Guide for Using Rosetta When Designing Ligand Binding Sites
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Latest Stable Copy and Install It With: Chmod+X Chimera- *.Bin&&./Chimera- *.Bin
Tangram Suite Documentation Release 0.0.8 InsiliChem Mar 26, 2019 General instructions 1 What is Tangram Suite 3 2 How to install Tangram Suite5 3 General usage 7 4 FAQ & Known issues 9 5 List of Tans 11 6 Tangram BondOrder 13 7 Tangram QMSetup 15 8 Tangram DummyAtoms 17 9 Tangram GAUDIView 19 10 Tangram MMSetup 21 11 Tangram NCIPlot 23 12 Tangram NormalModes 25 13 Tangram OrbiTraj 27 14 Tangram PLIP 29 15 Tangram PoPMuSiCGUI 31 16 Tangram PropKaGUI 33 17 Tangram ReVina 35 18 Tangram SubAlign 37 19 Tangram TalaDraw 39 i ii Tangram Suite Documentation, Release 0.0.8 It’s composed of several independent graphical interfaces and commands for UCSF Chimera. General instructions 1 Tangram Suite Documentation, Release 0.0.8 2 General instructions CHAPTER 1 What is Tangram Suite In progress 3 Tangram Suite Documentation, Release 0.0.8 4 Chapter 1. What is Tangram Suite CHAPTER 2 How to install Tangram Suite 2.1 Install the full Tangram Suite (recommended) 1 - If you don’t have UCSF Chimera installed, download the latest stable copy and install it with: chmod+x chimera- *.bin&&./chimera- *.bin 2 - Download the latest installer from the releases page (Linux and MacOS only) and run it with: bash tangram-*.sh 2.2 Using the conda meta-package Instead of using the Bash installer, you can use conda (if you are already using it) to create a new environment with the tangram metapackage, which will handle all the dependencies. While in alpha, both insilichem channels are needed (the main one and also the dev label): conda create-n tangram-c insilichem/label/dev-c insilichem-c omnia-c rdkit-c ,!conda-forge tangram 2.3 Updating extensions Each extension will check if there’s a new release available every time you launch it. -
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 ................................................. -
Open Data, Open Source, and Open Standards in Chemistry: the Blue Obelisk Five Years On" Journal of Cheminformatics Vol
Oral Roberts University Digital Showcase College of Science and Engineering Faculty College of Science and Engineering Research and Scholarship 10-14-2011 Open Data, Open Source, and Open Standards in Chemistry: The lueB Obelisk five years on Andrew Lang Noel M. O'Boyle Rajarshi Guha National Institutes of Health Egon Willighagen Maastricht University Samuel Adams See next page for additional authors Follow this and additional works at: http://digitalshowcase.oru.edu/cose_pub Part of the Chemistry Commons Recommended Citation Andrew Lang, Noel M O'Boyle, Rajarshi Guha, Egon Willighagen, et al.. "Open Data, Open Source, and Open Standards in Chemistry: The Blue Obelisk five years on" Journal of Cheminformatics Vol. 3 Iss. 37 (2011) Available at: http://works.bepress.com/andrew-sid-lang/ 19/ This Article is brought to you for free and open access by the College of Science and Engineering at Digital Showcase. It has been accepted for inclusion in College of Science and Engineering Faculty Research and Scholarship by an authorized administrator of Digital Showcase. For more information, please contact [email protected]. Authors Andrew Lang, Noel M. O'Boyle, Rajarshi Guha, Egon Willighagen, Samuel Adams, Jonathan Alvarsson, Jean- Claude Bradley, Igor Filippov, Robert M. Hanson, Marcus D. Hanwell, Geoffrey R. Hutchison, Craig A. James, Nina Jeliazkova, Karol M. Langner, David C. Lonie, Daniel M. Lowe, Jerome Pansanel, Dmitry Pavlov, Ola Spjuth, Christoph Steinbeck, Adam L. Tenderholt, Kevin J. Theisen, and Peter Murray-Rust This article is available at Digital Showcase: http://digitalshowcase.oru.edu/cose_pub/34 Oral Roberts University From the SelectedWorks of Andrew Lang October 14, 2011 Open Data, Open Source, and Open Standards in Chemistry: The Blue Obelisk five years on Andrew Lang Noel M O'Boyle Rajarshi Guha, National Institutes of Health Egon Willighagen, Maastricht University Samuel Adams, et al. -
Real-Time Pymol Visualization for Rosetta and Pyrosetta
Real-Time PyMOL Visualization for Rosetta and PyRosetta Evan H. Baugh1, Sergey Lyskov1, Brian D. Weitzner1, Jeffrey J. Gray1,2* 1 Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America, 2 Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, Maryland, United States of America Abstract Computational structure prediction and design of proteins and protein-protein complexes have long been inaccessible to those not directly involved in the field. A key missing component has been the ability to visualize the progress of calculations to better understand them. Rosetta is one simulation suite that would benefit from a robust real-time visualization solution. Several tools exist for the sole purpose of visualizing biomolecules; one of the most popular tools, PyMOL (Schro¨dinger), is a powerful, highly extensible, user friendly, and attractive package. Integrating Rosetta and PyMOL directly has many technical and logistical obstacles inhibiting usage. To circumvent these issues, we developed a novel solution based on transmitting biomolecular structure and energy information via UDP sockets. Rosetta and PyMOL run as separate processes, thereby avoiding many technical obstacles while visualizing information on-demand in real-time. When Rosetta detects changes in the structure of a protein, new coordinates are sent over a UDP network socket to a PyMOL instance running a UDP socket listener. PyMOL then interprets and displays the molecule. This implementation also allows remote execution of Rosetta. When combined with PyRosetta, this visualization solution provides an interactive environment for protein structure prediction and design. Citation: Baugh EH, Lyskov S, Weitzner BD, Gray JJ (2011) Real-Time PyMOL Visualization for Rosetta and PyRosetta. -
Computer-Assisted Catalyst Development Via Automated Modelling of Conformationally Complex Molecules
www.nature.com/scientificreports OPEN Computer‑assisted catalyst development via automated modelling of conformationally complex molecules: application to diphosphinoamine ligands Sibo Lin1*, Jenna C. Fromer2, Yagnaseni Ghosh1, Brian Hanna1, Mohamed Elanany3 & Wei Xu4 Simulation of conformationally complicated molecules requires multiple levels of theory to obtain accurate thermodynamics, requiring signifcant researcher time to implement. We automate this workfow using all open‑source code (XTBDFT) and apply it toward a practical challenge: diphosphinoamine (PNP) ligands used for ethylene tetramerization catalysis may isomerize (with deleterious efects) to iminobisphosphines (PPNs), and a computational method to evaluate PNP ligand candidates would save signifcant experimental efort. We use XTBDFT to calculate the thermodynamic stability of a wide range of conformationally complex PNP ligands against isomeriation to PPN (ΔGPPN), and establish a strong correlation between ΔGPPN and catalyst performance. Finally, we apply our method to screen novel PNP candidates, saving signifcant time by ruling out candidates with non‑trivial synthetic routes and poor expected catalytic performance. Quantum mechanical methods with high energy accuracy, such as density functional theory (DFT), can opti- mize molecular input structures to a nearby local minimum, but calculating accurate reaction thermodynamics requires fnding global minimum energy structures1,2. For simple molecules, expert intuition can identify a few minima to focus study on, but an alternative approach must be considered for more complex molecules or to eventually fulfl the dream of autonomous catalyst design 3,4: the potential energy surface must be frst surveyed with a computationally efcient method; then minima from this survey must be refned using slower, more accurate methods; fnally, for molecules possessing low-frequency vibrational modes, those modes need to be treated appropriately to obtain accurate thermodynamic energies 5–7. -
Designing Universal Chemical Markup (UCM) Through the Reusable Methodology Based on Analyzing Existing Related Formats
Designing Universal Chemical Markup (UCM) through the reusable methodology based on analyzing existing related formats Background: In order to design concepts for a new general-purpose chemical format we analyzed the strengths and weaknesses of current formats for common chemical data. While the new format is discussed more in the next article, here we describe our software s t tools and two stage analysis procedure that supplied the necessary information for the n i r development. The chemical formats analyzed in both stages were: CDX, CDXML, CML, P CTfile and XDfile. In addition the following formats were included in the first stage only: e r P CIF, InChI, NCBI ASN.1, NCBI XML, PDB, PDBx/mmCIF, PDBML, SMILES, SLN and Mol2. Results: A two stage analysis process devised for both XML (Extensible Markup Language) and non-XML formats enabled us to verify if and how potential advantages of XML are utilized in the widely used general-purpose chemical formats. In the first stage we accumulated information about analyzed formats and selected the formats with the most general-purpose chemical functionality for the second stage. During the second stage our set of software quality requirements was used to assess the benefits and issues of selected formats. Additionally, the detailed analysis of XML formats structure in the second stage helped us to identify concepts in those formats. Using these concepts we came up with the concise structure for a new chemical format, which is designed to provide precise built-in validation capabilities and aims to avoid the potential issues of analyzed formats. -
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. -
Chapter 1. Origins of Mac OS X
1 Chapter 1. Origins of Mac OS X "Most ideas come from previous ideas." Alan Curtis Kay The Mac OS X operating system represents a rather successful coming together of paradigms, ideologies, and technologies that have often resisted each other in the past. A good example is the cordial relationship that exists between the command-line and graphical interfaces in Mac OS X. The system is a result of the trials and tribulations of Apple and NeXT, as well as their user and developer communities. Mac OS X exemplifies how a capable system can result from the direct or indirect efforts of corporations, academic and research communities, the Open Source and Free Software movements, and, of course, individuals. Apple has been around since 1976, and many accounts of its history have been told. If the story of Apple as a company is fascinating, so is the technical history of Apple's operating systems. In this chapter,[1] we will trace the history of Mac OS X, discussing several technologies whose confluence eventually led to the modern-day Apple operating system. [1] This book's accompanying web site (www.osxbook.com) provides a more detailed technical history of all of Apple's operating systems. 1 2 2 1 1.1. Apple's Quest for the[2] Operating System [2] Whereas the word "the" is used here to designate prominence and desirability, it is an interesting coincidence that "THE" was the name of a multiprogramming system described by Edsger W. Dijkstra in a 1968 paper. It was March 1988. The Macintosh had been around for four years. -
UCSF Chimera Was Developed by the Computer Graphics Laboratory at the University of California, San Francisco, Under Support of NIH Grant P41-RR01081
matrixcopy apply the transformation matrix of one model to another tcolor color using texture map colors UCSF Chimera matrixget write the current transformation matrices to a ®le texture de®ne texture maps and associated colors QUICK REFERENCE GUIDE June 2007 matrixset read and apply transformation matrices from a ®le thickness move the clipping planes in opposite directions minimize energy-minimize structures turn rotate about the X, Y, or Z axis mmaker (matchmaker) align models in sequence, then in 3D vdw* display van der Waals (VDW) surface modelcolor set color at the model level vdwde®ne* set VDW radii Commands modeldisplay* set display at the model level vdwdensity set VDW surface dot density *reverse function Äcommand available move translate along the X, Y, or Z axis version show copyright information and Chimera version movie capture image frames and assemble them into a movie viewdock start ViewDock and load docking results 2dlabels create arbitrary text labels and place them in 2D namesel name and save the current selection wait suspend command processing until motion has stopped ac enable accelerators (keyboard shortcuts) neon create a shadowed stick/tube image (not on Windows) window adjust the view to contain the speci®ed atoms addaa add an amino acid to a peptide C-terminus objdisplay* display graphical objects windowsize adjust the dimensions of the graphics window addcharge assign partial charges to atoms open* read structures and data, execute command ®les write save a molecule model as a PDB ®le addh add hydrogens pdbrun -
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.