In Silico Screening and Molecular Docking of Bioactive Agents Towards Human Coronavirus Receptor
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Pyplif HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors
molecules Article PyPLIF HIPPOS-Assisted Prediction of Molecular Determinants of Ligand Binding to Receptors Enade P. Istyastono 1,* , Nunung Yuniarti 2, Vivitri D. Prasasty 3 and Sudi Mungkasi 4 1 Faculty of Pharmacy, Sanata Dharma University, Yogyakarta 55282, Indonesia 2 Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia; [email protected] 3 Faculty of Biotechnology, Atma Jaya Catholic University of Indonesia, Jakarta 12930, Indonesia; [email protected] 4 Department of Mathematics, Faculty of Science and Technology, Sanata Dharma University, Yogyakarta 55282, Indonesia; [email protected] * Correspondence: [email protected]; Tel.: +62-274883037 Abstract: Identification of molecular determinants of receptor-ligand binding could significantly increase the quality of structure-based virtual screening protocols. In turn, drug design process, especially the fragment-based approaches, could benefit from the knowledge. Retrospective virtual screening campaigns by employing AutoDock Vina followed by protein-ligand interaction finger- printing (PLIF) identification by using recently published PyPLIF HIPPOS were the main techniques used here. The ligands and decoys datasets from the enhanced version of the database of useful de- coys (DUDE) targeting human G protein-coupled receptors (GPCRs) were employed in this research since the mutation data are available and could be used to retrospectively verify the prediction. The results show that the method presented in this article could pinpoint some retrospectively verified molecular determinants. The method is therefore suggested to be employed as a routine in drug Citation: Istyastono, E.P.; Yuniarti, design and discovery. N.; Prasasty, V.D.; Mungkasi, S. PyPLIF HIPPOS-Assisted Prediction Keywords: PyPLIF HIPPOS; AutoDock Vina; drug discovery; fragment-based; molecular determi- of Molecular Determinants of Ligand Binding to Receptors. -
Open Babel Documentation Release 2.3.1
Open Babel Documentation Release 2.3.1 Geoffrey R Hutchison Chris Morley Craig James Chris Swain Hans De Winter Tim Vandermeersch Noel M O’Boyle (Ed.) December 05, 2011 Contents 1 Introduction 3 1.1 Goals of the Open Babel project ..................................... 3 1.2 Frequently Asked Questions ....................................... 4 1.3 Thanks .................................................. 7 2 Install Open Babel 9 2.1 Install a binary package ......................................... 9 2.2 Compiling Open Babel .......................................... 9 3 obabel and babel - Convert, Filter and Manipulate Chemical Data 17 3.1 Synopsis ................................................. 17 3.2 Options .................................................. 17 3.3 Examples ................................................. 19 3.4 Differences between babel and obabel .................................. 21 3.5 Format Options .............................................. 22 3.6 Append property values to the title .................................... 22 3.7 Filtering molecules from a multimolecule file .............................. 22 3.8 Substructure and similarity searching .................................. 25 3.9 Sorting molecules ............................................ 25 3.10 Remove duplicate molecules ....................................... 25 3.11 Aliases for chemical groups ....................................... 26 4 The Open Babel GUI 29 4.1 Basic operation .............................................. 29 4.2 Options ................................................. -
JRC QSAR Model Database
JRC QSAR Model Database EURL ECVAM DataBase service on ALternative Methods to animal experimentation To promote the development and uptake of alternative and advanced methods in toxicology and biomedical sciences SDF - STRUCTURE DATA FORMAT: How to create from SMILES The European Commission’s science and knowledge service Joint Research Centre Directorate F Health, Consumers & Reference Materials Chemicals Safety & Alternative Methods Unit The European Commission’s science and knowledge service Joint Research Centre EUR 28708 EN This publication is a Tutorial by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide user support. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication. Contact information Email: [email protected] JRC Science Hub https://ec.europa.eu/jrc JRC107492 EUR 28708 EN PDF ISBN 978-92-79-71294-4 ISSN 1831-9424 doi:10.2760/952280 Print ISBN 978-92-79-71295-1 ISSN 1018-5593 doi:10.2760/668595 Luxembourg: Publications Office of the European Union, 2017 Ispra: European Commission, 2017 © European Union, 2017 The reuse of the document is authorised, provided the source is acknowledged and the original meaning or message of the texts are not distorted. The European Commission shall not be held liable for any consequences stemming from the reuse. How to cite this document: Triebe -
Evaluation of Protein-Ligand Docking Methods on Peptide-Ligand
bioRxiv preprint doi: https://doi.org/10.1101/212514; this version posted November 1, 2017. 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. Evaluation of protein-ligand docking methods on peptide-ligand complexes for docking small ligands to peptides Sandeep Singh1#, Hemant Kumar Srivastava1#, Gaurav Kishor1#, Harinder Singh1, Piyush Agrawal1 and G.P.S. Raghava1,2* 1CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh, India. 2Indraprastha Institute of Information Technology, Okhla Phase III, Delhi India #Authors Contributed Equally Emails of Authors: SS: [email protected] HKS: [email protected] GK: [email protected] HS: [email protected] PA: [email protected] * Corresponding author Professor of Center for Computation Biology, Indraprastha Institute of Information Technology (IIIT Delhi), Okhla Phase III, New Delhi-110020, India Phone: +91-172-26907444 Fax: +91-172-26907410 E-mail: [email protected] Running Title: Benchmarking of docking methods 1 bioRxiv preprint doi: https://doi.org/10.1101/212514; this version posted November 1, 2017. 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. ABSTRACT In the past, many benchmarking studies have been performed on protein-protein and protein-ligand docking however there is no study on peptide-ligand docking. -
BIOVIA DISCOVERY STUDIO® 2016 COMPREHENSIVE MODELING and SIMULATIONS for LIFE SCIENCES Datasheet
BIOVIA DISCOVERY STUDIO® 2016 COMPREHENSIVE MODELING AND SIMULATIONS FOR LIFE SCIENCES Datasheet ACCURATELY Drug discovery is a multi-objective optimization. Scientists have to optimize both biochemical potency and characteristics such as ADME and toxicity. The latest PREDICT LIGAND release of BIOVIA’s predictive science application, Discovery Studio®, continues the BINDING evolution of new science in its market-leading CHARMm molecular simulations engine. Built on BIOVIA Foundation™, Discovery Studio® is uniquely positioned as ENERGIES the most comprehensive, collaborative modeling and simulation application for Life Sciences discovery research. DISCOVERY STUDIO 2016 Part of the 2016 BIOVIA product release series, Discovery Studio 2016 continues to deliver key new CHARMm-based molecular simulations. NEW AND ENHANCED SCIENCE • New! Steered Molecular Dynamics: Developed and validated in academia by members of the CHARMM Developer community2,3, the CHARMM AFM (Atomic Force Microscopy) function has been included in the latest release of Discovery Studio CHARMm • Apply a pull force to a molecular system to: • Estimate the ligand binding free energy • Study the conformational details of the ligand unbinding process • Investigate protein unfolding or conformational • Major DMol3 Performance Improvement: The latest release changes of the density functional theory program DMol3, version • Two protocols have been included to enable the simulation 2016, includes dramatic performance improvements, both in of protein or protein-ligand complexes while -
BIOVIA Discovery Studio
3DS.COM/BIOVIA3DS.COM/BIOVIA © © DassaultDassault Systèmes Systèmes| |Confidential Confidential InformationInformation | |3/16/2019 3/16/2019| BIOVIA Discovery Discovery BIOVIA COMPREHENSIVE MODELING 創源生技 FOR FOR SCIENCESLIFE ANDSIMULATIONS 經理 陳冠文 分子視算中心 Studio (Gene) 3DS.COM/BIOVIA © Dassault Systèmes | Confidential Information | 3/16/2019 | Copyright©2019 GGA Corp., All rights reserved. AllCorp., GGA Copyright and Disclaimer • Copyright © 2019 GGA corp. All rights reserved. • This presentation and/or any related documents contains statements regarding our plans or expectations | for future features, enhancements or functionalities of current or future products (collectively "Enhancements"). Our plans or expectations are subject to change at any time at our discretion. 3/16/2019 Accordingly, GGA Corp. is making no representation, undertaking no commitment or legal obligation to create, develop or license any product or Enhancements. • The presentation, documents or any related statements are not intended to, nor shall, create any legal | Confidential Information | Information | Confidential obligation upon GGA Corp., and shall not be relied upon in purchasing any product. Any such obligation shall only result from a written agreement executed by both parties. Systèmes • In addition, information disclosed in this presentation and related documents, whether oral or written, is © Dassault Dassault © confidential or proprietary information of GGA Corp.. It shall be used only for the purpose of furthering our business relationship, and shall not be disclosed to third parties. 3DS.COM/BIOVIA Copyright©2019 GGA Corp., All rights reserved. GGA is part of the BIONET Group (訊聯生物科技) | CEO: Christopher Tsai, Ph.D. 蔡政憲 博士 3/16/2019 Established: Nov. 2008 Main Product & Service Areas: | Confidential Information | Information | Confidential 1. -
Homology Modeling, Virtual Screening, Prime- MMGBSA, Autodock-Identification of Inhibitors of FGR Protein
Article Volume 11, Issue 4, 2021, 11088 - 11103 https://doi.org/10.33263/BRIAC114.1108811103 Homology Modeling, Virtual Screening, Prime- MMGBSA, AutoDock-Identification of Inhibitors of FGR Protein Narasimha Muddagoni 1 , Revanth Bathula 1 , Mahender Dasari 1 , Sarita Rajender Potlapally 1,* 1 Molecular Modeling Laboratory, Department of Chemistry, Nizam College, Osmania University, Hyderabad, India; * Correspondence: [email protected], [email protected]; Scopus Author ID 55317928000 Received: 11.11.2020; Revised: 4.12.2020; Accepted: 5.12.2020; Published: 8.12.2020 Abstract: Carcinogenesis is a multi-stage process in which damage to a cell's genetic material changes the cell from normal to malignant. Tyrosine-protein kinase FGR is a protein, member of the Src family kinases (SFks), nonreceptor tyrosine kinases involved in regulating various signaling pathways that promote cell proliferation and migration. FGR protein is also called Gardner-Rasheed Feline Sarcoma viral (v-fgr) oncogene homolog. FGR, FGR protein has an aberrant expression upregulated and activated by the tumor necrosis factor activation (TNF), enhancing the activity of FGR by phosphorylation and activation, causing ovarian cancer. In the present study, 3D structure of FGR protein is built by using comparative homology modeling techniques using MODELLER9.9 program. Energy minimization of protein is done by NAMD-VMD software. The quality of the protein is evaluated with ProSA, Verify 3D and Ramchandran Plot validated tools. The active site of protein is generated using SiteMap and literature Studies. In the present study of research, FGR protein was subjected to virtual screening with TOSLab ligand molecules database in the Schrodinger suite, to result in 12 lead molecules prioritized based on docking score, binding free energy and ADME properties. -
Quantitative Structure-Activity Relationship and Molecular Docking
Journal of Advanced Research (2017) 8, 33–43 Cairo University Journal of Advanced Research ORIGINAL ARTICLE Quantitative structure-activity relationship and molecular docking studies of a series of quinazolinonyl analogues as inhibitors of gamma amino butyric acid aminotransferase Usman Abdulfatai *, Adamu Uzairu, Sani Uba Department of Chemistry, Ahmadu Bello University, P.M.B. 1044, Zaria, Nigeria GRAPHICAL ABSTRACT ARTICLE INFO ABSTRACT Article history: Quantitative structure-activity relationship and molecular docking studies were carried out on a Received 4 July 2016 series of quinazolinonyl analogues as anticonvulsant inhibitors. Density Functional Theory Received in revised form 11 October (DFT) quantum chemical calculation method was used to find the optimized geometry of the 2016 anticonvulsants inhibitors. Four types of molecular descriptors were used to derive a quantita- tive relation between anticonvulsant activity and structural properties. The relevant molecular * Corresponding author. Fax: +234 (+603) 6196 4053. E-mail address: [email protected] (U. Abdulfatai). Peer review under responsibility of Cairo University. Production and hosting by Elsevier http://dx.doi.org/10.1016/j.jare.2016.10.004 2090-1232 Ó 2016 Production and hosting by Elsevier B.V. on behalf of Cairo University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). 34 U. Abdulfatai et al. Accepted 15 October 2016 descriptors were selected by Genetic Function Algorithm (GFA). The best model was validated Available online 16 November 2016 and found to be statistically significant with squared correlation coefficient (R2) of 0.934, 2 adjusted squared correlation coefficient (Radj) value of 0.912, Leave one out (LOO) cross valida- 2 2 Keywords: tion coefficient (Q ) value of 0.8695 and the external validation (Rpred) of 0.72. -
Preparing a PDB File the Protein Data Bank (PDB) Is Possibly the World’S Leading Public Source of Three-Dimensional Data for Biological Molecules (1)
Copyright ©2006, Accelrys Software Inc. All rights reserved. Preparing a PDB File The Protein Data Bank (PDB) is possibly the world’s leading public source of three-dimensional data for biological molecules (1). As of July 2006, over 37,000 entries could be found in the PDB. Hundreds more are being added every month. Both X-ray diffraction and other solid-state techniques account for the majority of the structures. However, over 5500 NMR structures are also available. These deposited structures include proteins, peptides, nucleic acids, carbohydrates, and complexes of these molecules. Figure 1: Schematic view of the ligand-binding domain from the As a first step in a modeling project, many vitamin D receptor (PDB file 1IE9). researchers look in the PDB to find available The crystallographic waters are shown structures related to their project. Preparation of as small spheres and the bound ligand these molecules for work in the Discovery Studio is shown as a CPK model. environment is a critical process to your modeling OH efforts. H3C CH In the following steps, we will load a PDB file for 3 the ligand-binding domain of the vitamin D receptor H C 3 (VDR) in complex with a ligand (named VDX in CH 3 this exercise). The file is 1IE9 as reported by Tocchini-Valentini et al. (5). The vitamin D receptor is responsible for the expression of a H variety of genes including calcium metabolism, bone formation, and cell growth and differentiation CH 2 (2). Understanding VDR conformational changes resulting from interactions with bound ligands may HO OH help to identify and treat persons at risk for Figure 2: 1α,25-dihydroxyvitamin disorders such as osteoporosis, breast cancer, or D3, the metabolized form of prostate cancer. -
Original Research Paper In-Silico FDA-Approved Drug Repurposing to Find
Original Research Paper In-silico FDA-approved drug repurposing to find the possible treatment of Coronavirus Disease-19 (COVID-19) Kumar Sharp1, Dr. Shubhangi Dange2* 12nd MBBS undergraduate student, Government Medical College and Hospital, Jalgaon 2Associate Professor, Dept. of Microbiology, Government Medical College and Hospital, Jalgaon *Corresponding author: - Dr. Shubhangi Dange, Associate Professor, Department of Microbiology, Government Medical College and Hospital, Jalgaon Email: [email protected] Abstract Identification of potential drug-target interaction for approved drugs serves as the basis of repurposing drugs. Studies have shown polypharmacology as common phenomenon. In-silico approaches help in screening large compound libraries at once which could take years in a laboratory. We screened a library of 1050 FDA-approved drugs against spike glycoprotein of SARS-CoV2 in-silico. Anti-cancer drugs have shown good binding affinity which is much better than hydroxychloroquine and arbidol. We have also introduced a hypothesis named “Bump” hypothesis which and be developed further in field of computational biology. Keywords: spike glycoprotein; FDA; drug repurposing; anti-cancer; hydroxychloroquine Introduction Identification of potential drug-target interaction for approved drugs serves as the basis of repurposing drugs. Studies have shown polypharmacology as common phenomenon [1][2]. Since the three-dimensional structures of proteins of SARS-CoV2 have been mapped it opens opportunity for in-silico approaches of either novel drug discovery or drug repurposing. In the absence of an exact cure or vaccine, coronavirus disease-19 has taken a huge toll of humanity. Our study of target specific drug docking and novel hypothesis contributes in this fight. In-silico approaches help in screening large compound libraries at once which could take years in a laboratory. -
What Is Discovery Studio?
Discovery Studio 2.0 Accelrys Life Science Tool 林進中 分子視算 What is Discovery Studio? • Discovery Studio is a complete modelling and simulations environment for Life Science researchers – Interactive, visual and integrated software – Consistent, contemporary user interface for added ease-of-use – Tools for visualisation, protein modeling, simulations, docking, pharmacophore analysis, QSAR and library design – Access computational servers and tools, share data, monitor jobs, and prepare and communicate their project progress – Windows and Linux clients and servers Accelrys Discovery Studio Application Discovery Studio Pipeline ISV Materials Discovery Accord WeWebbPPortort Pipeline ISV Materials Discovery Accord (web Studio Studio (web PilotPilot ClientClient Studio Studio ClientsClients access) (Pro or Lite ) (e.g., Client Client access) (Pro or Lite ) (e.g., Client Client Spotfire) Spotfire) Client Integration Layer SS c c i iT T e e g g i ic c P P l la a t t f f o o r r m m Tool Integration Layer Data Access Layer Cmd-Line Isentris Chemistry Biology Materials Accord Accord IDBS Oracle ISIS Reporting Statistics ISV Tools Databases Pipeline Pilot - Data Processing and Integration • Integration of data from multiple disparate data sources • Integration of disparate applications – Third party vendors and in- house developed codes under the same environment Pipeline Pilot - Data Processing and Integration • Automated execution of routine processes • Standardised data management • Capture of workflows and deployment of best practice Interoperability -
Hands-On Tutorials of Autodock 4 and Autodock Vina
Hands-on tutorials of AutoDock 4 and AutoDock Vina Pei-Ying Chu (朱珮瑩) Supervisor: Jung-Hsin Lin (林榮信) Research Center for Applied Sciences, Academia Sinica 2018 Frontiers in Computational Drug Design, Academia Sinica, March 16-20, 2018 AutoDock http://autodock.scripps.edu AutoDock is a suite of automated docking tools. It is designed to predict how small molecules, such as substrates or drug candidates, bind to a receptor of known 3D structure. AutoDock 4 is free and is available under the GNU General Public License. 2 AutoDock Vina http://vina.scripps.edu/ Because the scoring functions used by AutoDock 4 and AutoDock Vina are different and inexact, on any given problem, either program may provide a better result. AutoDock Vina is available under the Apache license, allowing commercial and 3 non-commercial use and redistribution. http://autodock.scripps.edu/downloads These programs were installed on VM. 4 http://mgltools.scripps.edu/ AutoDockTools (ADT) is developed to help set up the docking. ADT is included in MGLTools packages. 5 In general, each docking (AutoDock 4 and/or AutoDock Vina) requires: 1. structure of the receptor (protein), in pdbqt format 2. structure of the ligand (small molecule, drug, etc.) in pdbqt format 3. docking and grid parameters (search space) PDBQT format is very similar to PDB format but it includes partial charges ('Q') and AutoDock 4 (AD4) atom types ('T'). • Preparing the ligand involves ensuring that its atoms are assigned the correct AutoDock4 atom types, adding Gasteiger charges if necessary, merging non-polar hydrogens, detecting aromatic carbons if any, and setting up the 'torsion tree'.