Skeledock: a Web Application for Scaffold Docking in Playmolecule
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Rdock Reference Guide Rdock Development Team August 27, 2015 Contents
rDock Reference Guide rDock Development Team August 27, 2015 Contents 1 Preface 4 2 Acknowledgements 4 3 Introduction 4 4 Configuration 4 5 Cavity mapping 6 5.1 Two sphere method . 6 5.2 Reference ligand method . 8 6 Scoring function reference 11 6.1 Component Scoring Functions . 11 6.1.1 van der Waals potential . 11 6.1.2 Empirical attractive and repulsive polar potentials . 11 6.1.3 Solvation potential . 12 6.1.4 Dihedral potential . 13 6.2 Intermolecular scoring functions under evaluation . 13 6.2.1 Training sets . 13 6.2.2 Scoring Functions Design . 13 6.2.3 Scoring Functions Validation . 14 6.3 Code Implementation . 15 7 Docking protocol 17 7.1 Protocol Summary . 17 7.1.1 Pose Generation . 17 7.1.2 Genetic Algorithm . 17 7.1.3 Monte Carlo . 17 7.1.4 Simplex . 18 7.2 Code Implementation . 18 7.3 Standard rDock docking protocol (dock.prm) . 18 8 System definition file reference 22 8.1 Receptor definition . 22 8.2 Ligand definition . 23 8.3 Solvent definition . 24 8.4 Cavity mapping . 25 8.5 Cavity restraint . 27 8.6 Pharmacophore restraints . 27 8.7 NMR restraints . 28 8.8 Example system definition files . 28 9 Molecular files and atoms typing 30 9.1 Atomic properties. 30 9.2 Difference between formal charge and distributed formal charge . 30 9.3 Parsing a MOL2 file . 31 9.4 Parsing an SD file . 31 9.5 Assigning distributed formal charges to the receptor . 31 10 rDock file formats 32 10.1 .prm file format . -
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 ................................................. -
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. -
In Silico Screening and Molecular Docking of Bioactive Agents Towards Human Coronavirus Receptor
GSC Biological and Pharmaceutical Sciences, 2020, 11(01), 132–140 Available online at GSC Online Press Directory GSC Biological and Pharmaceutical Sciences e-ISSN: 2581-3250, CODEN (USA): GBPSC2 Journal homepage: https://www.gsconlinepress.com/journals/gscbps (RESEARCH ARTICLE) In silico screening and molecular docking of bioactive agents towards human coronavirus receptor Pratyush Kumar *, Asnani Alpana, Chaple Dinesh and Bais Abhinav Priyadarshini J. L. College of Pharmacy, Electronic Building, Electronic Zone, MIDC, Hingna Road, Nagpur-440016, Maharashtra, India. Publication history: Received on 09 April 2020; revised on 13 April 2020; accepted on 15 April 2020 Article DOI: https://doi.org/10.30574/gscbps.2020.11.1.0099 Abstract Coronavirus infection has turned into pandemic despite of efforts of efforts of countries like America, Italy, China, France etc. Currently India is also outraged by the virulent effect of coronavirus. Although World Health Organisation initially claimed to have all controls over the virus, till date infection has coasted several lives worldwide. Currently we do not have enough time for carrying out traditional approaches of drug discovery. Computer aided drug designing approaches are the best solution. The present study is completely dedicated to in silico approaches like virtual screening, molecular docking and molecular property calculation. The library of 15 bioactive molecules was built and virtual screening was carried towards the crystalline structure of human coronavirus (6nzk) which was downloaded from protein database. Pyrx virtual screening tool was used and results revealed that F14 showed best binding affinity. The best screened molecule was further allowed to dock with the target using Autodock vina software. -
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. -
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San Jon, Cal. fhit 31nsr Pasffie Game Subs Rate, $1.00 Rally to be Per Quarter ftitr Tutirgr Held Thuraday 2 2 X! I . J1,1-1 11.1FURNiA, WED \ I --1).\1., (2LI OBER II, 19i; r_12 Attractive Program for NOTICE Concert Series Tickets Placed on In order to choose a yell leader Pre-game Rally Planned; for the coming year, an election Sale Today, Committee Announces; will be held in front of the Morris Dailey auditorium all dily today, Parade Will Climax Fun Wednesday, October II. Presi- Naom Blinder, Violinist, Here Nov. 7 dent Covello asks every tudent Wildest Event of Evening Will to cooperate by voting. The Car.- Probably Be Parade Thru Leads Rally Famous Violinist To Appear Concert Series Tickets Sale didate re Howard Burns and Enthusiasm Downtown San Jose 7 (,hairman Shown Jim Hamilton. At S. J. State Nov. 'psis By Students Will In Music Program Dud DeGroot and Team - - -- Speeches During Alice Dixon Heads Conimittee Give \ the initial concert in the IO33- +4 Rally In Auditorium E. Higgins Of Music Department _ _ Plans rie, San Jose State music 'rivers will Concert &ries iniee the opportunity to bear Naom di,. ,rantest pre -game rally in the Radio Pep Rally Blinder. famous violinist, N9V. 7. ot San Jose State will be held in The tickets tor 'la Blinder is now concert master of th, series go on sal,- today '. Morris Dailey auditorium thit ir. qa. -peed san Francisco Symphony orchestra, and The!. may also be ! mening at 7:30, according to At Station KQW porch:, . -
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'. -
Dockomatic - Automated Ligand Creation and Docking Casey W
Boise State University ScholarWorks Chemistry Faculty Publications and Presentations Department of Chemistry and Biochemistry 11-8-2010 DockoMatic - Automated Ligand Creation and Docking Casey W. Bullock Boise State University Reed B. Jacob Boise State University Owen M. McDougal Boise State University Greg Hampikian Boise State University Tim Andersen Boise State University This document was originally published by BioMed Central in BMC Research Notes. Copyright restrictions may apply. DOI: 10.1186/ 1756-0500-3-289 DockoMatic - Automated Ligand Creation and Docking Casey W. Bullock1, Reed B. Jacob2, Owen M. McDougal3, Greg Hampikian4, Tim Andersen∗5 1;5Computer Science Department, Boise State University, Boise, Idaho 83725, USA 2;3Department of Chemistry and Biochemistry, Boise State University, Boise, Idaho 83725, USA 4Department of Biological Sciences, Boise State University, Boise, Idaho 83725, USA Email: Casey W. Bullock - [email protected]; Reed B. Jacob - [email protected]; Owen M. McDougal - [email protected]; Greg Hampikian - [email protected]; Tim Andersen∗- [email protected]; ∗Corresponding author Abstract Background: The application of computational modeling to rationally design drugs and characterize macro biomolecular receptors has proven increasingly useful due to the accessibility of computing clusters and clouds. AutoDock is a well-known and powerful software program used to model ligand to receptor binding interactions. In its current version, AutoDock requires significant amounts of user time to setup and run jobs, and collect results. This paper presents DockoMatic, a user friendly Graphical User Interface (GUI) application that eases and automates the creation and management of AutoDock jobs for high throughput screening of ligand to receptor interactions. -
Getting Started with Rdock Dr
Getting Started with rDock Dr. David Morley Getting Started with rDock Dr. David Morley Copyright © 2006 Vernalis Table of Contents Overview ............................................................................................................ vi 1. Prerequisites ..................................................................................................... 1 2. Unpacking the distribution files ............................................................................ 2 3. Building rDock .................................................................................................. 4 4. Running a short validation experiment ................................................................... 6 iv List of Tables 1.1. Required packages for building and running rDock ................................................ 1 2.1. rDock distribution files ..................................................................................... 2 3.1. Standard tmake build targets provided ............................................................... 4 4.1. Complexes included in the cpu_benchmark experiment ...................................... 6 4.2. rDock score components output as SD data fields .................................................. 7 v Overview rDock is a high-throughput molecular docking platform for protein and RNA targets. Under devel- opment at Vernalis (formerly RiboTargets) since 1998, the software (formerly known as RiboDock1), scoring functions, and search protocols have been refined continuously to meet the de- -
Computational Evidence for Nitro Derivatives of Quinoline and Quinoline N‑Oxide As Low‑Cost Alternative for the Treatment of SARS‑Cov‑2 Infection Letícia C
www.nature.com/scientificreports OPEN Computational evidence for nitro derivatives of quinoline and quinoline N‑oxide as low‑cost alternative for the treatment of SARS‑CoV‑2 infection Letícia C. Assis1, Alexandre A. de Castro1, João P. A. de Jesus2, Eugenie Nepovimova3, Kamil Kuca3*, Teodorico C. Ramalho1,3 & Felipe A. La Porta2* A new and more aggressive strain of coronavirus, known as SARS‑CoV‑2, which is highly contagious, has rapidly spread across the planet within a short period of time. Due to its high transmission rate and the signifcant time–space between infection and manifestation of symptoms, the WHO recently declared this a pandemic. Because of the exponentially growing number of new cases of both infections and deaths, development of new therapeutic options to help fght this pandemic is urgently needed. The target molecules of this study were the nitro derivatives of quinoline and quinoline N‑oxide. Computational design at the DFT level, docking studies, and molecular dynamics methods as a well‑reasoned strategy will aid in elucidating the fundamental physicochemical properties and molecular functions of a diversity of compounds, directly accelerating the process of discovering new drugs. In this study, we discovered isomers based on the nitro derivatives of quinoline and quinoline N‑oxide, which are biologically active compounds and may be low‑cost alternatives for the treatment of infections induced by SARS‑CoV‑2. We are currently facing a new coronavirus disease designated as COVID-19. It started in China and has spread rapidly around the world, resulting in serious threats to international health and the economy1,2.