DOCSLIB.ORG

Manual-2018.Pdf

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

GROMACS Groningen Machine for Chemical Simulations Reference Manual Version 2018 GROMACS Reference Manual Version 2018 Contributions from Emile Apol, Rossen Apostolov, Herman J.C. Berendsen, Aldert van Buuren, Pär Bjelkmar, Rudi van Drunen, Anton Feenstra, Sebastian Fritsch, Gerrit Groenhof, Christoph Junghans, Jochen Hub, Peter Kasson, Carsten Kutzner, Brad Lambeth, Per Larsson, Justin A. Lemkul, Viveca Lindahl, Magnus Lundborg, Erik Marklund, Pieter Meulenhoff, Teemu Murtola, Szilárd Páll, Sander Pronk, Roland Schulz, Michael Shirts, Alfons Sijbers, Peter Tieleman, Christian Wennberg and Maarten Wolf. Mark Abraham, Berk Hess, David van der Spoel, and Erik Lindahl. c 1991–2000: Department of Biophysical Chemistry, University of Groningen. Nijenborgh 4, 9747 AG Groningen, The Netherlands. c 2001–2018: The GROMACS development teams at the Royal Institute of Technology and Uppsala University, Sweden. More information can be found on our website: www.gromacs.org. iv Preface & Disclaimer This manual is not complete and has no pretention to be so due to lack of time of the contributors – our first priority is to improve the software. It is worked on continuously, which in some cases might mean the information is not entirely correct. Comments on form and content are welcome, please send them to one of the mailing lists (see www.gromacs.org), or open an issue at redmine.gromacs.org. Corrections can also be made in the GROMACS git source repository and uploaded to gerrit.gromacs.org. We release an updated version of the manual whenever we release a new version of the software, so in general it is a good idea to use a manual with the same major and minor release number as your GROMACS installation. On-line Resources You can find more documentation and other material at our homepage www.gromacs.org. Among other things there is an on-line reference, several GROMACS mailing lists with archives and contributed topologies/force fields. Citation information When citing this document in any scientific publication please refer to it as: M.J. Abraham, D. van der Spoel, E. Lindahl, B. Hess, and the GROMACS development team, GROMACS User Manual version 2018, www.gromacs.org (2018) However, we prefer that you cite (some of) the GROMACS papers [1,2,3,4,5,6,7,8] when you publish your results. Any future development depends on academic research grants, since the package is distributed as free software! GROMACS is Free Software The entire GROMACS package is available under the GNU Lesser General Public License (LGPL), version 2.1. This means it’s free as in free speech, not just that you can use it without pay- ing us money. You can redistribute GROMACS and/or modify it under the terms of the LGPL as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. For details, check the COPYING file in the source code or consult http://www.gnu.org/licenses/old-licenses/lgpl-2.1.html. The GROMACS source code and and selected set of binary packages are available on our home- page, www.gromacs.org. Have fun. Contents 1 Introduction1 1.1 Computational Chemistry and Molecular Modeling................1 1.2 Molecular Dynamics Simulations..........................2 1.3 Energy Minimization and Search Methods.....................5 2 Definitions and Units7 2.1 Notation.......................................7 2.2 MD units......................................7 2.3 Reduced units....................................9 2.4 Mixed or Double precision............................. 10 3 Algorithms 11 3.1 Introduction..................................... 11 3.2 Periodic boundary conditions............................ 11 3.2.1 Some useful box types........................... 13 3.2.2 Cut-off restrictions............................. 14 3.3 The group concept................................. 14 3.4 Molecular Dynamics................................ 15 3.4.1 Initial conditions.............................. 17 3.4.2 Neighbor searching............................. 18 3.4.3 Compute forces............................... 24 3.4.4 The leap-frog integrator.......................... 26 3.4.5 The velocity Verlet integrator....................... 26 3.4.6 Understanding reversible integrators: The Trotter decomposition..... 27 3.4.7 Multiple time stepping........................... 29 3.4.8 Temperature coupling........................... 29 vi Contents 3.4.9 Pressure coupling.............................. 35 3.4.10 The complete update algorithm...................... 42 3.4.11 Output step................................. 42 3.5 Shell molecular dynamics.............................. 44 3.5.1 Optimization of the shell positions..................... 44 3.6 Constraint algorithms................................ 45 3.6.1 SHAKE................................... 45 3.6.2 LINCS................................... 46 3.7 Simulated Annealing................................ 48 3.8 Stochastic Dynamics................................ 49 3.9 Brownian Dynamics................................ 50 3.10 Energy Minimization................................ 50 3.10.1 Steepest Descent.............................. 50 3.10.2 Conjugate Gradient............................. 51 3.10.3 L-BFGS................................... 51 3.11 Normal-Mode Analysis............................... 51 3.12 Free energy calculations.............................. 52 3.12.1 Slow-growth methods........................... 52 3.12.2 Thermodynamic integration........................ 54 3.13 Replica exchange.................................. 55 3.14 Essential Dynamics sampling............................ 56 3.15 Expanded Ensemble................................. 57 3.16 Parallelization.................................... 57 3.17 Domain decomposition............................... 57 3.17.1 Coordinate and force communication................... 58 3.17.2 Dynamic load balancing.......................... 58 3.17.3 Constraints in parallel........................... 60 3.17.4 Interaction ranges.............................. 60 3.17.5 Multiple-Program, Multiple-Data PME parallelization.......... 61 3.17.6 Domain decomposition flow chart..................... 63 3.18 Implicit solvation.................................. 63 4 Interaction function and force fields 67 4.1 Non-bonded interactions.............................. 67 Contents vii 4.1.1 The Lennard-Jones interaction....................... 68 4.1.2 Buckingham potential........................... 69 4.1.3 Coulomb interaction............................ 70 4.1.4 Coulomb interaction with reaction field.................. 70 4.1.5 Modified non-bonded interactions..................... 71 4.1.6 Modified short-range interactions with Ewald summation......... 73 4.2 Bonded interactions................................. 73 4.2.1 Bond stretching............................... 74 4.2.2 Morse potential bond stretching...................... 75 4.2.3 Cubic bond stretching potential...................... 75 4.2.4 FENE bond stretching potential...................... 76 4.2.5 Harmonic angle potential.......................... 76 4.2.6 Cosine based angle potential........................ 77 4.2.7 Restricted bending potential........................ 78 4.2.8 Urey-Bradley potential........................... 79 4.2.9 Bond-Bond cross term........................... 79 4.2.10 Bond-Angle cross term........................... 79 4.2.11 Quartic angle potential........................... 79 4.2.12 Improper dihedrals............................. 80 4.2.13 Proper dihedrals.............................. 81 4.2.14 Tabulated bonded interaction functions.................. 84 4.3 Restraints...................................... 86 4.3.1 Position restraints.............................. 86 4.3.2 Flat-bottomed position restraints...................... 87 4.3.3 Angle restraints............................... 88 4.3.4 Dihedral restraints............................. 88 4.3.5 Distance restraints............................. 89 4.3.6 Orientation restraints............................ 92 4.4 Polarization..................................... 96 4.4.1 Simple polarization............................. 96 4.4.2 Anharmonic polarization.......................... 96 4.4.3 Water polarization............................. 97 4.4.4 Thole polarization............................. 97 4.5 Free energy interactions............................... 97 viii Contents 4.5.1 Soft-core interactions............................ 100 4.6 Methods....................................... 102 4.6.1 Exclusions and 1-4 Interactions....................... 102 4.6.2 Charge Groups............................... 103 4.6.3 Treatment of Cut-offs in the group scheme................ 103 4.7 Virtual interaction sites............................... 104 4.8 Long Range Electrostatics............................. 107 4.8.1 Ewald summation.............................. 107 4.8.2 PME.................................... 108 4.8.3 P3M-AD.................................. 109 4.8.4 Optimizing Fourier transforms and PME calculations........... 110 4.9 Long Range Van der Waals interactions...................... 110 4.9.1 Dispersion correction............................ 110 4.9.2 Lennard-Jones PME............................ 112 4.10 Force field...................................... 115 4.10.1 GROMOS-96................................ 115 4.10.2 OPLS/AA.................................. 116 4.10.3 AMBER.................................. 116 4.10.4 CHARMM................................. 117 4.10.5 Coarse-grained force fields........................
Recommended publications
  • Supporting Information

    Supporting Information

    Electronic Supplementary Material (ESI) for RSC Advances. This journal is © The Royal Society of Chemistry 2020 Supporting Information How to Select Ionic Liquids as Extracting Agent Systematically? Special Case Study for Extractive Denitrification Process Shurong Gaoa,b,c,*, Jiaxin Jina,b, Masroor Abroc, Ruozhen Songc, Miao Hed, Xiaochun Chenc,* a State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China b Research Center of Engineering Thermophysics, North China Electric Power University, Beijing, 102206, China c Beijing Key Laboratory of Membrane Science and Technology & College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, PR China d Office of Laboratory Safety Administration, Beijing University of Technology, Beijing 100124, China * Corresponding author, Tel./Fax: +86-10-6443-3570, E-mail: [email protected], [email protected] 1 COSMO-RS Computation COSMOtherm allows for simple and efficient processing of large numbers of compounds, i.e., a database of molecular COSMO files; e.g. the COSMObase database. COSMObase is a database of molecular COSMO files available from COSMOlogic GmbH & Co KG. Currently COSMObase consists of over 2000 compounds including a large number of industrial solvents plus a wide variety of common organic compounds. All compounds in COSMObase are indexed by their Chemical Abstracts / Registry Number (CAS/RN), by a trivial name and additionally by their sum formula and molecular weight, allowing a simple identification of the compounds. We obtained the anions and cations of different ILs and the molecular structure of typical N-compounds directly from the COSMObase database in this manuscript.
  • Open Babel Documentation Release 2.3.1

    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 .................................................
  • GROMACS: Fast, Flexible, and Free

    GROMACS: Fast, Flexible, and Free

    GROMACS: Fast, Flexible, and Free DAVID VAN DER SPOEL,1 ERIK LINDAHL,2 BERK HESS,3 GERRIT GROENHOF,4 ALAN E. MARK,4 HERMAN J. C. BERENDSEN4 1Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, Box 596, S-75124 Uppsala, Sweden 2Stockholm Bioinformatics Center, SCFAB, Stockholm University, SE-10691 Stockholm, Sweden 3Max-Planck Institut fu¨r Polymerforschung, Ackermannweg 10, D-55128 Mainz, Germany 4Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, NL-9747 AG Groningen, The Netherlands Received 12 February 2005; Accepted 18 March 2005 DOI 10.1002/jcc.20291 Published online in Wiley InterScience (www.interscience.wiley.com). Abstract: This article describes the software suite GROMACS (Groningen MAchine for Chemical Simulation) that was developed at the University of Groningen, The Netherlands, in the early 1990s. The software, written in ANSI C, originates from a parallel hardware project, and is well suited for parallelization on processor clusters. By careful optimization of neighbor searching and of inner loop performance, GROMACS is a very fast program for molecular dynamics simulation. It does not have a force field of its own, but is compatible with GROMOS, OPLS, AMBER, and ENCAD force fields. In addition, it can handle polarizable shell models and flexible constraints. The program is versatile, as force routines can be added by the user, tabulated functions can be specified, and analyses can be easily customized. Nonequilibrium dynamics and free energy determinations are incorporated. Interfaces with popular quantum-chemical packages (MOPAC, GAMES-UK, GAUSSIAN) are provided to perform mixed MM/QM simula- tions. The package includes about 100 utility and analysis programs.
  • Neural Networks Based Variationally Enhanced Sampling

    Neural Networks Based Variationally Enhanced Sampling

    NEURAL NETWORKS BASED VARIATIONALLY ENHANCED SAMPLING A PREPRINT Luigi Bonati Department of Physics, ETH Zurich, 8092 Zurich, Switzerland and Institute of Computational Sciences, Università della Svizzera italiana, via G. Buffi 13, 6900 Lugano, Switzerland Yue-Yu Zhang Department of Chemistry and Applied Biosciences, ETH Zurich, 8092 Zurich, Switzerland and Institute of Computational Sciences, Università della Svizzera italiana, via G. Buffi 13, 6900 Lugano, Switzerland Michele Parrinello Department of Chemistry and Applied Biosciences, ETH Zurich, 8092 Zurich, Switzerland, Institute of Computational Sciences, Università della Svizzera italiana, via G. Buffi 13, 6900 Lugano, Switzerland, and Italian Institute of Technology, Via Morego 30, 16163 Genova, Italy September 25, 2019 ABSTRACT Sampling complex free energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number of key collective variables and to introduce a bias potential that is able to favor their fluctuations in order to accelerate sampling. Here we propose to use machine learning techniques in conjunction with the recent variationally enhanced sampling method [Valsson and Parrinello, Phys. Rev. Lett. 2014] in order to determine such potential. This is achieved by expressing the bias as a neural network. The parameters are determined in a variational learning scheme aimed at minimizing an appropriate functional. This required the development of a new and more efficient minimization technique. The expressivity of neural networks allows representing rapidly varying free energy surfaces, removes boundary effects artifacts and allows several collective variables to be handled.
  • Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

    Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

    processes Review Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development Outi M. H. Salo-Ahen 1,2,* , Ida Alanko 1,2, Rajendra Bhadane 1,2 , Alexandre M. J. J. Bonvin 3,* , Rodrigo Vargas Honorato 3, Shakhawath Hossain 4 , André H. Juffer 5 , Aleksei Kabedev 4, Maija Lahtela-Kakkonen 6, Anders Støttrup Larsen 7, Eveline Lescrinier 8 , Parthiban Marimuthu 1,2 , Muhammad Usman Mirza 8 , Ghulam Mustafa 9, Ariane Nunes-Alves 10,11,* , Tatu Pantsar 6,12, Atefeh Saadabadi 1,2 , Kalaimathy Singaravelu 13 and Michiel Vanmeert 8 1 Pharmaceutical Sciences Laboratory (Pharmacy), Åbo Akademi University, Tykistökatu 6 A, Biocity, FI-20520 Turku, Finland; ida.alanko@abo.fi (I.A.); rajendra.bhadane@abo.fi (R.B.); parthiban.marimuthu@abo.fi (P.M.); atefeh.saadabadi@abo.fi (A.S.) 2 Structural Bioinformatics Laboratory (Biochemistry), Åbo Akademi University, Tykistökatu 6 A, Biocity, FI-20520 Turku, Finland 3 Faculty of Science-Chemistry, Bijvoet Center for Biomolecular Research, Utrecht University, 3584 CH Utrecht, The Netherlands; [email protected] 4 Swedish Drug Delivery Forum (SDDF), Department of Pharmacy, Uppsala Biomedical Center, Uppsala University, 751 23 Uppsala, Sweden; [email protected] (S.H.); [email protected] (A.K.) 5 Biocenter Oulu & Faculty of Biochemistry and Molecular Medicine, University of Oulu, Aapistie 7 A, FI-90014 Oulu, Finland; andre.juffer@oulu.fi 6 School of Pharmacy, University of Eastern Finland, FI-70210 Kuopio, Finland; maija.lahtela-kakkonen@uef.fi (M.L.-K.); tatu.pantsar@uef.fi
  • Cygwin User's Guide

    Cygwin User's Guide

    Cygwin User’s Guide Cygwin User’s Guide ii Copyright © Cygwin authors Permission is granted to make and distribute verbatim copies of this documentation provided the copyright notice and this per- mission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this documentation under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this documentation into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the Free Software Foundation. Cygwin User’s Guide iii Contents 1 Cygwin Overview 1 1.1 What is it? . .1 1.2 Quick Start Guide for those more experienced with Windows . .1 1.3 Quick Start Guide for those more experienced with UNIX . .1 1.4 Are the Cygwin tools free software? . .2 1.5 A brief history of the Cygwin project . .2 1.6 Highlights of Cygwin Functionality . .3 1.6.1 Introduction . .3 1.6.2 Permissions and Security . .3 1.6.3 File Access . .3 1.6.4 Text Mode vs. Binary Mode . .4 1.6.5 ANSI C Library . .4 1.6.6 Process Creation . .5 1.6.6.1 Problems with process creation . .5 1.6.7 Signals . .6 1.6.8 Sockets . .6 1.6.9 Select . .7 1.7 What’s new and what changed in Cygwin . .7 1.7.1 What’s new and what changed in 3.2 .
  • Ee9e60701e814784783a672a9

    Ee9e60701e814784783a672a9

    International Journal of Technology (2017) 4: 611‐621 ISSN 2086‐9614 © IJTech 2017 A PRELIMINARY STUDY ON SHIFTING FROM VIRTUAL MACHINE TO DOCKER CONTAINER FOR INSILICO DRUG DISCOVERY IN THE CLOUD Agung Putra Pasaribu1, Muhammad Fajar Siddiq1, Muhammad Irfan Fadhila1, Muhammad H. Hilman1, Arry Yanuar2, Heru Suhartanto1* 1Faculty of Computer Science, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia 2Faculty of Pharmacy, Universitas Indonesia, Kampus UI Depok, Depok 16424, Indonesia (Received: January 2017 / Revised: April 2017 / Accepted: June 2017) ABSTRACT The rapid growth of information technology and internet access has moved many offline activities online. Cloud computing is an easy and inexpensive solution, as supported by virtualization servers that allow easier access to personal computing resources. Unfortunately, current virtualization technology has some major disadvantages that can lead to suboptimal server performance. As a result, some companies have begun to move from virtual machines to containers. While containers are not new technology, their use has increased recently due to the Docker container platform product. Docker’s features can provide easier solutions. In this work, insilico drug discovery applications from molecular modelling to virtual screening were tested to run in Docker. The results are very promising, as Docker beat the virtual machine in most tests and reduced the performance gap that exists when using a virtual machine (VirtualBox). The virtual machine placed third in test performance, after the host itself and Docker. Keywords: Cloud computing; Docker container; Molecular modeling; Virtual screening 1. INTRODUCTION In recent years, cloud computing has entered the realm of information technology (IT) and has been widely used by the enterprise in support of business activities (Foundation, 2016).
  • Spoken Tutorial Project, IIT Bombay Brochure for Chemistry Department

    Spoken Tutorial Project, IIT Bombay Brochure for Chemistry Department

    Spoken Tutorial Project, IIT Bombay Brochure for Chemistry Department Name of FOSS Applications Employability GChemPaint GChemPaint is an editor for 2Dchem- GChemPaint is currently being developed ical structures with a multiple docu- as part of The Chemistry Development ment interface. Kit, and a Standard Widget Tool kit- based GChemPaint application is being developed, as part of Bioclipse. Jmol Jmol applet is used to explore the Jmol is a free, open source molecule viewer structure of molecules. Jmol applet is for students, educators, and researchers used to depict X-ray structures in chemistry and biochemistry. It is cross- platform, running on Windows, Mac OS X, and Linux/Unix systems. For PG Students LaTeX Document markup language and Value addition to academic Skills set. preparation system for Tex typesetting Essential for International paper presentation and scientific journals. For PG student for their project work Scilab Scientific Computation package for Value addition in technical problem numerical computations solving via use of computational methods for engineering problems, Applicable in Chemical, ECE, Electrical, Electronics, Civil, Mechanical, Mathematics etc. For PG student who are taking Physical Chemistry Avogadro Avogadro is a free and open source, Research and Development in Chemistry, advanced molecule editor and Pharmacist and University lecturers. visualizer designed for cross-platform use in computational chemistry, molecular modeling, material science, bioinformatics, etc. Spoken Tutorial Project, IIT Bombay Brochure for Commerce and Commerce IT Name of FOSS Applications / Employability LibreOffice – Writer, Calc, Writing letters, documents, creating spreadsheets, tables, Impress making presentations, desktop publishing LibreOffice – Base, Draw, Managing databases, Drawing, doing simple Mathematical Math operations For Commerce IT Students Drupal Drupal is a free and open source content management system (CMS).
  • The Focus - Issue 36

    The Focus - Issue 36

    Contents The Focus - Issue 36 A Publication for ANSYS Users Contents Feature Articles ● Linux & ANSYS: Lessons Learned ● Backup Tool ● Design Modeler FAQ On the Web ● APDL Customization course notes now available for purchase ● ANSYS and MathCAD ● ANSYS Acquires Century Dynamics Resources ● PADT Support: How can we help? ● Upcoming Training at PADT ● About The Focus ❍ The Focus Library ❍ Contributor Information ❍ Subscribe / Unsubscribe ❍ Legal Disclaimer http://www.padtinc.com/epubs/focus/common/contents.asp [3/28/2005 9:06:12 AM] Linux & ANSYS: Lessons Learned The Focus - Issue 36 A Publication for ANSYS Users Linux & ANSYS: Lessons Learned by Eric Miller, PADT Every couple of years, the computing picture for analysts gets turned upside down. For a long time now the industry has been moving from Unix workstations to Windows/Intel desktop machines. The wintel price/performance has been fantastic, the IT guys are happier, and all of that productivity software that you spend so much time with runs in the same spot. We have been happy with a stable and known environment. However, accepting the fact that unless you work for a big company that can buy some Unix servers, you just don’t have an easy way to get some extra horsepower other then getting a new box. Then along comes this Finnish guy that may or may not have been named after Lucy’s little brother. With not much of a life and a very large brain, he popped out the majority of a complete and free version of Unix that anyone can use, breaking the stranglehold of (expensive) proprietary Unix OS’s that ran on (expensive) proprietary hardware.
  • Starting SCF Calculations by Superposition of Atomic Densities

    Starting SCF Calculations by Superposition of Atomic Densities

    Starting SCF Calculations by Superposition of Atomic Densities J. H. VAN LENTHE,1 R. ZWAANS,1 H. J. J. VAN DAM,2 M. F. GUEST2 1Theoretical Chemistry Group (Associated with the Department of Organic Chemistry and Catalysis), Debye Institute, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands 2CCLRC Daresbury Laboratory, Daresbury WA4 4AD, United Kingdom Received 5 July 2005; Accepted 20 December 2005 DOI 10.1002/jcc.20393 Published online in Wiley InterScience (www.interscience.wiley.com). Abstract: We describe the procedure to start an SCF calculation of the general type from a sum of atomic electron densities, as implemented in GAMESS-UK. Although the procedure is well known for closed-shell calculations and was already suggested when the Direct SCF procedure was proposed, the general procedure is less obvious. For instance, there is no need to converge the corresponding closed-shell Hartree–Fock calculation when dealing with an open-shell species. We describe the various choices and illustrate them with test calculations, showing that the procedure is easier, and on average better, than starting from a converged minimal basis calculation and much better than using a bare nucleus Hamiltonian. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 926–932, 2006 Key words: SCF calculations; atomic densities Introduction hrstuhl fur Theoretische Chemie, University of Kahrlsruhe, Tur- bomole; http://www.chem-bio.uni-karlsruhe.de/TheoChem/turbo- Any quantum chemical calculation requires properly defined one- mole/),12 GAMESS(US) (Gordon Research Group, GAMESS, electron orbitals. These orbitals are in general determined through http://www.msg.ameslab.gov/GAMESS/GAMESS.html, 2005),13 an iterative Hartree–Fock (HF) or Density Functional (DFT) pro- Spartan (Wavefunction Inc., SPARTAN: http://www.wavefun.
  • Structural Ensembles of Intrinsically Disordered Proteins Depend Strongly on Force Field: a Comparison to Experiment Sarah Rauscher,*,† Vytautas Gapsys,† Michal J

    Structural Ensembles of Intrinsically Disordered Proteins Depend Strongly on Force Field: a Comparison to Experiment Sarah Rauscher,*,† Vytautas Gapsys,† Michal J

    Article pubs.acs.org/JCTC Structural Ensembles of Intrinsically Disordered Proteins Depend Strongly on Force Field: A Comparison to Experiment Sarah Rauscher,*,† Vytautas Gapsys,† Michal J. Gajda,‡ Markus Zweckstetter,‡,§,∥ Bert L. de Groot,† and Helmut Grubmüller† † Department of Theoretical and Computational Biophysics, Max Planck Institute for Biophysical Chemistry, Göttingen 37077, Germany ‡ Department of NMR-based Structural Biology, Max Planck Institute for Biophysical Chemistry, Göttingen 37077, Germany § German Center for Neurodegenerative Diseases (DZNE), Göttingen 37077, Germany ∥ Center for Nanoscale Microscopy and Molecular Physiology of the Brain (CNMPB), University Medical Center, Göttingen 37073, Germany *S Supporting Information ABSTRACT: Intrinsically disordered proteins (IDPs) are notoriously challenging to study both experimentally and computationally. The structure of IDPs cannot be described by a single conformation but must instead be described as an ensemble of interconverting conformations. Atomistic simu- lations are increasingly used to obtain such IDP conformational ensembles. Here, we have compared the IDP ensembles generated by eight all-atom empirical force fields against primary small-angle X-ray scattering (SAXS) and NMR data. Ensembles obtained with different force fields exhibit marked differences in chain dimensions, hydrogen bonding, and secondary structure content. These differences are unexpect- edly large: changing the force field is found to have a stronger effect on secondary structure content than changing the entire peptide sequence. The CHARMM 22* ensemble performs best in this force field comparison: it has the lowest error in chemical shifts and J-couplings and agrees well with the SAXS data. A high population of left-handed α-helix is present in the CHARMM 36 ensemble, which is inconsistent with measured scalar couplings.
  • Parameterizing a Novel Residue

    Parameterizing a Novel Residue

    University of Illinois at Urbana-Champaign Luthey-Schulten Group, Department of Chemistry Theoretical and Computational Biophysics Group Computational Biophysics Workshop Parameterizing a Novel Residue Rommie Amaro Brijeet Dhaliwal Zaida Luthey-Schulten Current Editors: Christopher Mayne Po-Chao Wen February 2012 CONTENTS 2 Contents 1 Biological Background and Chemical Mechanism 4 2 HisH System Setup 7 3 Testing out your new residue 9 4 The CHARMM Force Field 12 5 Developing Topology and Parameter Files 13 5.1 An Introduction to a CHARMM Topology File . 13 5.2 An Introduction to a CHARMM Parameter File . 16 5.3 Assigning Initial Values for Unknown Parameters . 18 5.4 A Closer Look at Dihedral Parameters . 18 6 Parameter generation using SPARTAN (Optional) 20 7 Minimization with new parameters 32 CONTENTS 3 Introduction Molecular dynamics (MD) simulations are a powerful scientific tool used to study a wide variety of systems in atomic detail. From a standard protein simulation, to the use of steered molecular dynamics (SMD), to modelling DNA-protein interactions, there are many useful applications. With the advent of massively parallel simulation programs such as NAMD2, the limits of computational anal- ysis are being pushed even further. Inevitably there comes a time in any molecular modelling scientist’s career when the need to simulate an entirely new molecule or ligand arises. The tech- nique of determining new force field parameters to describe these novel system components therefore becomes an invaluable skill. Determining the correct sys- tem parameters to use in conjunction with the chosen force field is only one important aspect of the process.