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  Subjects -> ENGINEERING (Total: 2298 journals)
    - CHEMICAL ENGINEERING (192 journals)
    - CIVIL ENGINEERING (192 journals)
    - ELECTRICAL ENGINEERING (104 journals)
    - ENGINEERING (1209 journals)
    - ENGINEERING MECHANICS AND MATERIALS (385 journals)
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CHEMICAL ENGINEERING (192 journals)                     

Showing 1 - 192 of 192 Journals sorted alphabetically
AATCC Journal of Research     Full-text available via subscription   (Followers: 7)
ACS Sustainable Chemistry & Engineering     Hybrid Journal   (Followers: 6)
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: 22)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 8)
Advanced Chemical Engineering Research     Open Access   (Followers: 32)
Advanced Powder Technology     Hybrid Journal   (Followers: 17)
Advances in Applied Ceramics     Hybrid Journal   (Followers: 5)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 26)
Advances in Chemical Engineering and Science     Open Access   (Followers: 57)
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: 11)
Annual Review of Chemical and Biomolecular Engineering     Full-text available via subscription   (Followers: 12)
Anti-Corrosion Methods and Materials     Hybrid Journal   (Followers: 10)
Applied Petrochemical Research     Open Access   (Followers: 2)
Asia-Pacific Journal of Chemical Engineering     Hybrid Journal   (Followers: 8)
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: 8)
ChemBioEng Reviews     Full-text available via subscription   (Followers: 1)
Chemical and Engineering News     Free   (Followers: 15)
Chemical and Materials Engineering     Open Access   (Followers: 13)
Chemical and Petroleum Engineering     Hybrid Journal   (Followers: 13)
Chemical and Process Engineering     Open Access   (Followers: 28)
Chemical and Process Engineering Research     Open Access   (Followers: 24)
Chemical Engineering & Technology     Hybrid Journal   (Followers: 31)
Chemical Engineering and Processing: Process Intensification     Hybrid Journal   (Followers: 16)
Chemical Engineering and Science     Open Access   (Followers: 19)
Chemical Engineering Communications     Hybrid Journal   (Followers: 14)
Chemical Engineering Education     Full-text available via subscription  
Chemical Engineering Journal     Hybrid Journal   (Followers: 46)
Chemical Engineering Research and Design     Hybrid Journal   (Followers: 25)
Chemical Engineering Research Bulletin     Open Access   (Followers: 12)
Chemical Engineering Science     Hybrid Journal   (Followers: 27)
Chemical Geology     Hybrid Journal   (Followers: 23)
Chemical Papers     Hybrid Journal   (Followers: 2)
Chemical Product and Process Modeling     Hybrid Journal   (Followers: 4)
Chemical Reviews     Full-text available via subscription   (Followers: 185)
Chemical Society Reviews     Full-text available via subscription   (Followers: 42)
Chemical Technology     Open Access   (Followers: 16)
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: 251)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 14)
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   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 11)
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: 21)
Corrosion Engineering, Science and Technology     Hybrid Journal   (Followers: 37)
Corrosion Reviews     Hybrid Journal   (Followers: 6)
Crystal Research and Technology     Hybrid Journal   (Followers: 6)
Current Opinion in Chemical Engineering     Open Access   (Followers: 7)
Designed Monomers and Polymers     Open Access   (Followers: 2)
Education for Chemical Engineers     Hybrid Journal   (Followers: 5)
Eksergi     Open Access  
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)
Food and Environment Safety     Open Access  
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: 12)
Industrial & Engineering Chemistry Research     Full-text available via subscription   (Followers: 23)
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 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: 6)
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: 14)
Journal of Applied Polymer Science     Hybrid Journal   (Followers: 136)
Journal of Applied Science & Process Engineering     Open Access  
Journal of Biomaterials Science, Polymer Edition     Hybrid Journal   (Followers: 9)
Journal of Bioprocess Engineering and Biorefinery     Full-text available via subscription  
Journal of Chemical & Engineering Data     Full-text available via subscription   (Followers: 12)
Journal of Chemical and Biological Interfaces     Full-text available via subscription   (Followers: 1)
Journal of Chemical Ecology     Hybrid Journal   (Followers: 7)
Journal of Chemical Engineering     Open Access   (Followers: 20)
Journal of Chemical Engineering and Materials Science     Open Access   (Followers: 2)
Journal of Chemical Science and Technology     Open Access   (Followers: 5)
Journal of Chemical Sciences     Partially Free   (Followers: 22)
Journal of Chemical Technology & Biotechnology     Hybrid Journal   (Followers: 10)
Journal of Chemical Theory and Computation     Full-text available via subscription   (Followers: 17)
Journal of CO2 Utilization     Hybrid Journal   (Followers: 2)
Journal of Combinatorial Chemistry     Full-text available via subscription   (Followers: 1)
Journal of Crystallization Process and Technology     Open Access   (Followers: 8)
Journal of Environmental Chemical Engineering     Hybrid Journal   (Followers: 7)
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: 5)
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 Materials Science and Chemical Engineering     Open Access  
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: 6)
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: 6)
Journal of Polymers and the Environment     Hybrid Journal   (Followers: 1)
Journal of Pure and Applied Chemistry Research     Open Access   (Followers: 2)
Journal of the American Chemical Society     Full-text available via subscription   (Followers: 321)
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: 2)
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 Rekayasa Kimia & Lingkungan     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: 2)
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   (Followers: 1)
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: 3)
Plasmas and Polymers     Hybrid Journal  
Polymer     Hybrid Journal   (Followers: 127)
Polymer Bulletin     Hybrid Journal   (Followers: 7)
Polymer Composites     Hybrid Journal   (Followers: 16)
Polyolefins Journal     Open Access  
Powder Metallurgy Progress     Unknown   (Followers: 1)
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: 4)
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: 61)
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: 6)
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: 4)
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]   [11 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1476-9271
   Published by Elsevier Homepage  [3118 journals]
  • Patterns of cation binding to the aromatic amino acid R groups in Trp,
           Tyr, and Phe
    • Abstract: Publication date: February 2018
      Source:Computational Biology and Chemistry, Volume 72
      Author(s): Shelby L. Scherer, Amanda L. Stewart, Ryan C. Fortenberry
      Previous joint experimental and theoretical work demonstrates that typically soluble peptides will be rendered insoluble in the presence of saturated sodium ions in aqueous solution due to disruption of cation-π interactions between Trp and Lys. The present work utilizes quantum chemical methods including density functional theory, symmetry-adapted perturbation theory, and even coupled cluster theory to determine the strengths of cation-π interactions for the aromatic R groups of Trp, Tyr, and Phe (approximated as skatole, methyl phenol, and toluene) with both alkali and alkaline-Earth atomic cations and electron-accepting R groups from Lys, Arg, and His approximated as methyl ammonium, guanidinium, and imidazolium cations. This work shows that sodium ion is still the most likely disrupter of peptide folding built upon cation-π interactions, since Trp, Tyr, and Phe all bind more strongly to sodium ion than to any of the polyatomic cations. Additionally, the atomic cation complex binding energies decrease with an increase in partial charge on the atomic cation in the complex. However, as the average partial charge increases in the interacting hydrogen atoms in the polyatomic cations, the binding energy increases. The disruption of such peptide–peptide cation-π interactions is certainly relevant for peptide design in β-sheets or β-hairpin structures, but it could also have implications for astrobiology.
      Graphical abstract image

      PubDate: 2018-01-05T19:20:03Z
       
  • CORAL: QSAR Models for Carcinogenicity of Organic Compounds For Male and
           Female Rats
    • Abstract: Publication date: Available online 2 January 2018
      Source:Computational Biology and Chemistry
      Author(s): Alla P. Toropova, Andrey A. Toropov
      Quantitative structure - activity relationships (QSARs) for carcinogenicity (rats, TD50 ) have been built up using the CORAL software. Different molecular features, which are extracted from simplified molecular input-line entry system (SMILES) serve as the basis for building up a model. Correlation weights for the molecular features are calculated by means of the Monte Carlo optimization. Using the numerical data on the correlation weights, one can calculate a model of carcinogenicity as a mathematical function of descriptors, which are sum of the corresponding correlation weights. In other words, the correlation weights provide the maximal correlation coefficient between the descriptor and carcinogenicity, for the training set. This correlation was assessed via external validation set. Finally, lists of molecular alerts in aspects of carcinogenicity for male rats and for female rats were compared and their differences were characterized.
      Graphical abstract image

      PubDate: 2018-01-05T19:20:03Z
       
  • Identification of potential inhibitors against nuclear Dam1 complex
           subunit Ask1 of Candida albicans using virtual screening and MD
           simulations
    • Abstract: Publication date: Available online 1 January 2018
      Source:Computational Biology and Chemistry
      Author(s): Himanshu Tripathi, Feroz Khan
      Identification of hit compounds against specific target form the starting point for a drug discovery program. A consistent decline of new chemical entities (NCEs) in recent years prompted a challenge to explore newer approaches to discover potential hit compounds that in turn can be converted into leads, and ultimately drug with desired therapeutic efficacy. The vast amount of omics and activity data available in public databases offers an opportunity to identify novel targets and their potential inhibitors. State of the art in silico methods viz., clustering of compounds, virtual screening, molecular docking, MD simulations and MMPBSA calculations were employed in a pipeline to identify potential ‘hits’ against those targets as well whose structures, as of now, could only predict through threading approaches. In the present work, we have started from scratch, amino acid sequence of target and compounds retrieved from PubChem compound database, modeled it in such a way that led to the identification of possible inhibitors of Dam1 complex subunit Ask1 of Candida albicans. We also propose a ligand based binding site determination approach. We have identified potential inhibitors of Ask1 subunit of a Dam1 complex of C. albicans, which is required to prevent precocious spindle elongation in pre-mitotic phases. The proposed scheme may aid to find virtually potential inhibitors of other unique targets against candida.
      Graphical abstract image

      PubDate: 2018-01-05T19:20:03Z
       
  • Synthesis, biological evaluation and molecular docking studies of novel
           benzimidazole derivatives
    • Abstract: Publication date: Available online 30 December 2017
      Source:Computational Biology and Chemistry
      Author(s): Gagandeep Singh, Amanjot Singh, Raman K. Verma, Rajiv Mall, Uzma Azeem
      A novel series of N-substituted-benzimidazolyl linked para substituted benzylidene based molecules containing three pharmacologically potent hydrogen bonding parts namely; 2,4-thiazolidinedione (TZD: a 2,4-dicarbonyl), diethyl malonate (DEM: a 1,3-diester and an isooxazolidinedione analog) and methyl acetoacetate (MAA: a β-ketoester) (6a–11b) were synthesized and evaluated for in vitro α-glucosidase inhibition. The structure of the novel synthesized compounds was confirmed through the spectral studies (LC–MS, 1H NMR, 13C NMR, FT-IR). Comparative evaluation of these compounds revealed that the compound 9b showed maximum inhibitory potential against α-amylase and α-glucosidase giving an IC50 value of 0.54 ± 0.01 μM. Furthermore, binding affinities in terms of G score values and hydrogen bond interactions between all the synthesized compounds and the AA residues in the active site of the protein (PDB code: 3TOP) to that of Acarbose (standard drug) were explored with the help of molecular docking studies. Compound 9b was considered as promising candidate of this series.
      Graphical abstract image

      PubDate: 2018-01-05T19:20:03Z
       
  • Proteome-scale identification of Leishmania infantum for novel vaccine
           candidates: A hierarchical subtractive approach
    • Abstract: Publication date: Available online 24 December 2017
      Source:Computational Biology and Chemistry
      Author(s): Bahareh Vakili, Navid Nezafat, Gholam Reza Hatam, Bijan Zare, Nasrollah Erfani, Younes Ghasemi
      Vaccines are one of the most significant achievements in medical science. However, vaccine design is still challenging at all stages. The selection of antigenic peptides as vaccine candidates is the first and most important step for vaccine design. Experimental selection of antigenic peptides for the design of vaccines is a time-consuming, labor-intensive and expensive procedure. More recently, in the light of computer-aided biotechnology and reverse vaccinology, the precise selection of antigenic peptides and rational vaccine design against many pathogens have developed. In this study, the whole proteome of Leishmania infantum was analyzed using a pipeline of algorithms. From the set of 8045 proteins of L. infantum, sixteen novel antigenic proteins were derived using a hierarchical proteome subtractive analysis. These novel vaccine targets can be utilized as top candidates for designing the new prophylactic or therapeutic vaccines against visceral leishmaniasis. Significantly, all the sixteen novel vaccine candidates are non-allergen antigenic proteins that have not been used for the design of vaccines against visceral leishmaniasis until now.

      PubDate: 2017-12-24T19:00:57Z
       
  • Rational design of methicillin resistance staphylococcus aureus Inhibitors
           through 3D-QSAR, molecular docking and molecular dynamics simulations
    • Abstract: Publication date: Available online 20 December 2017
      Source:Computational Biology and Chemistry
      Author(s): Srilata Ballu, Ramesh Itteboina, Sree Kanth Sivan, Vijjulatha Manga
      Staphylococcus aureus is a gram positive bacterium. It is a leading cause of skin and respiratory infections, osteomyelitis, Ritter's disease, endocarditis, and bacteraemia in the developed world. We employed combined studies of 3D QSAR, molecular docking which are cross validated by molecular dynamics simulations and in silico ADME prediction have been performed on Isothiazoloquinolones inhibitors against methicillin resistance Staphylococcus aureus. Three-dimensional quantitative structure-activity relationship (3D-QSAR) study was applied using comparative molecular field analysis (CoMFA)with Q2of0.578, R2of0.988, and comparative molecular similarity indices analysis (CoMSIA) with Q2 of0.554, R2of0.975. The predictive ability of these model was determined using a test set of molecules that gave acceptable predictive correlation (r2 Pred) values 0.55 and 0.57 of CoMFA and CoMSIA respectively. Docking, simulations were employed to position the inhibitors into protein active site to find out the most probable binding mode and most reliable conformations. Developed models and Docking methods provide guidance to design moleculeswith enhanced activity.
      Graphical abstract image

      PubDate: 2017-12-24T19:00:57Z
       
  • AROHap: An Effective Algorithm for Single Individual Haplotype
           Reconstruction based on Asexual Reproduction Optimization
    • Abstract: Publication date: Available online 14 December 2017
      Source:Computational Biology and Chemistry
      Author(s): Mohammad-H Olyaee, Alireza Khanteymoori
      In this paper, a method for single individual haplotype (SIH) reconstruction using Asexual reproduction optimization (ARO) is proposed. Haplotypes, as a set of genetic variations in each chromosome, contain vital information such as the relationship between human genome and diseases. Finding haplotypes in diploid organisms is a challenging task. Experimental methods are expensive and require special equipment. In SIH problem, we encounter with several fragments and each fragment covers some parts of desired haplotype. The main goal is bi-partitioning of the fragments with minimum error correction (MEC). This problem is addressed as NP-hard and several attempts have been made in order to solve it using heuristic methods. The current method, AROHap, has two main phases. In the first phase, most of the fragments are clustered based on a practical metric distance. In the second phase, ARO algorithm as a fast convergence bio-inspired method is used to improve the initial bi-partitioning of the fragments in the previous step. AROHap is implemented with several benchmark datasets. The experimental results demonstrate that satisfactory results were obtained, proving that AROHap can be used for SIH reconstruction problem.

      PubDate: 2017-12-24T19:00:57Z
       
  • Corrigendum to “Docking assay of small molecule antivirals to p7 of
           HCV” [Comput. Biol. Chem. 53 (2014) 308–317]
    • Abstract: Publication date: Available online 13 December 2017
      Source:Computational Biology and Chemistry
      Author(s): L. Bichmann, Y.-T. Wang, W.B. Fischer


      PubDate: 2017-12-24T19:00:57Z
       
  • Exciton states and optical properties of the CP26 photosynthetic protein
    • Abstract: Publication date: Available online 13 December 2017
      Source:Computational Biology and Chemistry
      Author(s): Daniil V. Khokhlov, Aleksandr S. Belov, Vadim V. Eremin
      The photosynthetic complex CP26, one of the minor antennae of the photosystem II, plays an important role in regulation of the excitation energy transfer in the PSII. Due to instability during isolation and purification, it remained poorly studied from the viewpoint of theoretical chemistry because of the absence of X-ray crystallography data. In this work, using the recently determined three-dimensional structure of the complex we apply the quantum chemical approach to study the properties of exciton states in it. Spectral properties, structure of exciton states and roles of the pigments in the complex and photosystem II are discussed.
      Graphical abstract image

      PubDate: 2017-12-24T19:00:57Z
       
  • Identification of Novel nt-MGAM Inhibitors for Potential Treatment of Type
           2 Diabetes: Virtual Screening, Atom based 3D-QSAR Model, Docking Analysis
           and ADME Study
    • Abstract: Publication date: Available online 12 December 2017
      Source:Computational Biology and Chemistry
      Author(s): Aicha Laoud, Fouad Ferkous, Laura Maccari, Giorgio Maccari, Youcef Saihi, Khaireddine Kraim
      In this study, a virtual screening procedure was applied to identify new potential nt-MGAM inhibitors as a possible medication for type 2 diabetes. To this aim, a series of salacinol analogues were first investigated by docking analysis for their binding to the X-ray structure of the biological target nt-MGAM. Key interactions for ligand binding into the receptor active site were identified which shared common features to those found for other known inhibitors, which strengthen the results of this study. 3D QSAR model was then built and showed to be statistically significant and with a good predictive power for the training (R2 =0.99, SD=0.17, F=555.3 and N=27) and test set (Q2 =0.81, Pearson(r)=0.92, RMSE=0.52, N=08). The model was then used to virtually screen the ZINC database with the aim of identifying novel chemical scaffolds as potential nt-MGAM inhibitors. Further, in silico predicted ADME properties were investigated for the most promising molecules. The outcome of this investigation sheds light on the molecular characteristics of the binding of salacinol analogues to nt-MGAM enzyme and identifies new possible inhibitors which have the potential to be developed into drugs, thus significantly contributing to the design and optimization of therapeutic strategies against type 2 diabetes.
      Graphical abstract image

      PubDate: 2017-12-13T11:23:55Z
       
  • Experimental and density functional theory studies on benzalkonium
           ibuprofenate, a double active pharmaceutical ingredient
    • Abstract: Publication date: Available online 9 December 2017
      Source:Computational Biology and Chemistry
      Author(s): K.P. Safna Hussan, M. Shahin Thayyil, Vijisha K. Rajan, K. Muraleedharan
      Molecular aspects of a double active pharmaceutical ingredient in ionic liquid form, benzalkonium ibuprofenate (BaIb), were studied using density functional theory (DFT/B3LYP/6-31+G (d, p)). A detailed discussion on optimized geometry, energy, heat and the enthalpy of BaIb was carried out. The computed vibrational results agree well with the experimental results. The stability and biological activity were compared to the parent drugs on the basis of global descriptive parameters. The electrophilic and nucleophilic sites were pointed out in the MESP structures well evidently. NBO analysis was also done to predict the relative aromaticity, delocalization effects and the contribution towards stabilization energy of the title compound. The information about non-covalent, non-ionic weak interaction between the cation and anion were obtained from the list of Mulliken charges and NBO analysis.
      Graphical abstract image

      PubDate: 2017-12-13T11:23:55Z
       
  • In silico toxicity profiling of natural product compound libraries from
           African flora with anti-malarial and anti-HIV properties
    • Abstract: Publication date: Available online 6 December 2017
      Source:Computational Biology and Chemistry
      Author(s): Pascal Amoa Onguéné, Conrad V. Simoben, Ghislain W. Fotso, Kerstin Andrae-Marobela, Sami A. Khalid, Bonaventure T. Ngadjui, Luc Meva’a Mbaze, Fidele Ntie-Kang
      This paper describes an analysis of the diversity and chemical toxicity assessment of three chemical libraries of compounds from African flora (the p-ANAPL, AfroMalariaDb, and Afro-HIV), respectively containing compounds exhibiting activities against diverse diseases, malaria and HIV. The diversity of the three data sets was done by comparison of the three most important principal components computed from standard molecular descriptors. This was also done by a study of the most common substructures (MCSS keys). Meanwhile, the in silico toxicity predictions were done through the identification of chemical structural alerts using Lhasa's knowledge based Derek system. The results show that the libraries occupy different chemical space and that only an insignificant part of the respective libraries could exhibit toxicities beyond acceptable limits. The predicted toxicities end points for compounds which were predicted to “plausible” were further discussed in the light of available experimental data in the literature. Toxicity predictions are in agreement when using a machine learning approach that employs graph-based structural signatures. The current study sheds further light towards the use of the studied chemical libraries for virtual screening purposes.
      Graphical abstract image

      PubDate: 2017-12-13T11:23:55Z
       
  • IFC Editorial Board
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71


      PubDate: 2017-12-13T11:23:55Z
       
  • Title page
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71


      PubDate: 2017-12-13T11:23:55Z
       
  • Identification of effective DNA barcodes for Triticum plants through
           chloroplast genome-wide analysis
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Mohamed Awad, Ragab M. Fahmy, Kareem A. Mosa, Mohamed Helmy, Fawzy A. El-Feky
      The Egyptian flora is rich with a large number of Triticum plants, which are very difficult to discriminate between in the early developmental stages. This study assesses the significance of using two DNA Barcoding loci (matK and rbcL) in distinguishing between 18 different Triticum accessions in Egypt. We isolated and sequenced 15 rbcL and six matK fragments, but our analysis of the resultant sequences demonstrated a limited ability of matK and rbcL in distinguishing between Triticum accessions. Therefore, we pursued a bioinformatics approach to determine the most useful loci which may be used as DNA barcodes for the Triticum spp. We obtained the 10 available chloroplast genomes of the 10 Triticum species and sub-species from NCBI, and performed chloroplast genome-wide analysis to find the potential barcode loci. A total of 134 chloroplast genes, gene combinations, intergenic regions and intergenic region combinations were tested using a Tree-based method. We were unable to discriminate between Triticum species by using chloroplast genes, gene combinations and intergenic regions. However, a combination of the intergenic region (trnfM-trnT) with either (trnD-psbM), (petN-trnC), (matK-rps16) or (rbcL-psaI) demonstrated a very high discrimination capacity, suggesting their utilization as DNA barcodes for the Triticum plants. Furthermore, our novel DNA barcodes demonstrated high discrimination capacity for other Poaceae members.
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      PubDate: 2017-12-13T11:23:55Z
       
  • Evaluation of degradation mechanism of chlorhexidine by means of Density
           Functional Theory calculations
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Michele Aparecida Salvador, Camila Pinheiro Sousa, Simone Morais, Pedro de Lima-Neto, Adriana Nunes Correia, Paula Homem-de-Mello
      Chlorhexidine (CHD), a germicidal drug, has degradation products that can be hemotoxic and carcinogenic. However, there is no consensus in literature about the degradation pathway. In order to shed light on that mechanism, we have employed Density Functional Theory to study reactants, in different protonation states, products and intermediates involved in the different pathways. Based on free energy values comparison and frontier molecular orbital analysis, we have obtained the most stable structures in each protonation state. CHD in saturated form has HOMO localized in one p-chloroaniline, and, due to molecule’s symmetry, HOMO-1 has contributions from the other side of the molecule, but mainly from the biguanide portion of the molecule, instead of from the p-chloroaniline. For the saturated form, we have studied two possible degradation pathways, starting from the monoprotonated structure, and three pathways starting from the neutral structure. We found out that the mechanisms proposed in literature, whose pathways lead to p-chloroaniline (PCA) formation in a smaller number of steps, are more likely than the mechanisms with more intermediate steps or pathways that do not predict PCA formation. Also, based on free energy results, we have found that the formation of another sub-product (PBG-AU) is favorable as well.
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      PubDate: 2017-12-13T11:23:55Z
       
  • Predicting microRNA biological functions based on genes discriminant
           analysis
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Tao Ding, Junhua Xu, Mengmeng Sun, Shanshan Zhu, Jie Gao
      Although thousands of microRNAs (miRNAs) have been identified in recent experimental efforts, it remains a challenge to explore their specific biological functions through molecular biological experiments. Since those members from same family share same or similar biological functions, classifying new miRNAs into their corresponding families will be helpful for their further functional analysis. In this study, we initially built a vector space by characterizing the features from miRNA sequences and structures according to their miRBase family organizations. Then we further assigned miRNAs into its specific miRNA families by developing a novel genes discriminant analysis (GDA) approach in this study. As can be seen from the results of new families from GDA, in each of these new families, there was a high degree of similarity among all members of nucleotide sequences. At the same time, we employed 10-fold cross-validation machine learning to achieve the accuracy rates of 68.68%, 80.74%, and 83.65% respectively for the original miRNA families with no less than two, three, and four members. The encouraging results suggested that the proposed GDA could not only provide a support in identifying new miRNAs’ families, but also contributing to predicting their biological functions.

      PubDate: 2017-12-13T11:23:55Z
       
  • Signaling pathway impact analysis by incorporating the importance and
           specificity of genes (SPIA-IS)
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Hongyuan Fang, Xianbin Li, Xiangzhen Zan, Liangzhong Shen, Runnian Ma, Wenbin Liu
      rlying biology of differentially expressed genes and proteins. Although various approaches have been proposed to identify cancer-related pathways, most of them only partially consider the influence of those differentially expressed genes, such as the gene numbers, their perturbation in the signaling transduction, and the interaction between genes. Signaling-pathway impact analysis (SPIA) provides a convenient framework which considers both the classical enrichment analysis and the actual perturbation on a given pathway. In this study, we extended previous proposed SPIA by incorporating the importance and specificity of genes (SPIA-IS). We applied this approach to six datasets for colorectal cancer, lung cancer, and pancreatic cancer. Results from these datasets showed that the proposed SPIA-IS could effectively improve the performance of the original SPIA in identifying cancer-related pathways.

      PubDate: 2017-12-13T11:23:55Z
       
  • Factors analysis of protein O-glycosylation site prediction
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Xuemei Yang, Henry Han
      To improve the prediction accuracy of O-glycosylation sites, and analyze the structure of the O-glycosylation sites, factor analysis based prediction is proposed in this study. Our studies show that factor analysis strongly boosts machine learning algorithms’ performance in glycosylation site prediction besides demonstrates advantages compared to principal component analysis and nonnegative matrix factorization. In addition, we have found that factor analysis based linear discriminant analysis seem to be a desirable method in O-glycosylation site prediction for its advantage in both accuracy and time complexity than other machine learning methods. To the best of our knowledge, it is the first work to employ factor analysis in glycosylation site prediction and will inspire more future work in this topic.

      PubDate: 2017-12-13T11:23:55Z
       
  • Solving probability reasoning based on DNA strand displacement and
           probability modules
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Qiang Zhang, Xiaobiao Wang, Xiaojun Wang, Changjun Zhou
      In computation biology, DNA strand displacement technology is used to simulate the computation process and has shown strong computing ability. Most researchers use it to solve logic problems, but it is only rarely used in probabilistic reasoning. To process probabilistic reasoning, a conditional probability derivation model and total probability model based on DNA strand displacement were established in this paper. The models were assessed through the game “read your mind.” It has been shown to enable the application of probabilistic reasoning in genetic diagnosis.

      PubDate: 2017-12-13T11:23:55Z
       
  • Systematic identification of the druggable interactions between human
           protein kinases and naturally occurring compounds in endometriosis
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Lai Jiang, Chaoliang Tang, Jie Rao, Qing Xue, Hao Wu, Dabao Wu, Aijun Zhang, Ling Chen, Zhen Shen, Lei Lei
      Diverse kinase signaling pathways have been involved in the pathogenesis of endometriosis (EM), which can be modulated either by directly targeting the hub kinases or by indirectly regulating marginal members in the pathways. Here, a systematic kinase–inhibitor interaction profile was created for 8 naturally occurring compounds against 20 human protein kinases. The compounds are all non-sterid that have been reported as pharmacologically active molecular entities potential for EM therapeutics, while the kinases were curated via gene ontology terms enriched from the gene co-citation network with EM. The resulting profile was analyzed at structural, energetic and dynamic levels to identify druggable kinase–compound interactions. The compounds Gossypol, Curcumin and EGCG showed a similar interaction profile across these kinases; they can bind tightly to the top-listed kinases in gene ontology, while the compounds Marrubiin, Apigenin and DIM were predicted to exhibit generally weak affinity for the 20 curated kinases. The JNK kinase, a MAPK family member, was identified as a putative candidate of druggable target for EM therapeutics; the inhibitory activity of eight naturally occurring compounds as well as a sophisticated kinase inhibitor SP600125 against the JNK was tested using enzymatic activity analysis. As might be expected, the Gossypol and EGCG were determined to have high inhibitory activity at namomolar level (IC50 =55 and 94nM, respectively), which are comparable with or better than the positive control SP600125 (IC50 =76nM), while other tested compounds exhibited weak inhibition (IC50 >100nM) or bad potency (IC50 =n.d.) against the kinase.
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      PubDate: 2017-12-06T15:27:21Z
       
  • Discovery of potential Zika virus RNA polymerase inhibitors by
           docking-based virtual screening
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Anjali Singh, Nandan Kumar Jana
      Zika virus (ZIKV) infection has been associated with Guillain-Barre syndrome in adults and microcephaly in infants. The existence of insufficient structural data in most of the protein databases hinders the synthesis of anti-ZIKV pharmaceutics. In this work, we attempted to model the catalytic domain of the ZIKV RNA polymerase (RdRpC) along with a detailed assessment of conserved aspartates in ZIKV RdRpC palm domain as potential drug targets. The conserved and catalytically active aspartate residues present in the predicted RdRpC protein were virtually screened against a ZINC database for inhibitors, and the selected potential drug candidates were further filtered based on their ADMET profiles. One of the pharmacokinetically active compounds (Ligand 6) showed a remarkable docking profile against the strictly conserved aspartate residues of the RdRpC active site. We hypothesize that the Ligand 6 may form a potential drug candidate for RdRpC inhibition in the clinical treatment of ZIKV infection.
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      PubDate: 2017-12-06T15:27:21Z
       
  • Epitopes based drug design for dengue virus envelope protein: A
           computational approach
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Abdul Wadood, Aamir Mehmood, Huma Khan, Muhammad Ilyas, Ayaz Ahmad, Mohammed Alarjah, Tareq Abu-Izneid
      Dengue virus (DENV) has emerged as a rapidly spreading epidemic throughout the tropical and subtropical regions around the globe. No suitable drug has been designed yet to fight against DENV, therefore, the need for safe and effective antiviral drug has become imperative. The envelope protein of DENV is responsible for mediating the fusion process between viral and host membranes. This work reports an in silico approach to target B and T cell epitopes for dengue envelope protein inhibition. A conserved region “QHGTI” in B and T cell epitopes of dengue envelope glycoprotein was confirmed to be valid for targeting by visualizing its interactions with the host cell membrane TIM-1 protein which acts as a receptor for serotype 2 and 3. A reverse pharmacophore mapping approach was used to generate a seven featured pharmacophore model on the basis of predicted epitope. This pharmacophore model as a 3D query was used to virtually screen a chemical compounds dataset “Chembridge”. A total of 1010 compounds mapped on the developed pharmacophore model. These retrieved hits were subjected to filtering via Lipinski’s rule of five, as a result 442 molecules were shortlisted for further assessment using molecular docking. Finally, 14 hits of different structural properties having interactions with the active site residues of dengue envelope glycoprotein were selected as lead candidates. These structurally diverse lead candidates have strong likelihood to act as further starting structures in the development of novel and potential drugs for the treatment of dengue fever.
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      PubDate: 2017-12-06T15:27:21Z
       
  • Molecular modeling of cationic porphyrin-anthraquinone hybrids as DNA
           topoisomerase IIβ inhibitors
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Muhammad Arba, Ruslin, Sunandar Ihsan, Setyanto Tri Wahyudi, Daryono H. Tjahjono
      Human DNA Topoisomerase II has been regarded as a promising target in anticancer drug discovery. In the present study, we designed six porphyrin-anthraquinone hybrids bearing pyrazole or pyridine group as meso substituents and evaluated their potentials as DNA Topoisomerase IIβ inhibitor. First, we investigated the binding orientation of porphyrin hybrids into DNA topoisomerase IIβ employing AutoDock 4.2 and then performed 20-ns molecular dynamics simulations to see the dynamic stability of each porphyrin-Topo IIβ complex using Amber 14. We found that the binding of porphyrin hybrids occured through intercalation and groove binding mode in addition interaction with the amino acid residues constituting the active cavity of Topo IIβ. Each porphyrin-Topo IIβ complex was stabilized during 20-ns dynamics simulations. The MM-PBSA free energy calculation shows that the binding affinities of porphyrin hybrids were modified with the number of meso substituent. Interestingly, the affinity of all porphyrin hybrids to Topo IIβ was stronger than that of native ligand (EVP), indicating the potential of the designed porphyrin to be considered in experimental research.
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      PubDate: 2017-12-06T15:27:21Z
       
  • A novel approach for dimension reduction of microarray
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Rabia Aziz, C.K. Verma, Namita Srivastava
      This paper proposes a new hybrid search technique for feature (gene) selection (FS) using Independent component analysis (ICA) and Artificial Bee Colony (ABC) called ICA+ABC, to select informative genes based on a Naïve Bayes (NB) algorithm. An important trait of this technique is the optimization of ICA feature vector using ABC. ICA+ABC is a hybrid search algorithm that combines the benefits of extraction approach, to reduce the size of data and wrapper approach, to optimize the reduced feature vectors. This hybrid search technique is facilitated by evaluating the performance of ICA+ABC on six standard gene expression datasets of classification. Extensive experiments were conducted to compare the performance of ICA+ABC with the results obtained from recently published Minimum Redundancy Maximum Relevance (mRMR) +ABC algorithm for NB classifier. Also to check the performance that how ICA+ABC works as feature selection with NB classifier, compared the combination of ICA with popular filter techniques and with other similar bio inspired algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result shows that ICA+ABC has a significant ability to generate small subsets of genes from the ICA feature vector, that significantly improve the classification accuracy of NB classifier compared to other previously suggested methods.
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      PubDate: 2017-12-06T15:27:21Z
       
  • Designing anti-Zika virus peptides derived from predicted human-Zika virus
           protein-protein interactions
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Tom Kazmirchuk, Kevin Dick, Daniel. J. Burnside, Brad Barnes, Houman Moteshareie, Maryam Hajikarimlou, Katayoun Omidi, Duale Ahmed, Andrew Low, Clara Lettl, Mohsen Hooshyar, Andrew Schoenrock, Sylvain Pitre, Mohan Babu, Edana Cassol, Bahram Samanfar, Alex Wong, Frank Dehne, James. R. Green, Ashkan Golshani
      The production of anti-Zika virus (ZIKV) therapeutics has become increasingly important as the propagation of the devastating virus continues largely unchecked. Notably, a causal relationship between ZIKV infection and neurodevelopmental abnormalities has been widely reported, yet a specific mechanism underlying impaired neurological development has not been identified. Here, we report on the design of several synthetic competitive inhibitory peptides against key pathogenic ZIKV proteins through the prediction of protein–protein interactions (PPIs). Often, PPIs between host and viral proteins are crucial for infection and pathogenesis, making them attractive targets for therapeutics. Using two complementary sequence-based PPI prediction tools, we first produced a comprehensive map of predicted human-ZIKV PPIs (involving 209 human protein candidates). We then designed several peptides intended to disrupt the corresponding host-pathogen interactions thereby acting as anti-ZIKV therapeutics. The data generated in this study constitute a foundational resource to aid in the multi-disciplinary effort to combat ZIKV infection, including the design of additional synthetic proteins.
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      PubDate: 2017-12-06T15:27:21Z
       
  • A pipeline for proteome-scale identification and studies on hormone
           sensitive lipases in Mycobacterium tuberculosis
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Durairaj Sherlin, Sharmila Anishetty
      Hormone sensitive lipases (HSLs) play an important role in the survival of M. tuberculosis during dormancy. They help in the utilization of fatty acids from stored lipids. The objective of the current study was to identify all HSLs from the proteome of M. tuberculosis H37Rv. We have developed a novel HSL identification pipeline, based on amino acid sequence homology, presence of conserved motifs and other sequence features deciphered from known HSL dataset. Through this pipeline, we identified 10 proteins as putative HSLs in M. tuberculosis. We have annotated a lipase LipT, as putative p-nitrobenzyl esterase and also identified a new motif “PGG” which is a possible characteristic motif of a subfamily of HSLs.
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      PubDate: 2017-12-06T15:27:21Z
       
  • Antiviral potential of natural compounds against influenza virus
           hemagglutinin
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): S. Kannan, P. Kolandaivel
      Influenza virus of different subtypes H1N1, H2N2, H3N2 and H5N1 cause many human pandemic deaths and threatening the people worldwide. The Hemagglutinin (HA) protein mediates viral attachment to host receptors act as an attractive target. The sixteen natural compounds have been chosen to target the HA protein. Molecular docking studies have been performed to find binding affinity of the compounds. Out of the sixteen, three compounds CI, CII and CIII found to posses a higher binding affinity. The molecular dynamics (MD) simulation has been performed to study the structural, dynamical properties for the nine different complexes CI, CII, CIII bound with H1, H2, H3 proteins and the results were compared. The molecular mechanics Poission-Boltzmann surface area (MM-PBSA) method is used to compare the binding free energy, its different energy components and per residue binding contribution. The H1 subtype shows higher binding preference for all the curcumin derivatives than H2 and H3. The binding capability of protein subtypes with curcumin derivatives and the binding affinity of curcumin compounds are in the order H1>H2>H3 and CI>CII>CIII respectively. The two -O-CH3- groups present in the CI compound help to have strong binding with HA protein than CII and CIII. The van der Waals interaction energy plays a significant role for binding in all the complexes. The hydrogen bonding interactions were monitored throughout the MD simulation. The conserved region (153–155) and the helix region (193–194) of H1, H2, H3 protein subtypes are found to possess higher binding susceptibility for binding of the curcumin derivatives.
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      PubDate: 2017-12-06T15:27:21Z
       
  • Penalized estimation of sparse concentration matrices based on prior
           knowledge with applications to placenta elemental data
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Jai Woo Lee, Tracy Punshon, Erika L. Moen, Margaret R. Karagas, Jiang Gui
      Identifying patterns of association or dependency among high-dimensional biological datasets with sparse precision matrices remains a challenge. In this paper, we introduce a weighted sparse Gaussian graphical model that can incorporate prior knowledge to infer the structure of the network of trace element concentrations, including essential elements as well as toxic metals and metaloids measured in the human placentas. We present the weighted L1 penalized regularization procedure for estimating the sparse precision matrix in the setting of Gaussian graphical models. First, we use simulation models to demonstrate that the proposed method yields a better estimate of the precision matrix than the procedures that fail to account for the prior knowledge of the network structure. Then, we apply this method to estimate sparse element concentration matrices of placental biopsies from the New Hampshire Birth Cohort Study. The chemical architecture for elements is complex; thus, the method proposed herein was applied to infer the dependency structures of the elements using prior knowledge of their biological roles.

      PubDate: 2017-12-06T15:27:21Z
       
  • Selective ATP competitive leads of CDK4: Discovery by 3D-QSAR
           pharmacophore mapping and molecular docking approach
    • Abstract: Publication date: December 2017
      Source:Computational Biology and Chemistry, Volume 71
      Author(s): Rohini Rondla, Lavanya Souda PadmaRao, Vishwanath Ramatenki, Aboubakr Haredi-Abdel-Monsef, Sarita Rajender Potlapally, Uma Vuruputuri
      The discovery of ATP competitive CDK4 inhibitors is an on-going challenging task in cancer therapy. Here, an attempt has been made to develop new leads targeting ATP binding site of CDK4 by applying 3D-QSAR pharmacophore mapping and molecular docking methods The outcome of 6 leads offers a significant contribution for selective CDK4 inhibition, since they show potential binding interactions with Val96, Arg101, and Glu144 residues of CDK4, that are unique and from other kinases. It is worth noting that there is a striking similarity in binding interactions of the leads and known CDK4 inhibitors, namely Abemaciclib, Palbociclib and Ribociclib. Further key features, including high dock score value, good predicted activity, scaffold diversity, and the acceptable ADME profile of leads, provide a great opportunity for the development of highly potent and selective ATP competitive inhibitors of CDK4.
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      PubDate: 2017-12-06T15:27:21Z
       
  • A novel in silico minigene vaccine based on CD4+ T-helper and B-cell
           epitopes of EG95 isolates for vaccination against cystic echinococcosis
    • Abstract: Publication date: Available online 23 November 2017
      Source:Computational Biology and Chemistry
      Author(s): Mohammad M. Pourseif, Gholamali Moghaddam, Behrouz Naghili, Nazli Saeedi, Sepideh Parvizpour, Ahmad Nematollahi, Yadollah Omidi
      EG95 oncospheral antigen plays a crucial role in Echinococcus granulosus pathogenicity. Considering the diversity of antigen among different EG95 isolates, it seems to be an ideal antigen for designing a universal multivalent minigene vaccine, so-called multi-epitope vaccine. This is the first in silico study to design a construct for the development of global EG95-based hydatid vaccine against E. granulosus in intermediate hosts. After antigen sequence selection, the three-dimensional structure of EG95 was modeled and multilaterally validated. The preliminary parameters for B-cell epitope prediction were implemented such as the possible transmembrane helix, signal peptide, post-translational modifications and allergenicity. The high ranked linear and conformational B-cell epitopes derived from several online web-servers (e.g., ElliPro, BepiPred v1.0, BcePred, ABCpred, SVMTrip, IEDB algorithms, SEPPA v2.0 and Discotope v2.0) were utilized for multiple sequence alignment and then for engineering the vaccine construct. T-helper based epitopes were predicted by molecular docking between the high frequent ovar class II allele (Ovar-DRB1*1202) and hexadecamer fragments of the EG95 protein. Having used the immune-informatics tools, we formulated the first EG95-based minigene vaccine based on T-helper epitope with high-binding affinity to the ovar MHC allele. This designed construct was analyzed for different physicochemical properties. It was also codon-optimized for high-level expression in Escherichia coli k12. Taken all, we propose the present in silico vaccine constructs as a promising platform for the generation of broadly protective vaccines for species and genus-specific immunization of the natural hosts of the parasite.
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      PubDate: 2017-12-06T15:27:21Z
       
  • Structure Based Virtual Screening of the Ebola Virus Trimeric Glycoprotein
           Using Consensus Scoring
    • Abstract: Publication date: Available online 22 November 2017
      Source:Computational Biology and Chemistry
      Author(s): Abdulmujeeb T. Onawole, Temitope U. Kolapo, Kazeem O. Sulaiman, Rukayat O. Adegoke
      Ebola virus cause zoonotic viral infection with a highly fatal effect on humans and a potential risk of global spread. The recent resurgence of Ebola epidemic in Democratic Republic of Congo in few months ago, coupled with the fact that there is presently no approved antiviral drug against EBOV, demands for timely efficient and effective antiviral drug with deserving market approval for the treatment of Ebola virus disease. The role and acceptance of computational tools in the drug discovery process cannot be overemphasized vis-a-viz the identification of crystal structure of the target protein. The main target of potential vaccines that are recently undergoing clinical trials is trimeric glycoprotein (GP) of the EBOV and its exact crystal structure was used in this structure based virtual screening study, with the aid of consensus scoring to select three possible hit compounds from about 36 million compounds in MCULE’s database. Amongst the selected hit compounds, SC-2 (C21H19ClN4O4) show desired features with respect to drug likeness, ligand efficiency metrics, solubility, absorption and distribution properties and non-carcinogenicity to emerge as the most promising compound that can be optimized to lead compound against the GP EBOV. The binding mode showed that SC-2 is well embedded within the trimeric chains of the GP EBOV with interesting molecular interactions with some amino acids. The SC-2 hit compound, upon its optimization to lead, might be a good potential candidate with efficacy against the EBOV pathogen and subsequently receive necessary approval to be used as antiviral drug for the treatment of EVD.
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      PubDate: 2017-12-06T15:27:21Z
       
  • Identification of new antibacterial targets in RNA polymerase of
           Mycobacterium tuberculosis by detecting positive selection sites
    • Abstract: Publication date: Available online 21 November 2017
      Source:Computational Biology and Chemistry
      Author(s): QingBiao Wang, Yiqin Xu, Zhuoya Gu, Nian Liu, Ke Jin, Yao Li, M. James C. Crabbe, Yang Zhong
      Bacterial RNA polymerase (RNAP) is an effective target for antibacterial treatment. In order to search new potential targets in RNAP of Mycobacterium, we detected adaptive selections of RNAP related genes in 13 strains of Mycobacterium by phylogenetic analysis. We first collected sequences of 17 genes including rpoA, rpoB, rpoC, rpoZ, and sigma factor A-M. Then maximum likelihood trees were constructed, followed by positive selection detection. We found that sigG shows positive selection along the clade (M. tuberculosis, M. bovis), suggesting its important evolutionary role and its potential to be a new antibacterial target. Moreover, the regions near 933Cys and 935His on the rpoB subunit of M. tuberculosis showed significant positive selection, which could also be a new attractive target for anti-tuberculosis drugs.
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      PubDate: 2017-12-06T15:27:21Z
       
  • Design and verification of halogen-bonding system at the complex interface
           of human fertilization-related MUP PDZ5 domain with CAMK’s C-terminal
           peptide
    • Abstract: Publication date: Available online 21 November 2017
      Source:Computational Biology and Chemistry
      Author(s): Juan Wang, Yunjie Guo, Xue Zhang
      Calmodulin-dependent protein kinase (CAMK) is physiologically activated in fertilized human oocytes and is involved in the Ca2+ response pathways that link the fertilization calmodulin signal to meiosis resumption and cortical granule exocytosis. The kinase has an unstructured C-terminal tail that can be recognized and bound by the PDZ5 domain of its cognate partner, the multi-PDZ domain protein (MUP). In the current study, we reported a rational biomolecular design of halogen-bonding system at the complex interface of CAMK’s C-terminal peptide with MUP PDZ5 domain by using high-level computational approaches. Four organic halogens were employed as atom probes to explore the structural geometry and energetic property of designed halogen bonds in the PDZ5–peptide complex. It was found that the heavier halogen elements such as bromine Br and iodine I can confer stronger halogen bond but would cause bad atomic contacts and overlaps at the complex interface, while fluorine F cannot form effective halogen bond in the complex. In addition, the halogen substitution at different positions of peptide’s aromatic ring would result in distinct effects on the halogen-bonding system. The computational findings were then verified by using fluorescence analysis; it is indicated that the halogen type and substitution position play critical role in the interaction strength of halogen bonds, and thus the PDZ5–peptide binding affinity can be improved considerably by optimizing their combination.
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      PubDate: 2017-12-06T15:27:21Z
       
  • Comparative genome based cis-elements analysis in the 5′ upstream and
           3′ downstream region of Cell wall invertase and Phenylalanine ammonia
           lyase in Nicotiana benthamiana
    • Abstract: Publication date: Available online 13 November 2017
      Source:Computational Biology and Chemistry
      Author(s): S.A. Sheshadri, M.J. Nishanth, N. Harita, P. Brindha, S. Bindu
      Plant secondary metabolites are widely used in human disease treatment; though primary metabolism provides precursors for secondary metabolism, not much has been studied to unravel the link connecting both the processes. Most common form of gene regulation interconnecting diverse metabolism occurs at the transcriptional and/or posttranscriptional level mediated by regulatory cis-elements. The present study aims at understanding the common cis-elements network connecting the major primary metabolic enzyme, cell wall invertase (CWIN) and secondary metabolism genes in Nicotiana benthamiana (N. benthamiana). The CWIN and thirty one other gene sequences were extracted from N. benthamiana genome, followed by cis-element analysis of their 5′ upstream and 3′ downstream region using different programs (Genomatix software suite; PLACE and PlantCARe). Comparative cis-element analysis of CWIN (N. benthamiana and other plant species) and other primary, secondary metabolism and transcription factor genes (N. benthamiana) revealed the occurrence of common stress associated cis-elements. Predominantly, AHBP, L1BX, MYBL, MADS, MYBS, GTBX, DOFF and CCAF were found in the 5′ upstream region of all genes, whereas AHBP, MYBL, L1BX, HEAT, CCAF and KAN1 were largely occurring in the 3′ downstream region of all genes; indicating common function of these elements in transcriptional and posttranscriptional gene regulation. Further, genomic analysis using FGENESH, GenScan and homology based methods (BlastX and BlastN) was performed on the N. benthamiana contigs harboring CWIN and PAL, in an attempt to identify genomic neighborhood genes. The 5′ upstream and 3′ downstream region of genes in the genomic neighborhood of CWIN and PAL were also subjected to similar cis-element analysis, and the results indicated cis-elements profile similar to CWIN, PAL and other primary, secondary metabolism and transcription factor genes. The results of evolutionary studies confirmed that the 5′ upstream region of NbCWINs significantly showed more proximity to secondary metabolism genes 4CL and the redox gene SOD, followed by the phenylpropanoid pathway gene CHI. The 3′ downstream regions of NbCWINs were more closely related to other plant CWINs, followed by the redox gene, SOD and primary metabolism gene FBA. Thus, the commonly found stress responsive cis-elements in our study can play a vital role in modulating key pathways of both primary and secondary metabolism; thereby postulating their role in regulating plant growth and metabolisms under unfavorable growth conditions.
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      PubDate: 2017-12-06T15:27:21Z
       
  • A novel feature selection for RNA-seq analysis
    • Abstract: Publication date: Available online 5 November 2017
      Source:Computational Biology and Chemistry
      Author(s): Henry Han
      RNA-seq data are challenging existing omics data analytics for its volume and complexity. Although quite a few computational models were proposed from different standing points to conduct differential expression (D.E.) analysis, almost all these methods do not provide a rigorous feature selection for high-dimensional RNA-seq count data. Instead, most or even all genes are invited into differential calls no matter they have real contributions to data variations or not. Thus, it would inevitably affect the robustness of D.E. analysis and lead to the increase of false positive ratios. In this study, we presented a novel feature selection method: nonnegative singular value approximation (NSVA) to enhance RNA-seq differential expression analysis by taking advantage of RNA-seq count data's non-negativity. As a variance-based feature selection method, it selects genes according to its contribution to the first singular value direction of input data in a data-driven approach. It demonstrates robustness to depth bias and gene length bias in feature selection in comparison with its five peer methods. Combining with state-of-the-art RNA-seq differential expression analysis, it contributes to enhancing differential expression analysis by lowering false discovery rates caused by the biases. Furthermore, we demonstrated the effectiveness of the proposed feature selection by proposing a data-driven differential expression analysis: NSVA-seq, besides conducting network marker discovery.

      PubDate: 2017-12-06T15:27:21Z
       
  • Structure, Electronic, Optical and Thermodynamic behavior on the
           Polymerization of PMMA: A DFT Investigation
    • Abstract: Publication date: Available online 1 November 2017
      Source:Computational Biology and Chemistry
      Author(s): Esha V. Shah, Chetna M. Patel, Debesh R. Roy
      A density functional theory based scrutiny is implemented on the structure, electronic, optical and thermodynamic properties of the Poly (Methyl MethAcrylate) polymers (PMMA or nMMA; n=1–5). The quantum chemical descriptors e.g., HOMO-LUMO gap, ionization potential, chemical hardness, binding energies etc. of the PMMA polymers provides the measure for the structural and electronic properties. The parameters polarizability (α) and hyperpolarizability (β) provides information for the non-linear optical (NLO) properties of the polymers. The absorption range of the PMMA polymer in the electromagnetic radiation spectrum during its growth is assessed by the UltraViolet-Visible (UV–vis) optical absorption spectra. To gain further insight on the origin of stability during the polymerization process, we have simulated frontier molecular orbitals (FMOs) and various thermodynamic properties, viz., entropy (S), enthalpy (H) and Gibbs free energy (G).
      Graphical abstract image

      PubDate: 2017-12-06T15:27:21Z
       
  • Predicting lysine glycation sites using bi-profile bayes feature
           extraction
    • Abstract: Publication date: Available online 12 October 2017
      Source:Computational Biology and Chemistry
      Author(s): Zhe Ju, Juhe Sun, Yanjie Li, Li Wang
      Glycation is a nonenzymatic post-translational modification which has been found to be involved in various biological processes and closely associated with many metabolic diseases. The accurate identification of glycation sites is important to understand the underlying molecular mechanisms of glycation. As the traditional experimental methods are often labor-intensive and time-consuming, it is desired to develop computational methods to predict glycation sites. In this study, a novel predictor named BPB_GlySite is proposed to predict lysine glycation sites by using bi-profile bayes feature extraction and support vector machine algorithm. As illustrated by 10-fold cross-validation, BPB_GlySite achieves a satisfactory performance with a Sensitivity of 63.68%, a Specificity of 72.60%, an Accuracy of 69.63% and a Matthew’s correlation coefficient of 0.3499. Experimental results also indicate that BPB_GlySite significantly outperforms three existing glycation sites predictors: NetGlycate, PreGly and Gly-PseAAC. Therefore, BPB_GlySite can be a useful bioinformatics tool for the prediction of glycation sites. A user-friendly web-server for BPB_GlySite is established at 123.206.31.171/BPB_GlySite/.
      Graphical abstract image

      PubDate: 2017-10-14T13:23:45Z
       
  • Ligand-Based Computational Modelling of Platelet-Derived Growth Factor
           Beta Receptor Leading to New Angiogenesis Inhibitory Leads
    • Abstract: Publication date: Available online 10 October 2017
      Source:Computational Biology and Chemistry
      Author(s): Rua’a A Al-Aqtash, Malek A. Zihlif, Hana Hammad, Zeyad D. Nassar, Jehad Al Meliti, Mutasem O. Taha
      Platelet derived growth factor beta receptor (PDGFR- β) plays an important role in angiogenesis. PDGFR-β expression is correlated with increased vascularity and maturation of blood vessels in cancer. Pharmacophore modeling and quantitative structure-activity relationship (QSAR) analysis were combined to explore the structural requirements for ligand-PDGFR-β recognition using 107 known PDGFR-β inhibitors. Genetic function algorithm (GFA) coupled to k nearest neighbor (kNN) and multiple linear regression (MLR) analysis were employed to generate predictive QSAR models based on optimal combinations of pharmacophores and physicochemical descriptors. The successful pharmacophores were complemented with exclusion spheres to optimize their receiver operating characteristic curve (ROC) profiles. The QSAR models and their associated pharmacophore hypotheses were validated by identification and experimental evaluation of new angiogenesis inhibitory leads retrieved from the National Cancer Institute (NCI) structural database. Two hits illustrated low micromolar IC50 values in two distinct anti-angiogenesis bioassays.
      Graphical abstract image

      PubDate: 2017-10-11T13:19:28Z
       
  • 3D-QSAR studies on indole and 7-azoindole derivatives as ROCK-2
           inhibitors: An Integrative Computational Approach
    • Abstract: Publication date: Available online 9 October 2017
      Source:Computational Biology and Chemistry
      Author(s): Santhosh Kumar Nagarajan, Sathya Babu, Honglae Sohn, Thirumurthy Madhavan
      Rho Kinases (ROCK) has been found to regulate a wide range of fundamental cell functions such as contraction, motility, proliferation, and apoptosis. Recent experiments have defined new functions of ROCKs in cells, including centrosome positioning and cell-size regulation, which might contribute to various physiological and pathological states. In this study, we have performed pharmacophore modeling and 3D QSAR studies on a series of 36 indoles and 7-azoindoles derivatives as ROCK2 inhibitors to elucidate the structural variations with their inhibitory activities. Ligand based CoMFA and CoMSIA models were generated based on three different alignment methods such as systematic search, simulated annealing and pharmacophore. A total of 15 CoMFA models and 27 CoMSIA were generated using different alignments. One model from each alignment is selected based on the statistical values. Contour maps of the selected models were compared, analyzed and reported. The 3D QSAR study revealed that electro positive group linked to the methoxy-benzene ring position of the structure will enhance the biological activity and bulkier substitutions are preferred in the methyl dihydroindole region. Also, it is found that the hydrogen bond donor substituted at the R1 position enhances the inhibitory activity. In future, this study would give proper guidelines to further enhance the activity of novel inhibitors for ROCK2.
      Graphical abstract image

      PubDate: 2017-10-11T13:19:28Z
       
  • HashGO: Hashing Gene Ontology for protein function prediction
    • Abstract: Publication date: Available online 4 October 2017
      Source:Computational Biology and Chemistry
      Author(s): Guoxian Yu, Yingwen Zhao, Chang Lu, Jun Wang
      Gene Ontology (GO) is a standardized and controlled vocabulary of terms that describe the molecular functions, biological roles and cellular locations of proteins. GO terms and GO hierarchy are regularly updated as the accumulated biological knowledge. More than 50,000 terms are included in GO and each protein is annotated with several or dozens of these terms. Therefore, accurately predicting the association between proteins and massive GO terms is rather challenging. To accurately predict the association between massive GO terms and proteins, we proposed a method called Hashing GO for protein function prediction (HashGO in short). HashGO firstly adopts a protein-term association matrix to store available GO annotations of proteins. Then, it tailors a graph hashing method to explore the underlying structure between GO terms and to obtain a series of hash functions to compress the high-dimensional protein-term association matrix into a low-dimensional one. Next, HashGO computes the semantic similarity between proteins based on Hamming distance on that low-dimensional matrix. After that, it predicts missing annotations of a protein based on the annotations of its semantic neighbors. Experimental results on archived GO annotations of two model species (Yeast and Human) show that HashGO not only more accurately predicts functions than other related approaches, but also runs faster than them.

      PubDate: 2017-10-11T13:19:28Z
       
  • Assessment of in vivo organ-uptake and in silico prediction of CYP
           mediated metabolism of DA-Phen, a new dopaminergic agent.
    • Abstract: Publication date: Available online 28 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Flavia Maria Sutera, Libero Italo Giannola, Denise Murgia, Viviana De Caro
      The drug development process strives to predict metabolic fate of a drug candidate, together with its uptake in major organs, whether they act as target, deposit or metabolism sites, to the aim of establish a relationship between the pharmacodynamics and the pharmacokinetics and highlight the potential toxicity of the drug candidate. The present study was aimed at evaluating the in vivo uptake of 2-Amino-N-[2-(3,4-dihydroxy-phenyl)-ethyl]-3-phenyl-propionamide (DA-Phen) − a new dopaminergic neurotransmission modulator, in target and non-target organs of animal subjects and integrating these data with SMARTCyp results, an in silico method that predicts the sites of cytochrome P450-mediated metabolism of drug-like molecules. Wistar rats, subjected to two different behavioural studies in which DA-Phen was intraperitoneally administrated at a dose equal to 0.03mmol/kg, were sacrificed after the experimental protocols and their major organs were analysed to quantify the drug uptake. The data obtained were integrated with in silico prediction of potential metabolites of DA-Phen using the SmartCYP predictive tool. DA-Phen reached quantitatively the Central Nervous System and the results showed that the amide bond of the DA-Phen is scarcely hydrolysed as it was found intact in analyzed organs. As a consequence, it is possible to assume that DA-Phen acts as dopaminergic modulator per se and not as a Dopamine prodrug, thus avoiding peripheral release and toxic side effects due to the endogenous neurotransmitter. Furthermore the identification of potential metabolites related to biotransformation of the drug candidate leads to a more careful evaluation of the appropriate route of administration for future intended therapeutic aims and potential translation into clinical studies.
      Graphical abstract image

      PubDate: 2017-10-02T13:11:51Z
       
  • Prediction of new chromene-based inhibitors of tubulin using
           structure-based virtual screening and molecular dynamics simulation
           methods
    • Abstract: Publication date: Available online 27 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Hassan Aryapour, Maryam Dehdab, Farzin Sohraby, Afshar Bargahi
      Multidrug resistance (MDR) is one of the serious problems in cancer research that causes failure in chemotherapy. Chromene-based compounds have been proven to be the novel anti-MDR agents for inhibiting proliferation of tumor cells through tubulin polymerization inhibition of by binding at the colchicine binding site. In this study, we screened a chromene-based database of small molecules using physicochemical, ADMET properties and molecular docking to identify potential hit compounds. In order to validate our hit compounds, molecular dynamics simulations and related analysis were carried out and the results suggest that our hit compounds (PubChem CIDs: 16814409, 17594471, 57367244 and 69899719) can prove to be potential inhibitors of tubulin. The in silico results show that the present hits, like colchicine, effectively suppressed the dynamic instability of microtubules and induced microtubule-depolymerization and cell cycle arrest.
      Graphical abstract image

      PubDate: 2017-10-02T13:11:51Z
       
  • QSAR study of pyrazolo[4,3-e][1,2,4]triazine sulfonamides against
           tumor-associated human carbonic anhydrase isoforms IX and XII
    • Abstract: Publication date: Available online 21 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Joanna Matysiak, Alicja Skrzypek, Paweł Tarasiuk, Mariusz Mojzych
      The QSAR models for a set of pyrazolo[4,3-e][1,2,4]triazines incorporating benzenesulfonamide moiety combined directly with the heterocyclic ring or by NH linkage were generated. The inhibitory potency of compounds against human carbonic anhydrase isoforms IX and XII and antiproliferative activity against human MCF-7 cells were used as the dependent variables. The Codessa pro software was used for the descriptors calculation and the Best Multi-Linear Regression (BMLR) algorithm was employed to build the QSAR models. It was found that quantum descriptors are critical of the compounds activities. The selected models have good predictive accuracy confirmed by a set of the statistical quantities recommended by OECD.
      Graphical abstract image

      PubDate: 2017-09-25T19:33:45Z
       
  • In silico 3-D structure prediction and molecular docking studies of
           Inosine monophosphate dehydrogenase from Plasmodium falciparum
    • Abstract: Publication date: Available online 15 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Muslim Raza, Zahid Khan, Aftab Ahmad, Saleem Raza, Ajab Khan, Imdad Ullah Mohammadzai, Shah Zada
      Growing resistance in malarial parasites, particularly in Plasmodium falciparum needs a serious search for the discovery of novel drug targets. Inosine monophosphate dehydrogenase (IMPDH) is an important target for antimalarial drug discovery process in P. falciparum for the treatment of malaria. In the absence of x-ray crystal structure of this enzyme, homology modeling proved to be a reasonable alternate to study substrate binding mechanisms of this enzyme. In this study, a 3-D homology model for P. falciparum IMPDH was constructed taking human IMPDH (PDB code 1NF7) as template. Furthermore, an in-silico combinatorial library of ribavirin (RVP) derivatives (1347 molecules) was designed and virtually screened for ligands having selectively greater binding affinity with Plasmodium falciparum IMPDH relative to human IMPDH II. A total of five Ribavirin derivatives were identified as having greater binding affinity (-126 to −108 Kcal/mol and −9.4 to −8.6 Kcal/mol) with Plasmodium falciparum IMPDH. These five inhibitors should be used as selective and potent for Plasmodium falciparum IMPDH. Such type of study will provide information to synthetic medicinal chemist to enhance the potential of compounds (RVP derivatives) as chemotherapeutic agents to fight against the increasing burden of malarial infections.
      Graphical abstract image

      PubDate: 2017-09-19T19:23:52Z
       
  • Identification and functional prediction of stress responsive AP2/ERF
           transcription factors in Brassica napus by genome-wide analysis
    • Abstract: Publication date: Available online 14 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Hajar Owji, Ali Hajiebrahimi, Hassan Seradj, Shiva Hemmati
      Using homology and domain authentication, 321 putative AP2/ERF transcription factors were identified in Brassica napus, called BnAP2/ERF TFs. BnAP2/ERF TFs were classified into five major subfamilies, including DREB, ERF, AP2, RAV, and BnSoloist. This classification is based on phylogenetic analysis, motif identification, gene structure analysis, and physiochemical characterization. These TFs were annotated based on phylogenetic relationship with Brassica rapa. BnAP2/ERF TFs were located on 19 chromosomes of B. napus. Orthologs and paralogs were identified using synteny-based methods Ks calculation within B. napus genome and between B. napus with other species such as B. rapa, Brassica oleracea, and Arabidopsis thaliana indicated that BnAP2/ERF TFs were formed through duplication events occurred before B. napus formation. Kn/Ks values were between 0-1, suggesting the purifying selection among BnAP2/ERF TFs. Gene ontology annotation, cis-regulatory elements and functional interaction networks suggested that BnAP2/ERF TFs participate in response to stressors, including drought, high salinity, heat and cold as well as developmental processes particularly organ specification and embryogenesis. The identified cis-regulatory elements in the upstream of BnAP2/ERF TFs were responsive to abscisic acid. Analysis of the expression data derived from Illumina Hiseq 2000 RNA sequencing revealed that BnAP2/ERF genes were highly expressed in the roots comparing to flower buds, leaves, and stems. Also, the ERF subfamily was over-expressed under salt and fungal treatments. BnERF039 and BnERF245 are candidates for salt-tolerant B. napus. BnERF253-256 and BnERF260-277 are potential cytokinin response factors. BnERF227, BnERF228, BnERF234, BnERF134, BnERF132, BnERF176, and BnERF235 were suggested for resistance against Leptosphaeria maculan and Leptosphaeria biglobosa.
      Graphical abstract image

      PubDate: 2017-09-19T19:23:52Z
       
  • An investigation of novel traditional Chinese medicine formula for
           management of acute skin inflammation in silico
    • Abstract: Publication date: Available online 14 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Hsin-Chieh Tang, Hung-Jin Huang, Cheng-Chun Lee, Calvin Yu Chian Chen
      Matrix metalloproteinase-9 (MMP-9) appears to play an important role in acute skin inflammation. Subantimicrobial dose of tetracycline has been demonstrated to inhibit the activity of MMP-9 protein. However, long-term use tetracycline will induce side effect. The catalytic site of MMP-9 is located at zinc-binding amino acids, His401, His405 and His411. We attempted to search novel medicine formula as MMP-9 inhibitors from traditional Chinese medicine (TCM) database by using in silico studies. We utilized high-throughput virtual screening to find which natural compounds could bind to the zinc-binding site. The quantitative structure-activity relationship (QSAR) models, which constructed by scaffold of MMP-9 inhibitors and its activities, were employed to predict the bio-activity of the natural compounds for MMP-9. The results showed that Celacinnine, Lobelanidine and Celallocinnine were qualified to interact with zinc-binding site and displayed well predictive activity. We found that Celallocinnine was the best TCM compound for zinc binging sites of MMP-9 because the stable interactions were observed under dynamic condition. In addition, Celacinnine and Lobelanidine could interact with MMP-9 related protein that identified by drug-target interaction network analysis. Thus, we suggested the herbs Hypericum patulum, Sedum acre, and Tripterygium wilfordii that containing Celallocinnine, Celacinnine and Lobelanidine might be a novel medicine formula to avoid the side effect of tetracycline and increase the efficacy of treatment.
      Graphical abstract image

      PubDate: 2017-09-19T19:23:52Z
       
  • A physicochemical descriptor based method for effective and rapid
           screening of dual inhibitors against BACE-1 and GSK-3β as targets for
           Alzheimer’s disease.
    • Abstract: Publication date: Available online 8 September 2017
      Source:Computational Biology and Chemistry
      Author(s): Akhil Kumar, Gaurava Srivastava, Ashok Sharma
      Due to multifactorial nature of Alzheimer’s disease one target-one ligand hypothesis often looks insufficient. BACE-1 and GSK-3β are well established therapeutic drug targets and interaction between BACE-1 and GSK-3β pathways has also been established. Thus, designing of dual inhibitor for these two targets seems rational and may provide effective therapeutic strategies against AD. Recent studies revealed that only two scaffolds i.e. triazinone and curcumin act as a dual inhibitor against BACE-1 and GSK-3β. Thus, this discovery set the path to screen new chemical entities from a vast chemical space (∼1060 compounds) that inhibit both the targets. However, small part of the large chemical space will only show biological activity for specific targets. Virtual screening of large libraries is impractical and computational expensive especially in case of dual inhibitor design. In the case of dual or multi target inhibitor designing, we screened the database for each target that further increases time and resources. In this study we have done physicochemical descriptor based profiling to know the biological relevant chemical space for BACE-1 and GSK-3β inhibitors and proposed the suitable range of important physicochemical properties, occurrence of functional groups. We generated scaffolds tree of known inhibitors of BACE-1 and GSK-3β suggesting the common structure/fragment that can be used to design dual inhibitors. This approach can filter the potential dual inhibitor candidates of BACE-1 and GSK-3β from non inhibitors.
      Graphical abstract image

      PubDate: 2017-09-13T21:10:45Z
       
  • A ligand-based comparative molecular field analysis (CoMFA) and homology
           model based molecular docking studies on 3′, 4′-dihydroxyflavones as
           rat 5-lipoxygenase inhibitors: Design of new inhibitors
    • Abstract: Publication date: Available online 24 August 2017
      Source:Computational Biology and Chemistry
      Author(s): T.K.Shameera Ahamed, K. Muraleedharan
      In this study, ligand based comparative molecular field analysis (CoMFA) with five principal components was performed on class of 3′, 4′-dihydroxyflavone derivatives for potent rat 5-LOX inhibitors. The percentage contributions in building of CoMFA model were 91.36% for steric field and 8.6% for electrostatic field. R2 values for training and test sets were found to be 0.9320 and 0.8259, respectively. In case of LOO, LTO and LMO cross validation test, q2 values were 0.6587, 0.6479 and 0.5547, respectively. These results indicate that the model has high statistical reliability and good predictive power. The extracted contour maps were used to identify the important regions where the modification was necessary to design a new molecule with improved activity. The study has developed a homology model for rat 5-LOX and recognized the key residues at the binding site. Docking of most active molecule to the binding site of 5-LOX confirmed the stability and rationality of CoMFA model. Based on molecular docking results and CoMFA contour plots, new inhibitors with higher activity with respect to the most active compound in data set were designed.
      Graphical abstract image

      PubDate: 2017-09-01T20:47:19Z
       
  • Molecular Dynamics-Assisted Pharmacophore Modeling of Caspase-3-Isatin
           Sulfonamide Complex: Recognizing Essential Intermolecular Contacts and
           Features of Sulfonamide Inhibitor Class for Caspase-3 Binding
    • Abstract: Publication date: Available online 9 August 2017
      Source:Computational Biology and Chemistry
      Author(s): Sivakumar Prasanth Kumar, Chirag N. Patel, Prakash C. Jha, Himanshu A. Pandya
      The identification of isatin sulfonamide as a potent small molecule inhibitor for caspase-3 had fuelled the synthesis and characterization of the numerous sulfonamide class of inhibitors to optimize for potency. Earlier works that relied on the ligand-based approaches have successfully shown the regions of optimizations for sulfonamide scaffold. We present here molecular dynamics-based pharmacophore modeling of caspase-3-isatin sulfonamide crystal structure to elucidate the essential non-covalent contacts and its associated pharmacophore features necessary to ensure caspase-3 binding. We performed 20ns long dynamics of this crystal structure to extract global conformation states which were rigorously validated using an exclusive focussed library of experimental actives and inactives of sulfonamide origin by Receiver Operating Characteristic (ROC) curves. Eighteen structure-based pharmacophore hypotheses were identified which constituted both better sensitivity and specificity (>0.6) and showed that Cys163 (S1 sub-site; required for covalent and H bonding with Michael acceptor), Gly122 (S1;H bond with carbonyl oxygen), His121(S1; π stack with bicyclic isatin moiety) and Tyr204 (S2; π stack with phenyl group of the isatin sulfonamide molecule) were stringent binding entities for enabling caspase-3 optimal binding. The introduction of spatially prioritized pharmacophores and scrutinized non-covalent interactions obtained from dynamics-based pharmacophore models in a suitable virtual screening strategy will be helpful to screen and optimize molecules belonging to sulfonamide class of caspase-3 inhibitors.
      Graphical abstract image

      PubDate: 2017-08-11T02:21:23Z
       
 
 
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