Confbuster: Open-Source Tools for Macrocycle Conformational Search and Analysis Xavier Barbeau1, Antony T
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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. -
Open Data, Open Source and Open Standards in Chemistry: the Blue Obelisk five Years On
Open Data, Open Source and Open Standards in chemistry: The Blue Obelisk ¯ve years on Noel M O'Boyle¤1 , Rajarshi Guha2 , Egon L Willighagen3 , Samuel E Adams4 , Jonathan Alvarsson5 , Richard L Apodaca6 , Jean-Claude Bradley7 , Igor V Filippov8 , Robert M Hanson9 , Marcus D Hanwell10 , Geo®rey R Hutchison11 , Craig A James12 , Nina Jeliazkova13 , Andrew SID Lang14 , Karol M Langner15 , David C Lonie16 , Daniel M Lowe4 , J¶er^omePansanel17 , Dmitry Pavlov18 , Ola Spjuth5 , Christoph Steinbeck19 , Adam L Tenderholt20 , Kevin J Theisen21 , Peter Murray-Rust4 1Analytical and Biological Chemistry Research Facility, Cavanagh Pharmacy Building, University College Cork, College Road, Cork, Co. Cork, Ireland 2NIH Center for Translational Therapeutics, 9800 Medical Center Drive, Rockville, MD 20878, USA 3Division of Molecular Toxicology, Institute of Environmental Medicine, Nobels vaeg 13, Karolinska Institutet, 171 77 Stockholm, Sweden 4Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge, Lens¯eld Road, CB2 1EW, UK 5Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden 6Metamolecular, LLC, 8070 La Jolla Shores Drive #464, La Jolla, CA 92037, USA 7Department of Chemistry, Drexel University, 32nd and Chestnut streets, Philadelphia, PA 19104, USA 8Chemical Biology Laboratory, Basic Research Program, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA 9St. Olaf College, 1520 St. Olaf Ave., North¯eld, MN 55057, USA 10Kitware, Inc., 28 Corporate Drive, Clifton Park, NY 12065, USA 11Department of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, PA 15260, USA 12eMolecules Inc., 380 Stevens Ave., Solana Beach, California 92075, USA 13Ideaconsult Ltd., 4.A.Kanchev str., So¯a 1000, Bulgaria 14Department of Engineering, Computer Science, Physics, and Mathematics, Oral Roberts University, 7777 S. -
64-194 Projekt Parallelrechnerevaluation Abschlussbericht
64-194 Projekt Parallelrechnerevaluation Abschlussbericht Automatisierte Build-Tests für Spack Sarah Lubitz Malte Fock [email protected] [email protected] Studiengang: LA Berufliche Schulen – Informatik Studiengang LaGym - Informatik Matr.-Nr. 6570465 Matr.-Nr. 6311966 Inhaltsverzeichnis i Inhaltsverzeichnis 1 Einleitung1 1.1 Motivation.....................................1 1.2 Vorbereitungen..................................2 1.2.1 Spack....................................2 1.2.2 Python...................................2 1.2.3 Unix-Shell.................................3 1.2.4 Slurm....................................3 1.2.5 GitHub...................................4 2 Code 7 2.1 Automatisiertes Abrufen und Installieren von Spack-Paketen.......7 2.1.1 Python Skript: install_all_packets.py..................7 2.2 Batch-Jobskripte..................................9 2.2.1 Test mit drei Paketen........................... 10 2.3 Analyseskript................................... 11 2.3.1 Test mit 500 Paketen........................... 12 2.3.2 Code zur Analyse der Error-Logs, forts................. 13 2.4 Hintereinanderausführung der Installationen................. 17 2.4.1 Wrapper Skript.............................. 19 2.5 E-Mail Benachrichtigungen........................... 22 3 Testlauf 23 3.1 Durchführung und Ergebnisse......................... 23 3.2 Auswertung und Schlussfolgerungen..................... 23 3.3 Fazit und Ausblick................................ 24 4 Schulische Relevanz 27 Literaturverzeichnis -
How to Print a Crystal Structure Model in 3D Teng-Hao Chen,*A Semin Lee, B Amar H
Electronic Supplementary Material (ESI) for CrystEngComm. This journal is © The Royal Society of Chemistry 2014 How to Print a Crystal Structure Model in 3D Teng-Hao Chen,*a Semin Lee, b Amar H. Flood b and Ognjen Š. Miljanić a a Department of Chemistry, University of Houston, 112 Fleming Building, Houston, Texas 77204-5003, United States b Department of Chemistry, Indiana University, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States Supporting Information Instructions for Printing a 3D Model of an Interlocked Molecule: Cyanostars .................................... S2 Instructions for Printing a "Molecular Spinning Top" in 3D: Triazolophanes ..................................... S3 References ....................................................................................................................................................................... S4 S1 Instructions for Printing a 3D Model of an Interlocked Molecule: Cyanostars Mechanically interlocked molecules such as rotaxanes and catenanes can also be easily printed in 3D. As long as the molecule is correctly designed and modeled, there is no need to assemble and glue the components after printing—they are printed inherently as mechanically interlocked molecules. We present the preparation of a cyanostar- based [3]rotaxane as an example. A pair of sandwiched cyanostars S1 (CS ) derived from the crystal structure was "cleaned up" in the molecular computation software Spartan ʹ10, i.e. disordered atoms were removed. A linear molecule was created and threaded through the cavity of two cyanostars (Figure S1a). Using a space filling view of the molecule, the three components were spaced sufficiently far apart to ensure that they did not make direct contact Figure S1 . ( a) Side and top view of two cyanostars ( CS ) with each other when they are printed. threaded with a linear molecule. ( b) Side and top view of a Bulky stoppers were added to each [3]rotaxane. -
Conformational and Chemical Selection by a Trans-Acting Editing
Correction BIOCHEMISTRY Correction for “Conformational and chemical selection by a trans- acting editing domain,” by Eric M. Danhart, Marina Bakhtina, William A. Cantara, Alexandra B. Kuzmishin, Xiao Ma, Brianne L. Sanford, Marija Kosutic, Yuki Goto, Hiroaki Suga, Kotaro Nakanishi, Ronald Micura, Mark P. Foster, and Karin Musier-Forsyth, which was first published August 2, 2017; 10.1073/pnas.1703925114 (Proc Natl Acad Sci USA 114:E6774–E6783). The authors note that Oscar Vargas-Rodriguez should be added to the author list between Brianne L. Sanford and Marija Kosutic. Oscar Vargas-Rodriguez should be credited with de- signing research, performing research, and analyzing data. The corrected author line, affiliation line, and author contributions appear below. The online version has been corrected. The authors note that the following statement should be added to the Acknowledgments: “We thank Daniel McGowan for assistance with preparing mutant proteins.” Eric M. Danharta,b, Marina Bakhtinaa,b, William A. Cantaraa,b, Alexandra B. Kuzmishina,b,XiaoMaa,b, Brianne L. Sanforda,b, Oscar Vargas-Rodrigueza,b, Marija Kosuticc,d,YukiGotoe, Hiroaki Sugae, Kotaro Nakanishia,b, Ronald Micurac,d, Mark P. Fostera,b, and Karin Musier-Forsytha,b aDepartment of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210; bCenter for RNA Biology, The Ohio State University, Columbus, OH 43210; cInstitute of Organic Chemistry, Leopold Franzens University, A-6020 Innsbruck, Austria; dCenter for Molecular Biosciences, Leopold Franzens University, A-6020 Innsbruck, Austria; and eDepartment of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan Author contributions: E.M.D., M.B., W.A.C., B.L.S., O.V.-R., M.P.F., and K.M.-F. -
Package Name Software Description Project
A S T 1 Package Name Software Description Project URL 2 Autoconf An extensible package of M4 macros that produce shell scripts to automatically configure software source code packages https://www.gnu.org/software/autoconf/ 3 Automake www.gnu.org/software/automake 4 Libtool www.gnu.org/software/libtool 5 bamtools BamTools: a C++ API for reading/writing BAM files. https://github.com/pezmaster31/bamtools 6 Biopython (Python module) Biopython is a set of freely available tools for biological computation written in Python by an international team of developers www.biopython.org/ 7 blas The BLAS (Basic Linear Algebra Subprograms) are routines that provide standard building blocks for performing basic vector and matrix operations. http://www.netlib.org/blas/ 8 boost Boost provides free peer-reviewed portable C++ source libraries. http://www.boost.org 9 CMake Cross-platform, open-source build system. CMake is a family of tools designed to build, test and package software http://www.cmake.org/ 10 Cython (Python module) The Cython compiler for writing C extensions for the Python language https://www.python.org/ 11 Doxygen http://www.doxygen.org/ FFmpeg is the leading multimedia framework, able to decode, encode, transcode, mux, demux, stream, filter and play pretty much anything that humans and machines have created. It supports the most obscure ancient formats up to the cutting edge. No matter if they were designed by some standards 12 ffmpeg committee, the community or a corporation. https://www.ffmpeg.org FFTW is a C subroutine library for computing the discrete Fourier transform (DFT) in one or more dimensions, of arbitrary input size, and of both real and 13 fftw complex data (as well as of even/odd data, i.e. -
Conformational Analysis 3D Structures, Conformations and Molecular Surfaces
Conformational Analysis 3D Structures, Conformations and Molecular Surfaces Christof H. Schwab Molecular Networks GmbH Altamira LLC Henkestr. 91 1455 Candlewood Drive 91052 Erlangen, Germany mn-am.com Columbus, Ohio 43235, USA Molecular Networks and Altamira MN-AM Erlangen, Germany Columbus, Ohio, USA Friedrich-Alexander-Universität The Ohio State University 1997 2008 Chemoinformatics 3D structure generation Physicochemical and reaction properties Metabolic reaction knowledge Computational toxicology and risk assessment Database and knowledgebase Predictive models Consulting services 2 Product Lines – Chemoinformatics CORINA Classic CORINA Classic Industry-standard 3D structure generation CORINA Symphony Profiling and managing of chemical datasets Workflows Structure cleaning/processing Descriptor generation (properties and fragments) SYLVIA Estimation of synthetic accessibility of compounds Public tools ChemoTyper (with ToxPrint Chemotypes) https://chemotyper.org, https://toxprint.org 3 Product Lines – Computational Toxicology and Risk Assessment ChemTunes Platform to support decision making in human health and regulatory critical endpoints Toxicity database (all endpoints) "Inventory" concept for compound location ChemTunes ToxGPS Prediction models All human health related endpoints Workflows TTC (thresholds of toxicological concern) Genotoxic Impurities Read-Across (in development) Workflow ICH M7 GTI (in development) 4 3D Structures, Conformations and Molecular Surfaces – Overview 3D structures – why, -
Optimizing the Use of Open-Source Software Applications in Drug
DDT • Volume 11, Number 3/4 • February 2006 REVIEWS TICS INFORMA Optimizing the use of open-source • software applications in drug discovery Reviews Werner J. Geldenhuys1, Kevin E. Gaasch2, Mark Watson2, David D. Allen1 and Cornelis J.Van der Schyf1,3 1Department of Pharmaceutical Sciences, School of Pharmacy,Texas Tech University Health Sciences Center, Amarillo,TX, USA 2West Texas A&M University, Canyon,TX, USA 3Pharmaceutical Chemistry, School of Pharmacy, North-West University, Potchefstroom, South Africa Drug discovery is a time consuming and costly process. Recently, a trend towards the use of in silico computational chemistry and molecular modeling for computer-aided drug design has gained significant momentum. This review investigates the application of free and/or open-source software in the drug discovery process. Among the reviewed software programs are applications programmed in JAVA, Perl and Python, as well as resources including software libraries. These programs might be useful for cheminformatics approaches to drug discovery, including QSAR studies, energy minimization and docking studies in drug design endeavors. Furthermore, this review explores options for integrating available computer modeling open-source software applications in drug discovery programs. To bring a new drug to the market is very costly, with the current of combinatorial approaches and HTS. The addition of computer- price tag approximating US$800 million, according to data reported aided drug design technologies to the R&D approaches of a com- in a recent study [1]. Therefore, it is not surprising that pharma- pany, could lead to a reduction in the cost of drug design and ceutical companies are seeking ways to optimize costs associated development by up to 50% [6,7].