Preparing and Analyzing Large Molecular Simulations With
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Olaseni Sode
Olaseni Sode Work Address: 5735 S. Ellis Ave. [email protected] Searle 231 +1 (314) 856-2373 The University of Chicago Chicago, IL 60615 Home Address: 1119 E Hyde Park Blvd [email protected] Apt. 3E +1 (314) 856-2373 Chicago, IL 60615 http://www.olaseniosode.com Education University of Illinois at Urbana-Champaign Urbana, IL Ph.D. in Chemistry September 2012 – Supervisor: Professor So Hirata – Thesis title: A Theoretical Study of Molecular Crystals – Transferred from the University of Florida in 2010 with Prof. Hirata Morehouse College Atlanta, GA B.S. & B.A. in Chemistry and French December 2006 – Studied at the Université de Nantes, Nantes, France (2004-2005) Research Experience The University of Chicago Chicago, IL Postdoctoral Research – research advisor: Dr. Gregory A. Voth 2012–present – Studied proposed proton transport minimum free-energy pathways in [FeFe]-hydrogenase via molecular dynamics simulations and the multistate empirical valence bond technique. – Collaborated to develop the multiscale string method to accelerate refinement of chemical reaction pathways of adenosine triphosphate (ATP) hydrolysis in globular and filamentous actin proteins. University of California, San Diego La Jolla, CA Visiting Scholar – research advisor: Dr. Francesco Paesani 2014, 2015 – Developed and implemented a reactive molecular dynamics scheme for condensed phase systems of the hydrated hydronium ions. University of Illinois at Urbana-Champaign Urbana, IL Doctoral Research – research advisor: Dr. So Hirata 2010-2012 – Characterized the vibrational structure of extended systems including infrared and Raman-active vibrations, as well as phonon dispersions and densities of states in the first Brillouin zone. – Developed zero-point vibrational energy analysis scheme for molecular clusters using a fragmented, local basis scheme. -
Pysimm: a Python Package for Simulation of Molecular Systems
SoftwareX 6 (2016) 7–12 Contents lists available at ScienceDirect SoftwareX journal homepage: www.elsevier.com/locate/softx pysimm: A python package for simulation of molecular systems Michael E. Fortunato a, Coray M. Colina a,b,∗ a Department of Chemistry, University of Florida, Gainesville, FL 32611, United States b Department of Materials Science and Engineering and Nuclear Engineering, University of Florida, Gainesville, FL 32611, United States article info a b s t r a c t Article history: In this work, we present pysimm, a python package designed to facilitate structure generation, simulation, Received 15 August 2016 and modification of molecular systems. pysimm provides a collection of simulation tools and smooth Received in revised form integration with highly optimized third party software. Abstraction layers enable a standardized 1 December 2016 methodology to assign various force field models to molecular systems and perform simple simulations. Accepted 5 December 2016 These features have allowed pysimm to aid the rapid development of new applications specifically in the area of amorphous polymer simulations. Keywords: ' 2016 The Authors. Published by Elsevier B.V. Amorphous polymers Molecular simulation This is an open access article under the CC BY license Python (http://creativecommons.org/licenses/by/4.0/). Code metadata Current code version v0.1 Permanent link to code/repository used for this code version https://github.com/ElsevierSoftwareX/SOFTX-D-16-00070 Legal Code License MIT Code versioning system used git Software code languages, tools, and services used python2.7 Compilation requirements, operating environments & Linux dependencies If available Link to developer documentation/manual http://pysimm.org/documentation/ Support email for questions [email protected] 1. -
Mercury 2.4 User Guide and Tutorials 2011 CSDS Release
Mercury 2.4 User Guide and Tutorials 2011 CSDS Release Copyright © 2010 The Cambridge Crystallographic Data Centre Registered Charity No 800579 Conditions of Use The Cambridge Structural Database System (CSD System) comprising all or some of the following: ConQuest, Quest, PreQuest, Mercury, (Mercury CSD and Materials module of Mercury), VISTA, Mogul, IsoStar, SuperStar, web accessible CSD tools and services, WebCSD, CSD Java sketcher, CSD data file, CSD-UNITY, CSD-MDL, CSD-SDfile, CSD data updates, sub files derived from the foregoing data files, documentation and command procedures (each individually a Component) is a database and copyright work belonging to the Cambridge Crystallographic Data Centre (CCDC) and its licensors and all rights are protected. Use of the CSD System is permitted solely in accordance with a valid Licence of Access Agreement and all Components included are proprietary. When a Component is supplied independently of the CSD System its use is subject to the conditions of the separate licence. All persons accessing the CSD System or its Components should make themselves aware of the conditions contained in the Licence of Access Agreement or the relevant licence. In particular: • The CSD System and its Components are licensed subject to a time limit for use by a specified organisation at a specified location. • The CSD System and its Components are to be treated as confidential and may NOT be disclosed or re- distributed in any form, in whole or in part, to any third party. • Software or data derived from or developed using the CSD System may not be distributed without prior written approval of the CCDC. -
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 ................................................. -
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. -
Instructions on Making Pdf Files Containing 3D
Detailed Instructions for Creating an Embedded Virtual 3-D Image of a Molecule Creating the documents with embedded virtual three-dimensional images may be done fairly easily by repeating the following procedures. Once you have embedded your first image, the use of that image is limited only by your imagination. The following instructions are written in extreme detail with annotated screen shots of the four programs you will use throughout for clarification. Once you are familiar with the procedure embedding a structure of interest should take less than 20 min., limited only by the speed with which you can click a mouse. In practice most of our embedded 3-D images have been created by our undergraduate student collaborators. Step 1. Creating a 2-D image in ChemSketch Using ACD ChemSketch 11.0 Freeware (http://www.acdlabs.com/download/chemsk_download.html ) a two dimensional structure may be drawn as per your specific need (see Figure 1). Once the molecule is drawn, optimized it by clicking the 3-D optimization button then save as an MDL molfile (*.mol). Figure 1: A screen shot of acetone drawn with ChemSketch after 3-D optimization. Step 2. Converting the *.mol file to a *.pdb file using Open Babel The file can now be converted from a .mol file to a .pdb file, Protein Data Bank format, to be properly accessed with the Adobe Acrobat 3D Toolkit 8.1.0. The file conversion is done using Open Babel Graphical User Interface v2.2.0 (openbabel.sourceforge.net) under default settings. Step by step instructions: A. -
A Study on Cheminformatics and Its Applications on Modern Drug Discovery
Available online at www.sciencedirect.com Procedia Engineering 38 ( 2012 ) 1264 – 1275 Internatio na l Conference on Modeling Optimisatio n and Computing (ICMOC 2012) A Study on Cheminformatics and its Applications on Modern Drug Discovery B.Firdaus Begama and Dr. J.Satheesh Kumarb aResearch Scholar, Bharathiar University, Coimbatore, India, [email protected] bAssistant Professor, Bharathiar University, Coimbatore, India, [email protected] Abstract Discovering drugs to a disease is still a challenging task for medical researchers due to the complex structures of biomolecules which are responsible for disease such as AIDS, Cancer, Autism, Alzimear etc. Design and development of new efficient anti-drugs for the disease without any side effects are becoming mandatory in the recent history of human life cycle due to changes in various factors which includes food habit, environmental and migration in human life style. Cheminformaticds deals with discovering drugs based in modern drug discovery techniques which in turn rectifies complex issues in traditional drug discovery system. Cheminformatics tools, helps medical chemist for better understanding of complex structures of chemical compounds. Cheminformatics is a new emerging interdisciplinary field which primarily aims to discover Novel Chemical Entities [NCE] which ultimately results in design of new molecule [chemical data]. It also plays an important role for collecting, storing and analysing the chemical data. This paper focuses on cheminformatics and its applications on drug discovery and modern drug discovery techniques which helps chemist and medical researchers for finding solution to the complex disease. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Noorul Islam Centre for Higher Education. -
Getting Started in Jmol
Getting Started in Jmol Part of the Jmol Training Guide from the MSOE Center for BioMolecular Modeling Interactive version available at http://cbm.msoe.edu/teachingResources/jmol/jmolTraining/started.html Introduction Physical models of proteins are powerful tools that can be used synergistically with computer visualizations to explore protein structure and function. Although it is interesting to explore models and visualizations created by others, it is much more engaging to create your own! At the MSOE Center for BioMolecular Modeling we use the molecular visualization program Jmol to explore protein and molecular structures in fully interactive 3-dimensional displays. Jmol a free, open source molecular visualization program used by students, educators and researchers internationally. The Jmol Training Guide from the MSOE Center for BioMolecular Modeling will provide the tools needed to create molecular renderings, physical models using 3-D printing technologies, as well as Jmol animations for online tutorials or electronic posters. Examples of Proteins in Jmol Jmol allows users to rotate proteins and molecular structures in a fully interactive 3-dimensional display. Some sample proteins designed with Jmol are shown to the right. Hemoglobin Proteins Insulin Proteins Green Fluorescent safely carry oxygen in the help regulate sugar in Proteins create blood. the bloodstream. bioluminescence in animals like jellyfish. Downloading Jmol Jmol Can be Used in Two Ways: 1. As an independent program on a desktop - Jmol can be downloaded to run on your desktop like any other program. It uses a Java platform and therefore functions equally well in a PC or Mac environment. 2. As a web application - Jmol has a web-based version (oftern refered to as "JSmol") that runs on a JavaScript platform and therefore functions equally well on all HTML5 compatible browsers such as Firefox, Internet Explorer, Safari and Chrome. -
An Explicit-Solvent Conformation Search Method Using Open Software
An explicit-solvent conformation search method using open software Kari Gaalswyk and Christopher N. Rowley Department of Chemistry, Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador, Canada ABSTRACT Computer modeling is a popular tool to identify the most-probable conformers of a molecule. Although the solvent can have a large effect on the stability of a conformation, many popular conformational search methods are only capable of describing molecules in the gas phase or with an implicit solvent model. We have developed a work-flow for performing a conformation search on explicitly-solvated molecules using open source software. This method uses replica exchange molecular dynamics (REMD) to sample the conformational states of the molecule efficiently. Cluster analysis is used to identify the most probable conformations from the simulated trajectory. This work-flow was tested on drug molecules a-amanitin and cabergoline to illustrate its capabilities and effectiveness. The preferred conformations of these molecules in gas phase, implicit solvent, and explicit solvent are significantly different. Subjects Biophysics, Pharmacology, Computational Science Keywords Conformation search, Explicit solvent, Cluster analysis, Replica exchange molecular dynamics INTRODUCTION Many molecules can exist in multiple conformational isomers. Conformational isomers have the same chemical bonds, but differ in their 3D geometry because they hold different torsional angles (Crippen & Havel, 1988). The conformation of a molecule can affect Submitted 5 April 2016 chemical reactivity, molecular binding, and biological activity (Struthers, Rivier & Accepted 6May2016 Published 31 May 2016 Hagler, 1985; Copeland, 2011). Conformations differ in stability because they experience different steric, electrostatic, and solute-solvent interactions. The probability, p,ofa Corresponding author Christopher N. -
Python Tools in Computational Chemistry (And Biology)
Python Tools in Computational Chemistry (and Biology) Andrew Dalke Dalke Scientific, AB Göteborg, Sweden EuroSciPy, 26-27 July, 2008 “Why does ‘import numpy’ take 0.4 seconds? Does it need to import 228 libraries?” - My first Numpy-discussion post (paraphrased) Your use case isn't so typical and so suffers on the import time end of the balance. - Response from Robert Kern (Others did complain. Import time down to 0.28s.) 52,000 structures PDB doubles every 2½ years HEADER PHOTORECEPTOR 23-MAY-90 1BRD 1BRD 2 COMPND BACTERIORHODOPSIN 1BRD 3 SOURCE (HALOBACTERIUM $HALOBIUM) 1BRD 4 EXPDTA ELECTRON DIFFRACTION 1BRD 5 AUTHOR R.HENDERSON,J.M.BALDWIN,T.A.CESKA,F.ZEMLIN,E.BECKMANN, 1BRD 6 AUTHOR 2 K.H.DOWNING 1BRD 7 REVDAT 3 15-JAN-93 1BRDB 1 SEQRES 1BRDB 1 REVDAT 2 15-JUL-91 1BRDA 1 REMARK 1BRDA 1 .. ATOM 54 N PRO 8 20.397 -15.569 -13.739 1.00 20.00 1BRD 136 ATOM 55 CA PRO 8 21.592 -15.444 -12.900 1.00 20.00 1BRD 137 ATOM 56 C PRO 8 21.359 -15.206 -11.424 1.00 20.00 1BRD 138 ATOM 57 O PRO 8 21.904 -15.930 -10.563 1.00 20.00 1BRD 139 ATOM 58 CB PRO 8 22.367 -14.319 -13.591 1.00 20.00 1BRD 140 ATOM 59 CG PRO 8 22.089 -14.564 -15.053 1.00 20.00 1BRD 141 ATOM 60 CD PRO 8 20.647 -15.054 -15.103 1.00 20.00 1BRD 142 ATOM 61 N GLU 9 20.562 -14.211 -11.095 1.00 20.00 1BRD 143 ATOM 62 CA GLU 9 20.192 -13.808 -9.737 1.00 20.00 1BRD 144 ATOM 63 C GLU 9 19.567 -14.935 -8.932 1.00 20.00 1BRD 145 ATOM 64 O GLU 9 19.815 -15.104 -7.724 1.00 20.00 1BRD 146 ATOM 65 CB GLU 9 19.248 -12.591 -9.820 1.00 99.00 1 1BRD 147 ATOM 66 CG GLU 9 19.902 -11.351 -10.387 1.00 99.00 1 1BRD 148 ATOM 67 CD GLU 9 19.243 -10.169 -10.980 1.00 99.00 1 1BRD 149 ATOM 68 OE1 GLU 9 18.323 -10.191 -11.782 1.00 99.00 1 1BRD 150 ATOM 69 OE2 GLU 9 19.760 -9.089 -10.597 1.00 99.00 1 1BRD 151 ATOM 70 N TRP 10 18.764 -15.737 -9.597 1.00 20.00 1BRD 152 ATOM 71 CA TRP 10 18.034 -16.884 -9.090 1.00 20.00 1BRD 153 ATOM 72 C TRP 10 18.843 -17.908 -8.318 1.00 20.00 1BRD 154 ATOM 73 O TRP 10 18.376 -18.310 -7.230 1.00 20.00 1BRD 155 . -
Computational Chemistry for Chemistry Educators Shawn C
Volume 1, Issue 1 Journal Of Computational Science Education Computational Chemistry for Chemistry Educators Shawn C. Sendlinger Clyde R. Metz North Carolina Central University College of Charleston Department of Chemistry Department of Chemistry and Biochemistry 1801 Fayetteville Street, 66 George Street, Durham, NC 27707 Charleston, SC 29424 919-530-6297 843-953-8097 [email protected] [email protected] ABSTRACT 1. INTRODUCTION In this paper we describe an ongoing project where the goal is to The majority of today’s students are technologically savvy and are develop competence and confidence among chemistry faculty so often more comfortable using computers than the faculty who they are able to utilize computational chemistry as an effective teach them. In order to harness the student’s interest in teaching tool. Advances in hardware and software have made technology and begin to use it as an educational tool, most faculty research-grade tools readily available to the academic community. members require some level of instruction and training. Because Training is required so that faculty can take full advantage of this chemistry research increasingly utilizes computation as an technology, begin to transform the educational landscape, and important tool, our approach to chemistry education should reflect attract more students to the study of science. this. The ability of computer technology to visualize and manipulate objects on an atomic scale can be a powerful tool to increase both student interest in chemistry as well as their level of Categories and Subject Descriptors understanding. Computational Chemistry for Chemistry Educators (CCCE) is a project that seeks to provide faculty the J.2 [Physical Sciences and Engineering]: Chemistry necessary knowledge, experience, and technology access so that they can begin to design and incorporate computational approaches in the courses they teach. -
Reactive Molecular Dynamics Study of the Thermal Decomposition of Phenolic Resins
Article Reactive Molecular Dynamics Study of the Thermal Decomposition of Phenolic Resins Marcus Purse 1, Grace Edmund 1, Stephen Hall 1, Brendan Howlin 1,* , Ian Hamerton 2 and Stephen Till 3 1 Department of Chemistry, Faculty of Engineering and Physical Sciences, University of Surrey, Guilford, Surrey GU2 7XH, UK; [email protected] (M.P.); [email protected] (G.E.); [email protected] (S.H.) 2 Bristol Composites Institute (ACCIS), Department of Aerospace Engineering, School of Civil, Aerospace, and Mechanical Engineering, University of Bristol, Bristol BS8 1TR, UK; [email protected] 3 Defence Science and Technology Laboratory, Porton Down, Salisbury SP4 0JQ, UK; [email protected] * Correspondence: [email protected]; Tel.: +44-1483-686-248 Received: 6 March 2019; Accepted: 23 March 2019; Published: 28 March 2019 Abstract: The thermal decomposition of polyphenolic resins was studied by reactive molecular dynamics (RMD) simulation at elevated temperatures. Atomistic models of the polyphenolic resins to be used in the RMD were constructed using an automatic method which calls routines from the software package Materials Studio. In order to validate the models, simulated densities and heat capacities were compared with experimental values. The most suitable combination of force field and thermostat for this system was the Forcite force field with the Nosé–Hoover thermostat, which gave values of heat capacity closest to those of the experimental values. Simulated densities approached a final density of 1.05–1.08 g/cm3 which compared favorably with the experimental values of 1.16–1.21 g/cm3 for phenol-formaldehyde resins.