Structure Based Pharmacophore Modeling, Virtual Screening
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Structure-Based Virtual Screening of Hypothetical Inhibitors of the Enzyme Longiborneol Synthase—A Potential Target to Reduce Fusarium Head Blight Disease
J Mol Model (2016) 22: 163 DOI 10.1007/s00894-016-3021-1 ORIGINAL PAPER Structure-based virtual screening of hypothetical inhibitors of the enzyme longiborneol synthase—a potential target to reduce Fusarium head blight disease E. Bresso1 & V. L ero ux 2 & M. Urban3 & K. E. Hammond-Kosack3 & B. Maigret2 & N. F. Martins1 Received: 17 December 2015 /Accepted: 27 May 2016 /Published online: 21 June 2016 # Springer-Verlag Berlin Heidelberg 2016 Abstract Fusarium head blight (FHB) is one of the most compounds from a library of 15,000 drug-like compounds. destructive diseases of wheat and other cereals worldwide. These putative inhibitors of longiborneol synthase provide a During infection, the Fusarium fungi produce mycotoxins that sound starting point for further studies involving molecular represent a high risk to human and animal health. Developing modeling coupled to biochemical experiments. This process small-molecule inhibitors to specifically reduce mycotoxin could eventually lead to the development of novel approaches levels would be highly beneficial since current treatments to reduce mycotoxin contamination in harvested grain. unspecifically target the Fusarium pathogen. Culmorin pos- sesses a well-known important synergistically virulence role Keywords Fusarium mycotoxins . Culmorin . Inhibitors . among mycotoxins, and longiborneol synthase appears to be a Homology modeling . Molecular dynamics . Ensemble key enzyme for its synthesis, thus making longiborneol syn- docking thase a particularly interesting target. This study aims to dis- cover potent and less toxic agrochemicals against FHB. These compounds would hamper culmorin synthesis by inhibiting Introduction longiborneol synthase. In order to select starting molecules for further investigation, we have conducted a structure- Fusarium head blight (FHB), caused by Fusarium based virtual screening investigation. -
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. -
Pharmacophore Modeling and Applications in Drug Discovery
REVIEWS Drug Discovery Today Volume 15, Numbers 11/12 June 2010 Reviews Pharmacophore modeling and INFORMATICS applications in drug discovery: challenges and recent advances Sheng-Yong Yang State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan 610041, China Pharmacophore approaches have become one of the major tools in drug discovery after the past century’s development. Various ligand-based and structure-based methods have been developed for improved pharmacophore modeling and have been successfully and extensively applied in virtual screening, de novo design and lead optimization. Despite these successes, pharmacophore approaches have not reached their expected full capacity, particularly in facing the demand for reducing the current expensive overall cost associated with drug discovery and development. Here, the challenges of pharmacophore modeling and applications in drug discovery are discussed and recent advances and latest developments are described, which provide useful clues to the further development and application of pharmacophore approaches. Introduction lead optimization and multitarget drug design (Fig. 1). A variety of The concept of pharmacophore was first introduced in 1909 by automated tools for pharmacophore modeling and applications Ehrlich [1], who defined the pharmacophore as ‘a molecular appeared constantly after the advances in computational chem- framework that carries (phoros) the essential features responsible istry in the past 20 years; these pharmacophore modeling tools, for a drug’s (pharmacon) biological activity’. After a century’s together with their inventor(s) and typical characteristics, are development, the basic pharmacophore concept still remains summarized in Supplementary Table S1. Many successful stories unchanged, but its intentional meaning and application range of pharmacophore approaches in facilitating drug discovery have have been expanded considerably. -
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). -
Computer-Aided Drug Design: a Practical Guide
Computer-Aided Drug Design: A Practical Guide Forrest Smith, Ph.D. Department of Drug Discovery and Development Harrison School of Pharmacy Auburn, University History of Drug Design • Natural Products-Ebers Papyrus, 1500 B.C. documents over 700 plant based products used to treat a variety of illnesses • The rise of Organic Chemistry, middle or the 20th Century, semi-synthetic and synthetic drugs • Computers emerge in the late 1980s, CADD with minimal impact • Automation in the 1990s, High Through-put Screening and Robotics, Compound Libraries • CADD has continued to advance since its introduction with improving capabilities Computational Chemistry • Ab initio calculations • Semi-empirical calculations • Molecular Mechanics Molecular Mechanics Global Minimum Force Fields • MM2, MM3, MM4 • MMFF • AMBER • CHARM • OPLS Finding the Global Minimum • Systematic Search • Monte Carlo Methods • Simulated Annealing • Quenched Dynamics Minor Groove Binders Computer-Aided Drug Design • Structure Based Design – Docking – Molecular Dynamics – Free Energy Perturbation • Ligand Based Design-QSAR – COMFA – Pharmacophore Modeling – Shape Based Methods Docking-The Receptor • X-Ray Crystal Structures – RCSB Protein Data Bank (https://www.rcsb.org/) – Private Data • Homology Modeling • Nuclear Magnetic Resonance Docking- The Ligand • Proprietary Ligands • Databases qReal Databases • FDA approved drugs- (http://chemoinfo.ipmc.cnrs.fr/MOLDB/index.html) • Purchasable compounds Zinc 15, currently 100 million compounds (http://zinc15.docking.org/) q Virtual Databases- -
Ligand Based 3D-QSAR Pharmacophore, Molecular Docking and ADME to Identify Potential Broblast Growth Factor Receptor 1 Inhibitor
Ligand based 3D-QSAR pharmacophore, molecular docking and ADME to identify potential broblast growth factor receptor 1 inhibitors Zizhong Tang Sichuan Agricultural University Lu Huang Sichuan Agricultural University Xiaoli Fu Sichuan Agricultural University Haoxiang Wang Sichuan Agricultural University Biao Tang Sichuan Agricultural University Yirong Xiao Sichuan Agricultural University Hospital Caixia Zhou Sichuan Agricultural University Zhiqiao Zhao Sichuan Agricultural University Yujun Wan Sichuan food feimentation industry research and design institute Hui Chen ( [email protected] ) Sichuan Agricultural University Huipeng Yao Sichuan Agricultural University Zhi Shan Sichuan Agricultural University Tongliang Bu Sichuan Agricultural University Xulong Wu Chengdu Agricultural College Research Page 1/27 Keywords: FGFR1, Inhibitor, molecular docking, pharmacophore, ADME Posted Date: September 1st, 2020 DOI: https://doi.org/10.21203/rs.3.rs-64361/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 2/27 Abstract Background The FGF/FGFR system may affect tumor cells and stromal microenvironment through autocrine and paracrine stimulation, thereby signicantly promoting oncogene transformation and tumor growth. Abnormal expression of FGFR1 in cells is considered to be the main cause of tumorigenesis and a potential target for the treatment of cancer. Methods The known inhibitors were collected to construct 3D-QSAR pharmacophore model, which was veried by cost analysis, test set validation and Fischer test. Virtual screening of zinc database based on pharmacophore was carried out. FGFR1 crystal complex was downloaded from the protein database to dock with the compound. Finally, the absorption, distribution, metabolism and excretion (ADME) characteristics and toxicity of a series of potential inhibitors were studied. -
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. -
Natural Products As Leads to Potential Drugs: an Old Process Or the New Hope for Drug Discovery?
J. Med. Chem. 2008, 51, 2589–2599 2589 Natural Products as Leads to Potential Drugs: An Old Process or the New Hope for Drug Discovery? David J. Newman† Natural Products Branch, DeVelopmental Therapeutics Program, DCTD, National Cancer InstitutesFrederick, P.O. Box B, Frederick, Maryland 21702 ReceiVed April 5, 2007 I. Introduction From approximately the early 1980s, the “influence of natural products” upon drug discovery in all therapeutic areas apparently has been on the wane because of the advent of combinatorial chemistry technology and the “associated expectation” that these techniques would be the future source of massive numbers of novel skeletons and drug leads/new chemical entities (NCEa) where the intellectual property aspects would be very simple. As a result, natural product work in the pharmaceutical industry, except for less than a handful of large pharmaceutical compa- nies, effectively ceased from the end of the 1980s. Figure 1. Source of small molecule drugs, 1981–2006: major What has now transpired (cf. evidence shown in Newman categories, N ) 983 (in percentages). Codes are as in ref 1. Major and Cragg, 20071 and Figures 1 and 2 below showing the categories are as follows: “N”, natural product; “ND”, derived from a natural product and usually a semisynthetic modification; “S”, totally continued influence of natural products as leads to or sources synthetic drug often found by random screening/modification of an of drugs over the past 26 years (1981–2006)) is that, to date, existing agent; “S*”, made by total synthesis, but the pharmacophore there has only been one de novo combinatorial NCE approved is/was from a natural product. -
Downloading Only Compounds with the Properties “Drug-Like”, “Purchasable”
bioRxiv preprint doi: https://doi.org/10.1101/2021.03.02.433618; this version posted June 9, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. A Pharmacophore Model for SARS-CoV-2 3CLpro Small Molecule Inhibitors and in Vitro Experimental Validation of Computationally Screened Inhibitors Enrico Glaab*†1, Ganesh Babu Manoharan2, Daniel Abankwa*2 1Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg 2Department of Life Sciences and Medicine, University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg *EG and DA are joint senior autHors †Contact: [email protected] Abstract Among the biomedical efforts in response to the current coronavirus (COVID-19) pandemic, pharmacological strategies to reduce viral load in patients with severe forms of the disease are being studied intensively. One of the main drug target proteins proposed so far is the SARS- CoV-2 viral protease 3CLpro (also called Mpro), an essential component for viral replication. Ongoing ligand- and receptor-based computational screening efforts would be facilitated by an improved understanding of the electrostatic, hydrophobic and steric features that characterize small molecule inhibitors binding stably to 3CLpro, as well as by an extended collection of known binders. Here, we present combined virtual screening, molecular dynamics simulation, machine learning and in vitro experimental validation analyses which have led to the identification of small molecule inhibitors of 3CLpro with micromolar activity, and to a pharmacophore model that describes functional chemical groups associated with the molecular recognition of ligands by the 3CLpro binding pocket.