Structure-Based Virtual Screening of Hypothetical Inhibitors of the Enzyme Longiborneol Synthase—A Potential Target to Reduce Fusarium Head Blight Disease
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Virtual Screening of the Inhibitors Targeting at the Viral Protein 40 of Ebola Virus V
Karthick et al. Infectious Diseases of Poverty (2016) 5:12 DOI 10.1186/s40249-016-0105-1 RESEARCH ARTICLE Open Access Virtual screening of the inhibitors targeting at the viral protein 40 of Ebola virus V. Karthick1, N. Nagasundaram1, C. George Priya Doss1,2, Chiranjib Chakraborty1,3, R. Siva4, Aiping Lu1, Ge Zhang1 and Hailong Zhu1* Abstract Background: The Ebola virus is highly pathogenic and destructive to humans and other primates. The Ebola virus encodes viral protein 40 (VP40), which is highly expressed and regulates the assembly and release of viral particles in the host cell. Because VP40 plays a prominent role in the life cycle of the Ebola virus, it is considered as a key target for antiviral treatment. However, there is currently no FDA-approved drug for treating Ebola virus infection, resulting in an urgent need to develop effective antiviral inhibitors that display good safety profiles in a short duration. Methods: This study aimed to screen the effective lead candidate against Ebola infection. First, the lead molecules were filtered based on the docking score. Second, Lipinski rule of five and the other drug likeliness properties are predicted to assess the safety profile of the lead candidates. Finally, molecular dynamics simulations was performed to validate the lead compound. Results: Our results revealed that emodin-8-beta-D-glucoside from the Traditional Chinese Medicine Database (TCMD) represents an active lead candidate that targets the Ebola virus by inhibiting the activity of VP40, and displays good pharmacokinetic properties. Conclusion: This report will considerably assist in the development of the competitive and robust antiviral agents against Ebola infection. -
Characterization of a Cytochrome P450 Monooxygenase Gene Involved in the Biosynthesis of Geosmin in Penicillium Expansum
Open Archive TOULOUSE Archive Ouverte (OATAO) OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 10812 To link to this article : DOI : 10.5897/AJMR11.1361 URL : http://academicjournals.org/journal/AJMR/article- abstract/841ECBA21771 To cite this version : Siddique, Muhammad Hussnain and Liboz, Thierry and Bacha, Nafees and Puel, Olivier and Mathieu, Florence and Lebrihi, Ahmed Characterization of a cytochrome P450 monooxygenase gene involved in the biosynthesis of geosmin in Penicillium expansum. (2012) African Journal of Microbiology Research, vol. 6 (n° 19). pp. 4122-4127. ISSN 1996-0808 Any correspondance concerning this service should be sent to the repository administrator: [email protected] Characterization of a cytochrome P450 monooxygenase gene involved in the biosynthesis of geosmin in Penicillium expansum Muhammad Hussnain Siddique1,2, Thierry Liboz1,2, Nafees Bacha3, Olivier Puel4, Florence Mathieu1,2 and Ahmed Lebrihi1,2,5* 1Université de Toulouse, INPT-UPS, Laboratoire de Génie Chimique, avenue de l’Agrobiopole, 31326 Castanet-Tolosan Cedex, France. 2Le Centre national de la recherche scientifique (CNRS), Laboratoire de Génie Chimique, 31030 Toulouse, France. 3Centre of Biotechnology and Microbiology, University of Peshawar, Pakistan. 4Institut National de la Recherche Agronomique (INRA), Laboratoire de Pharmacologie Toxicologie, 31931 Toulouse, France. 5Université Moulay Ismail, Marjane 2, BP 298, Meknes, Morocco. Geosmin is a terpenoid, an earthy-smelling substance associated with off-flavors in water and wine. The biosynthesis of geosmin is well characterized in bacteria, but little is known about its production in eukaryotes, especially in filamentous fungi. -
Report on an NIH Workshop on Ultralarge Chemistry Databases Wendy A
1 Report on an NIH Workshop on Ultralarge Chemistry Databases Wendy A. Warr Wendy Warr & Associates, 6 Berwick Court, Holmes Chapel, Cheshire, CW4 7HZ, United Kingdom. Email: [email protected] Introduction The virtual workshop took place on December 1-3, 2020. It was aimed at researchers, groups, and companies that generate, manage, sell, search, and screen databases of more than one billion small molecules (Figure 1). There were about 550 “attendees” from 37 different countries. Recent advances in computational chemistry have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures, carrying out similarity searches, studying structure-activity relationships (SAR), experimenting with scaffold-hopping, and using other drug discovery methodologies.1 For clarity, one could differentiate “spaces” from “libraries”, and “libraries” from “databases”. Spaces are combinatorially constructed collections of compounds; they are usually very big indeed and it is not possible to enumerate all the precise chemical structures that are covered. Libraries are enumerated collections of full structures: usually fewer than 1010 molecules. Databases are a way to storing libraries, for example, in a relational database management system. Figure 1. Ultralarge chemical databases. (Source: Marcus Gastreich based on the publication by Hoffmann and Gastreich.) This report summarizes talks from about 30 practitioners in the field of ultralarge collections of molecules. The aim is to represent as accurately as possible the information that was delivered by the speakers; the report does not seek to be evaluative. 2 Welcoming remarks; defining a drug discovery gateway Susan Gregurick, Office of Data Science and Strategy, NIH, USA Data should be “findable, accessible, interoperable and reusable” (FAIR)2 and with this in mind, NIH has been creating, curating, integrating, and querying ultralarge chemistry databases. -
Qsar Methods Development, Virtual and Experimental Screening for Cannabinoid Ligand Discovery
QSAR METHODS DEVELOPMENT, VIRTUAL AND EXPERIMENTAL SCREENING FOR CANNABINOID LIGAND DISCOVERY by Kyaw Zeyar Myint BS, Biology, BS, Computer Science, Hampden-Sydney College, 2007 Submitted to the Graduate Faculty of School of Medicine in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2012 UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE This dissertation was presented by Kyaw Zeyar Myint It was defended on August 20th, 2012 and approved by Dr. Ivet Bahar, Professor, Department of Computational and Systems Biology Dr. Billy W. Day, Professor, Department of Pharmaceutical Sciences Dr. Christopher Langmead, Associate Professor, Department of Computer Science, CMU Dissertation Advisor: Dr. Xiang-Qun Xie, Professor, Department of Pharmaceutical Sciences ii Copyright © by Kyaw Zeyar Myint 2012 iii QSAR METHODS DEVELOPMENT, VIRTUAL AND EXPERIMENTAL SCREENING FOR CANNABINOID LIGAND DISCOVERY Kyaw Zeyar Myint, PhD University of Pittsburgh, 2012 G protein coupled receptors (GPCRs) are the largest receptor family in mammalian genomes and are known to regulate wide variety of signals such as ions, hormones and neurotransmitters. It has been estimated that GPCRs represent more than 30% of current drug targets and have attracted many pharmaceutical industries as well as academic groups for potential drug discovery. Cannabinoid (CB) receptors, members of GPCR superfamily, are also involved in the activation of multiple intracellular signal transductions and their endogenous ligands or cannabinoids have attracted pharmacological research because of their potential therapeutic effects. In particular, the cannabinoid subtype-2 (CB2) receptor is known to be involved in immune system signal transductions and its ligands have the potential to be developed as drugs to treat many immune system disorders without potential psychotic side- effects. -
Fast Three Dimensional Pharmacophore Virtual Screening of New Potent Non-Steroid Aromatase Inhibitors
View metadata, citation and similar papers at core.ac.uk brought to you by CORE J. Med. Chem. 2009, 52, 143–150 143 provided by Estudo Geral Fast Three Dimensional Pharmacophore Virtual Screening of New Potent Non-Steroid Aromatase Inhibitors Marco A. C. Neves,† Teresa C. P. Dinis,‡ Giorgio Colombo,*,§ and M. Luisa Sa´ e Melo*,† Centro de Estudos Farmaceˆuticos, Laborato´rio de Quı´mica Farmaceˆutica, Faculdade de Farma´cia, UniVersidade de Coimbra, 3000-295, Coimbra, Portugal, Centro de Neurocieˆncias, Laborato´rio de Bioquı´mica, Faculdade de Farma´cia, UniVersidade de Coimbra, 3000-295, Coimbra, Portugal, and Istituto di Chimica del Riconoscimento Molecolare, CNR, 20131, Milano, Italy ReceiVed July 28, 2008 Suppression of estrogen biosynthesis by aromatase inhibition is an effective approach for the treatment of hormone sensitive breast cancer. Third generation non-steroid aromatase inhibitors have shown important benefits in recent clinical trials with postmenopausal women. In this study we have developed a new ligand- based strategy combining important pharmacophoric and structural features according to the postulated aromatase binding mode, useful for the virtual screening of new potent non-steroid inhibitors. A small subset of promising drug candidates was identified from the large NCI database, and their antiaromatase activity was assessed on an in vitro biochemical assay with aromatase extracted from human term placenta. New potent aromatase inhibitors were discovered to be active in the low nanomolar range, and a common binding mode was proposed. These results confirm the potential of our methodology for a fast in silico high-throughput screening of potent non-steroid aromatase inhibitors. Introduction built and proved to be valuable in understanding the binding 14-16 Aromatase, a member of the cytochrome P450 superfamily determinants of several classes of inhibitors. -
Bigger Is Better in Virtual Drug Screens
NEWS & VIEWS RESEARCH might no longer be elusive, but the nitrogen Doney, S. C. Biogeosciences 11, 691–708 (2014). cycle remains a treasure trove for discovery. ■ 6. Knapp, A. N., Casciotti, K. L., Berelson, W. M., Prokopenko, M. G. & Capone, D. G. Proc. Natl Acad. Sci. USA 113, 4398–4403 (2016). Nicolas Gruber is at the Institute of 7. Bonnet, S., Caffin, M., Berthelot, H. & Moutin, T. Biogeochemistry and Pollutant Dynamics, Proc. Natl Acad. Sci. USA 114, E2800–E2801 (2017). ETH Zurich, 8092 Zurich, Switzerland. 8. Wang, W.-L., Moore, J. K., Martiny, A. C. & e-mail: [email protected] Primeau, F. W. Nature 566, 205–211 (2019). 9. DeVries, T. & Primeau, F. J. Phys. Oceanogr. 41, 1. Redfield, A. C. in James Johnstone Memorial Volume 2381–2401 (2011). (ed. Daniel, R. J.) 176–192 (Univ. College Liverpool, 10. Gruber, N. in Nitrogen in the Marine Environment 50 Years Ago 1934). 2nd edn (eds Capone, D. G., Bronk, D. A., 2. Carpenter, E. J. & Capone, D. G. in Nitrogen in the Mulholland, M. R. & Carpenter, E. J.) 1–150 Test tube babies may not be just Marine Environment 2nd edn (eds Capone, D. G., (Elsevier, 2008). Bronk, D. A., Mulholland, M. R. & Carpenter, E. J.) 11. Yang, S., Gruber, N., Long, M. C. & Vogt, M. Glob. round the corner, but the day 141–198 (Elsevier, 2008). Biogeochem. Cycles 31, 1470–1487 (2017). when all the knowledge necessary 3. Gruber, N. Proc. Natl Acad. Sci. USA 113, 12. Kim, I.-N. et al. Science 346, 1102–1106 (2014). to produce them will be available 4246–4248 (2016). -
Searching for Novel Targets to Control Wheat Head Blight Disease—I-Protein Identification, 3D Modeling and Virtual Screening
Advances in Microbiology, 2016, 6, 811-830 http://www.scirp.org/journal/aim ISSN Online: 2165-3410 ISSN Print: 2165-3402 Searching for Novel Targets to Control Wheat Head Blight Disease—I-Protein Identification, 3D Modeling and Virtual Screening Natália F. Martins1, Emmanuel Bresso1, Roberto C. Togawa1, Martin Urban2, John Antoniw2, Bernard Maigret3, Kim Hammond-Kosack2 1EMBRAPA Recursos Genéticos e Biotecnologia Parque Estação Biológica, Brasília, Brazil 2Department of Plant Biology and Crop Science, Rothamsted Research, Harpenden, UK 3CNRS, LORIA, UMR 7503, Lorraine University, Nancy, France How to cite this paper: Martins, N.F., Abstract Bresso, E., Togawa, R.C., Urban, M., Anto- niw, J., Maigret, B. and Hammond-Kosack, Fusarium head blight (FHB) is a destructive disease of wheat and other cereals. FHB K. (2016) Searching for Novel Targets to occurs in Europe, North America and around the world causing significant losses in Control Wheat Head Blight Disease— production and endangers human and animal health. In this article, we provide the I-Protein Identification, 3D Modeling and strategic steps for the specific target selection for the phytopathogen system wheat- Virtual Screening. Advances in Microbiol- ogy, 6, 811-830. Fusarium graminearum. The economic impact of FHB leads to the need for innova- http://dx.doi.org/10.4236/aim.2016.611079 tion. Currently used fungicides have been shown to be effective over the years, but recently cereal infecting Fusaria have developed resistance. Our work presents a new Received: June 21, 2016 perspective on target selection to allow the development of new fungicides. We de- Accepted: September 11, 2016 veloped an innovative approach combining both genomic analysis and molecular Published: September 14, 2016 modeling to increase the discovery for new chemical compounds with both safety Copyright © 2016 by authors and and low environmental impact. -
Autodock Vina Manual
4/21/2021 AutoDock Vina - molecular docking and virtual screening program Home Features Download Tutorial FAQ Manual Questions? AutoDock Vina Manual Contents Features License Tutorial Frequently Asked Questions Platform Notes and Installation Windows Linux Mac Building from Source Other Software Usage Summary Configuration File Search Space Exhaustiveness Output Advanced Options Virtual Screening History Citation Getting Help Reporting Bugs Features Accuracy AutoDock Vina significantly improves the average accuracy of the binding mode predictions compared to AutoDock 4, judging by our tests on the training set used in AutoDock 4 development.[*] Additionally and independently, AutoDock Vina has been tested against a virtual screening benchmark called the Directory of Useful Decoys by the Watowich group, and was found to be "a strong competitor against the other programs, and at the top of the pack in many cases". It should be noted that all six of the other docking programs, to which it was compared, are distributed commercially. AutoDock Tools Compatibility For its input and output, Vina uses the same PDBQT molecular structure file Binding mode prediction accuracy on the test set. format used by AutoDock. PDBQT files can be generated (interactively or in "AutoDock" refers to AutoDock 4, and "Vina" to AutoDock batch mode) and viewed using MGLTools. Other files, such as the AutoDock Vina 1. and AutoGrid parameter files (GPF, DPF) and grid map files are not needed. Ease of Use Vina's design philosophy is not to require the user to understand its implementation details, tweak obscure search parameters, cluster results or know advanced algebra (quaternions). All that is required is the structures of the molecules being docked and the specification of the search space including the binding site. -
Generated by SRI International Pathway Tools Version 25.0, Authors S
Authors: Pallavi Subhraveti Ron Caspi Quang Ong Peter D Karp An online version of this diagram is available at BioCyc.org. Biosynthetic pathways are positioned in the left of the cytoplasm, degradative pathways on the right, and reactions not assigned to any pathway are in the far right of the cytoplasm. Transporters and membrane proteins are shown on the membrane. Ingrid Keseler Periplasmic (where appropriate) and extracellular reactions and proteins may also be shown. Pathways are colored according to their cellular function. Gcf_000725805Cyc: Streptomyces xanthophaeus Cellular Overview Connections between pathways are omitted for legibility. -
Production of Plant-Associated Volatiles by Select Model and Industrially Important Streptomyces Spp
microorganisms Article Production of Plant-Associated Volatiles by Select Model and Industrially Important Streptomyces spp. 1, 2, 3 1 Zhenlong Cheng y, Sean McCann y, Nicoletta Faraone , Jody-Ann Clarke , E. Abbie Hudson 2, Kevin Cloonan 2, N. Kirk Hillier 2,* and Kapil Tahlan 1,* 1 Department of Biology, Memorial University of Newfoundland, St. John’s, NL A1B 3X9, Canada; [email protected] (Z.C.); [email protected] (J.-A.C.) 2 Department of Biology, Acadia University, Wolfville, NS B4P 2R6, Canada; [email protected] (S.M.); [email protected] (E.A.H.); [email protected] (K.C.) 3 Department of Chemistry, Acadia University, Wolfville, NS B4P 2R6, Canada; [email protected] * Correspondence: [email protected] (N.K.H.); [email protected] (K.T.) These authors contributed equally. y Received: 13 October 2020; Accepted: 9 November 2020; Published: 11 November 2020 Abstract: The Streptomyces produce a great diversity of specialized metabolites, including highly volatile compounds with potential biological activities. Volatile organic compounds (VOCs) produced by nine Streptomyces spp., some of which are of industrial importance, were collected and identified using gas chromatography–mass spectrometry (GC-MS). Biosynthetic gene clusters (BGCs) present in the genomes of the respective Streptomyces spp. were also predicted to match them with the VOCs detected. Overall, 33 specific VOCs were identified, of which the production of 16 has not been previously reported in the Streptomyces. Among chemical classes, the most abundant VOCs were terpenes, which is consistent with predicted biosynthetic capabilities. In addition, 27 of the identified VOCs were plant-associated, demonstrating that some Streptomyces spp. -
Supporting Information High-Throughput Virtual Screening
Supporting Information High-Throughput Virtual Screening of Proteins using GRID Molecular Interaction Fields Simone Sciabola, Robert V. Stanton, James E. Mills, Maria M. Flocco, Massimo Baroni, Gabriele Cruciani, Francesca Perruccio and Jonathan S. Mason Contents Table S1 S2-S21 Figure S1 S22 * To whom correspondence should be addressed: Simone Sciabola, Pfizer Research Technology Center, Cambridge, 02139 MA, USA Phone: +1-617-551-3327; Fax: +1-617-551-3117; E-mail: [email protected] S1 Table S1. Description of the 990 proteins used as decoy for the Protein Virtual Screening analysis. PDB ID Protein family Molecule Res. (Å) 1n24 ISOMERASE (+)-BORNYL DIPHOSPHATE SYNTHASE 2.3 1g4h HYDROLASE 1,3,4,6-TETRACHLORO-1,4-CYCLOHEXADIENE HYDROLASE 1.8 1cel HYDROLASE(O-GLYCOSYL) 1,4-BETA-D-GLUCAN CELLOBIOHYDROLASE I 1.8 1vyf TRANSPORT PROTEIN 14 KDA FATTY ACID BINDING PROTEIN 1.85 1o9f PROTEIN-BINDING 14-3-3-LIKE PROTEIN C 2.7 1t1s OXIDOREDUCTASE 1-DEOXY-D-XYLULOSE 5-PHOSPHATE REDUCTOISOMERASE 2.4 1t1r OXIDOREDUCTASE 1-DEOXY-D-XYLULOSE 5-PHOSPHATE REDUCTOISOMERASE 2.3 1q0q OXIDOREDUCTASE 1-DEOXY-D-XYLULOSE 5-PHOSPHATE REDUCTOISOMERASE 1.9 1jcy LYASE 2-DEHYDRO-3-DEOXYPHOSPHOOCTONATE ALDOLASE 1.9 1fww LYASE 2-DEHYDRO-3-DEOXYPHOSPHOOCTONATE ALDOLASE 1.85 1uk7 HYDROLASE 2-HYDROXY-6-OXO-7-METHYLOCTA-2,4-DIENOATE 1.7 1v11 OXIDOREDUCTASE 2-OXOISOVALERATE DEHYDROGENASE ALPHA SUBUNIT 1.95 1x7w OXIDOREDUCTASE 2-OXOISOVALERATE DEHYDROGENASE ALPHA SUBUNIT 1.73 1d0l TRANSFERASE 35KD SOLUBLE LYTIC TRANSGLYCOSYLASE 1.97 2bt4 LYASE 3-DEHYDROQUINATE DEHYDRATASE -
Discovery of Cryptic Compounds from Streptomyces Lavendulae FRI-5 Using an Engineered Microbial Host (異種発現系の活用による放線菌休眠化合物の覚醒と同定)
Discovery of cryptic compounds from Streptomyces Title lavendulae FRI-5 using an engineered microbial host Author(s) Pait, Ivy Grace Citation Issue Date Text Version ETD URL https://doi.org/10.18910/69537 DOI 10.18910/69537 rights Note Osaka University Knowledge Archive : OUKA https://ir.library.osaka-u.ac.jp/ Osaka University Doctoral Dissertation Discovery of cryptic compounds from Streptomyces lavendulae FRI-5 using an engineered microbial host (異種発現系の活用による放線菌休眠化合物の覚醒と同定) Ivy Grace Umadhay Pait January 2018 Division of Advanced Science and Biotechnology Graduate School of Engineering, Osaka University Contents Chapter 1 General Introduction 1.1 The role of microbial natural products in drug discovery 1 1.2 Bioactivities and biosynthesis of polyketides and nonribosomal peptides 3 1.2.1 The assembly-line enzymology of polyketides 3 1.2.2 The biosynthetic logic of nonribosomal peptide compounds 7 1.3 Streptomyces, the proven resource for therapeutics 9 1.3.1 Life cycle of Streptomyces 10 1.3.2 Regulation of secondary metabolite production in Streptomyces 11 1.4 Genome mining and reviving interest in microbial secondary metabolites 14 1.4.1 Decline in natural product research 14 1.4.2 Bacterial genome mining for new natural products 16 1.4.3 Streptomyces genome as the richest bacterial resource for cryptic BGCs 17 1.4.4 Approaches for triggering the production of cryptic metabolites 19 1.4.4.1 Altering chemical and physical conditions 19 1.4.4.2 Genetic modification/Molecular approaches 21 1.5 Heterologous expression in a genome-minimized