online 27 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Yu-Xiu Meng, Quan-Hong Liu, Deng-Hong Chen, Ying Meng
      Background Despite advances in neonatal care, sepsis remains a major cause of morbidity and mortality in neonates worldwide. Pathway cross-talk analysis might contribute to the inference of the driving forces in bacterial sepsis and facilitate a better understanding of underlying pathogenesis of neonatal sepsis. Objective This study aimed to explore the critical pathways associated with the progression of neonatal sepsis by the pathway cross-talk analysis. Methods By integrating neonatal transcriptome data with known pathway data and protein-protein interaction data, we systematically uncovered the disease pathway cross-talks and constructed a disease pathway cross-talk network for neonatal sepsis. Then, attract method was employed to explore the dysregulated pathways associated with neonatal sepsis. To determine the critical pathways in neonatal sepsis, rank product (RP) algorithm, centrality analysis and impact factor (IF) were introduced sequentially, which synthetically considered the differential expression of genes and pathways, pathways cross-talks and pathway parameters in the network. The dysregulated pathways with the highest IF values as well as RP<0.01 were defined as critical pathways in neonatal sepsis. Results By integrating three kinds of data, only 6,919 common genes were included to perform the pathway cross-talk analysis. By statistic analysis, a total of 1,249 significant pathway cross-talks were selected to construct the pathway cross-talk network. Moreover, 47 dys-regulated pathways were identified via attract method, 20 pathways were identified under RP<0.01, and the top 10 pathways with the highest IF were also screened from the pathway cross-talk network. Among them, we selected 8 common pathways, i.e. critical pathways. Conclusions In this study, we systematically tracked 8 critical pathways involved in neonatal sepsis by integrating attract method and pathway cross-talk network. These pathways might be responsible for the host response in infection, and of great value for advancing diagnosis and therapy of neonatal sepsis.
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      PubDate: 2017-03-05T08:43:57Z
  • Exploration of Interaction Zones of β-tubulin Colchicine Binding Domainof
           Helminths and Binding Mechanism of Anthelmintics
    • Abstract: Publication date: Available online 24 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Prabodh Ranjan, Sivakumar Prasanth Kumar, Vijayakrishna Kari, Prakash Chandra Jha
      Numerous studies postulated the possible modes of anthelmintic activity by targeting alternate or extended regions of colchicine binding domain of helminth β-tubulin. We present three interaction zones (zones vide −1 to −3) in the colchicine binding domain of Haemonchus contortus (a helminth) β-tubulin homology model and developed zone-wise structure-based pharmacophore models coupled with molecular docking technique to unveil the binding hypotheses. The resulted ten structure-based hypotheses were then refined to essential three point pharmacophore features that captured recurring and crucial non-covalent receptor contacts and proposed three characteristics necessary for optimal zone-2 binding: a conserved pair of H bond acceptor (HBA to form H bond with Asn226 residue) and an aliphatic moiety of molecule separated by 3.75±0.44Å. Further, an aliphatic or a heterocyclic group distant (11.75±1.14Å) to the conserved aliphatic site formed the third feature component in the zone-2 specific anthelmintic pharmacophore model. Alternatively, an additional HBA can be substituted as a third component to establish H bonding with Asn204. We discern that selective zone-2 anthelmintics can be designed effectively by closely adapting the pharmacophore feature patterns and its geometrical constraints.
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      PubDate: 2017-02-26T08:37:29Z
  • Comparative and Evolutionary Studies of Mammalian Arylsulfatase and
           Sterylsulfatase Genes and Proteins Encoded on the X-Chromosome
    • Abstract: Publication date: Available online 24 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Roger S. Holmes
      At least 19 sulfatase genes have been reported on the human genome, including four arylsulfatase (ARS) genes (ARSD; ARSE; ARSF; ARSH) and a sterylsulfatase (STS) gene located together on the X-chromosome. Bioinformatic analyses of mammalian genomes were undertaken using known human STS and ARS amino acid sequences to study the evolution of these genes and proteins encoded on eutherian and marsupial genomes. Several domain regions and key residues were conserved including signal peptides, active site residues, metal (Ca2+) and substrate binding sequences, transmembranes and N-glycosylation sites. Phylogenetic analyses describe the relationships and potential origins of these genes during mammalian evolution. Primate ARSH enzymes lacked signal peptide sequences which may influence their biological functions. CpG117 and CpG92 were detected within the 5′ region of the human STS and ARSD genes, respectively, and miR-205 within the 3′-UTR for the human STS gene, using bioinformatic methods A proposal is described for a primordial invertebrate STS-like gene serving as an ancestor for unequal cross over events generating the gene complex on the eutherian mammalian X-chromosome.

      PubDate: 2017-02-26T08:37:29Z
    • Abstract: Publication date: Available online 14 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Manan Bhargavi, Sree Kanth Sivan, Sarita Rajender Potlapally
      Cancer is a genomic disease characterised as impaired cellular energy metabolism. Cancer cells derive most of their energy from oxidative phosphorylation unlike normal ones during cell progression TSPO protein present in external mitochondrial membrane, is involved in various cellular functions like Cell proliferation, mitochondrial respiration, synthesis of steroids and also participates in import of cholesterol into the inner mitochondrial membrane from outside of the membrane of mitochondria. The 3D model of TSPO protein is built using comparative homology modelling techniques and validated by proSA, Ramachandran plot and ERRAT in the present work. Active site prediction is carried out using SiteMap and literature, which allows the prediction of the important binding pockets for the identification of putative active site. New molecular entities as TSPO inhibitors were obtained from Virtual screening using MS Spectrum databank in Schrodinger suite and were prioritised based on Glide Score. Docking was performed using Autodock to identify molecules with different scaffolds and were prioritised based on binding energy and RMSD values. Qikprop is used to calculate pharmacokinetic properties of the screened molecules which are found to be in permissible range as possible novel inhibitors of TSPO protein to supress cell proliferation.
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      PubDate: 2017-02-19T07:37:52Z
  • Enhanced identification of β-lactamases and its classes using sequence,
           physicochemical and evolutionary information with sequence feature
           characterization of the classes
    • Abstract: Publication date: Available online 14 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Abhigyan Nath, S. Karthikeyan
      β-lactamases provides one of the most successful means of evading the therapeutic effects of β lactam class of antibiotics by many gram positive and gram negative bacteria. On the basis of sequence identity, β-lactamases have been identified into four distinct classes- A, B, C and D. The classes A, C and D are the serine β-lactamases and class B is the metallo-lactamse. In the present study, we developed a two stage cascade classification system. The first-stage performs the classification of β-lactamases from non-β-lactamases and the second-stage performs the further classification of β-lactamases into four different β–lactamase classes. In the first-stage binary classification, we obtained an accuracy of 97.3% with a sensitivity of 89.1% and specificity of 98.0% and for the second stage multi-class classification, we obtained an accuracy of 87.3% for the class A, 91.0% for the class B, 96.3% for the class C and 96.4% for class D. A systematic statistical analysis is carried out on the sieved-out, correctly-predicted instances from the second stage classifier, which revealed some interesting patterns. We analyzed different classes of β-lactamases on the basis of sequence and physicochemical property differences between them. Among amino acid composition, H, W, Y and V showed significant differences between the different β-lactamases classes. Differences in average physicochemical properties are observed for isoelectric point, volume, flexibility, hydrophobicity, bulkiness and charge in one or more β-lactamase classes. The key differences in physicochemical property groups can be observed in small and aromatic groups. Among amino acid property group n-grams except charged n-grams, all other property group n-grams are significant in one or more classes. Statistically significant differences in dipeptide counts among different β-lactamase classes are also reported.
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      PubDate: 2017-02-19T07:37:52Z
  • In silico structural and functional analysis of Mesorhizobium ACC
    • Abstract: Publication date: Available online 11 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Krishnendu Pramanik, Tithi Soren, Soumik Mitra, Tushar Kanti Maiti
      Nodulation is one of the very important processes of legume plants as it is the initiating event of fixing nitrogen. Although ethylene has essential role in normal plant metabolism but it has also negative impact on plants particularly in nodule formation in legume plants. It is also produced due to a variety of biotic or abiotic stresses. 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase is a rhizobial enzyme which cleaves ACC (immediate precursor of ethylene) into α-ketobutyrate and ammonia. As a result, the level of ethylene from the plant cells is decreased and the negative impact of ethylene on nodule formation is reduced. ACC deaminase is widely studied in several plant growth promoting rhizobacterial (PGPR) strains including many legume nodulating bacteria like Mesorhizobium sp. It is an important symbiotic nitrogen fixer belonging to the class – alphaproteobacteria under the order Rhizobiales. ACC deaminase has positive role in Legume-rhizobium symbiosis. Rhizobial ACC deaminase has the potentiality to reduce the adverse effects of ethylene, thereby triggering the nodulation process. The present study describes an in silico comparative structural (secondary structure prediction, homology modeling) and functional analysis of ACC deaminase from Mesorhizobium spp. to explore physico-chemical properties using a number of bio-computational tools. M. loti was selected as a representative species of Mesorhizobium genera for 3D modelling of ACC deaminase protein. Correlation by the phylogenetic relatedness on the basis of both ACC deaminase enzymes and respective acdS genes of different strains of Mesorhizobium has also studied.
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      PubDate: 2017-02-13T16:04:54Z
  • An in-silico approach to find a peptidomimetic targeting extracellular
           domain of HER3 from a HER3 Nanobody
    • Abstract: Publication date: Available online 10 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Z. Pourhashem, M. Mehrpouya, N. Yardehnavi, A. Eslamparast, F. Kazemi-Lomedasht
      HER3 is an important therapeutic target in cancer treatments. HER3 Nanobodies (Nbs) are a novel class of antibodies with several competitive advantages over conventional antibodies. A peptidomimetic derived from these Nbs can be considered to be a small peptide mimicking some of the molecular recognition interactions of a natural peptide or protein in a three-dimensional (3D) space, with a receptor that has improved properties. In this study, we introduce a new approach to design a peptidomimetic derived from HER3 Nb through an in silico analysis. We propose that the complementarity determining region (CDR3) of HER3 Nb is large enough to effectively interact with HER3 antigen as well as with the entire Nb. A computational analysis has been performed using Nb models retrieved from SWISS-pdb Viewer 4.1.0 (spdbv) as a target spot and HER3 extracellular domain as its antigenic target to identify the interactions between them by the protein-protein docking method. Detailed analysis of selected models with docked complex help us to identify the interacting amino acid residues between the two molecules. The results of in silico analysis show that the CDR3 of HER3 Nb might be used by itself as a peptidomimetic drug instead of the full Nb. HER3 peptidomimetic-derived HER3 Nb may reduce Nb production costs and be used as a substitute for HER3 Nb after further experimental work. The paper demonstrates the feasibility of peptidomimetics designs using bioinformatic tools.
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      PubDate: 2017-02-13T16:04:54Z
  • Computer Evaluation of VirE2 Protein Complexes for ssDNA Transfer Ability
    • Abstract: Publication date: Available online 9 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Irina Volokhina, Yury Gusev, Svyatoslav Mazilov, Yelizaveta Moiseeva, Mikhail Chumakov
      The single-stranded transfer DNA from the Ti plasmid of the soil bacteria Agrobacterium nonspecifically integrates into the plant chromosome and is inherited at subsequent cell divisions. How it is transferred across host membranes is unknown, but it is believed that VirE2 proteins form a membrane-spanning pore or channel in a lipid bilayer and possibly mediate the delivery of the single-stranded transfer DNA–VirD2–VirE2 complex to the plant cell chromosomes. The aim of this work was to perform a computer simulation of VirE2’s pore-forming capacity and an evaluation of constructed VirE2 complexes. The oscillating motions of complexes consisting of two and four VirE2 subunits were estimated by the molecular dynamics and normal modes methods. We did not predict any large changes in domain orientation for two and four-subunit VirE2 complexes within simulation times of 1ns. A possible gating mechanism similar to that seen in the ion channels of the complex formed from two VirE2 proteins was proposed, whereas no conformational changes were predicted inside the pore in the complex formed from four VirE2 proteins.
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      PubDate: 2017-02-13T16:04:54Z
  • 2,4-Ditellurouracil and its 5-fluoro derivative: Theoretical
           investigations of structural, energetics and ADME parameters
    • Abstract: Publication date: Available online 9 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Ibrahim A. Alswaidan, Kritish Sooknah, Lydia Rhyman, Cemal Parlak, Derek T. Ndinteh, Mohamed I. Elzagheid, Ponnadurai Ramasami
      2,4-Ditellurouracil exhibits keto-enol tautomerism via different pathways resulting in seven tautomers. These pathways were studied in the gas phase using density functional theory method. The functionals used were BLYP, B3LYP and BHLYP and the basis sets were 6–311++G(d,p) for all atoms except that LanL2DZ ECP was used for tellurium atom only. The results indicate that the diketo form is more stable as observed for uracil and its sulfur and selenium analogues. The effect of introducing fluorine at position 5 was also investigated and the energy difference between the diketo and dienol forms is reduced. 2,4-Ditellurouracil and its 5-fluoro analogue are expected to exist exclusively as the diketo form due to the high interconversion energy barrier. We extended the investigation to predict ADME parameters of the most stable diketo and dienol tautomers in view of understanding their biological properties. This research enlightens keto-enol tautomerism of 2,4-ditellurouracil and its 5-fluoro derivative with additional insights to biological functions.
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      PubDate: 2017-02-13T16:04:54Z
  • Simultaneous estimation of detection sensitivity and absolute copy number
           from digital PCR serial dilution
    • Abstract: Publication date: Available online 1 February 2017
      Source:Computational Biology and Chemistry
      Author(s): Xutao Deng, Brian S. Custer, Michael P. Busch, Sonia Bakkour, Tzong-Hae Lee
      Digital polymerase chain reaction (dPCR) is a refinement of the conventional PCR approach to nucleic acid detection and absolute quantification. Digital PCR works by partitioning a sample of DNA or cDNA into many individual, parallel PCR reactions. Current quantification methods rely on the assumption that the PCR reactions are always able to detect single target molecules. When the assumption does not hold, the copy numbers will be severely underestimated. We developed a novel dPCR quantification method which determines whether the single copy assumption is violated or not by simultaneously estimating the assay sensitivity and the copy numbers using serial dilution data sets. The implemented method is available as an R package “digitalPCR”.

      PubDate: 2017-02-06T15:49:06Z
  • A new search subspace to compensate failure of cavity-based localization
           of ligand-binding sites
    • Abstract: Publication date: Available online 31 January 2017
      Source:Computational Biology and Chemistry
      Author(s): Kalpana Singh, Tapobrata Lahiri
      The common exercise adopted in almost all the ligand-binding sites (LBS) predictive methods is to considerably reduce the search space up to a meager fraction of the whole protein. In this exercise it is assumed that the LBS are mostly localized within a search subspace, cavities, which topologically appear to be valleys within a protein surface. Therefore, extraction of cavities is considered as a most important preprocessing step for finally predicting LBS. However, prediction of LBS based on cavity search subspace is found to fail for some proteins. To solve this problem a new search subspace was introduced which was found successful to localize LBS in most of the proteins used in this work for which cavity-based method MetaPocket 2.0 failed. Therefore this work appeared to augment well the existing binding site predictive methods through its applicability for complementary set of proteins for which cavity-based methods might fail. Also, to decide on the proteins for which instead of cavity-subspace the new subspace should be explored, a decision framework based on simple heuristic is made which uses geometric parameters of cavities extracted through MetaPocket 2.0. It is found that option for selecting the new or cavity-search subspace can be predicted correctly for nearly 87.5% of test proteins.
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      PubDate: 2017-02-06T15:49:06Z
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