Benchmark Assessment of Molecular Geometries and Energies from Small Molecule Force Fields [Version 1; Peer Review: 2 Approved]
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BOSS, Version 4.6 Biochemical and Organic Simulation System User’S Manual for UNIX, Linux, and Windows
BOSS, Version 4.6 Biochemical and Organic Simulation System User’s Manual for UNIX, Linux, and Windows William L. Jorgensen Department of Chemistry, Yale University P. O. Box 208107 New Haven, Connecticut 06520-8107 Phone: (203) 432-6278 Fax: (203) 432-6299 Internet: [email protected] January 2004 Contents Page Introduction 4 1 Can’t Wait to Start – Use the x Scripts 6 2 Statistical Mechanics Simulation – Theory 6 3 Energy and Free Energy Evaluation 7 4 New Features 9 5 Operating Systems 14 6 Installation 14 7 Files 14 8 Command or bat File Input 16 9 Parameter File Input 18 10 Z-matrix File Input 26 10.1 Atom Input 29 10.2 Geometry Variations 29 10.3 Bond Length Variations 29 10.4 Additional Bonds 30 10.5 Harmonic Restraints 30 10.6 Bond Angle Variations 30 10.7 Additional Bond Angles 31 10.8 Dihedral Angle Variations 31 10.9 Additional Dihedral Angles 33 10.10 Domain Definitions 34 10.11 Conformational Searching 34 10.12 Sample Z-matrix 34 10.13 Z-matrix Input for Custom Solvents 35 11 PDB Input 37 12 Coordinate Input in Mind Format and Reaction Path Following 38 13 Pure Liquid Simulations 39 14 Cluster Simulations 39 2 15 Solventless and Molecular Mechanics Calculations 40 15.1 Continuum Simulations 40 15.2 Energy Minimizations 41 15.3 Dihedral Angle Driving 42 15.4 Potential Surface Scanning 42 15.5 Normal Coordinate Analysis 43 15.6 Conformational Searching 45 16 Available Solvent Boxes 48 17 Ewald Summation for Long-Range Electrostatics 50 18 Test Jobs 51 19 Output 56 19.1 Some Variable Definitions 58 20 Contents of the Distribution Files 59 21 Appendix – No. -
FORCE FIELDS and CRYSTAL STRUCTURE PREDICTION Contents
FORCE FIELDS AND CRYSTAL STRUCTURE PREDICTION Bouke P. van Eijck ([email protected]) Utrecht University (Retired) Department of Crystal and Structural Chemistry Padualaan 8, 3584 CH Utrecht, The Netherlands Originally written in 2003 Update blind tests 2017 Contents 1 Introduction 2 2 Lattice Energy 2 2.1 Polarcrystals .............................. 4 2.2 ConvergenceAcceleration . 5 2.3 EnergyMinimization .......................... 6 3 Temperature effects 8 3.1 LatticeVibrations............................ 8 4 Prediction of Crystal Structures 9 4.1 Stage1:generationofpossiblestructures . .... 9 4.2 Stage2:selectionoftherightstructure(s) . ..... 11 4.3 Blindtests................................ 14 4.4 Beyondempiricalforcefields. 15 4.5 Conclusions............................... 17 4.6 Update2017............................... 17 1 1 Introduction Everybody who looks at a crystal structure marvels how Nature finds a way to pack complex molecules into space-filling patterns. The question arises: can we understand such packings without doing experiments? This is a great challenge to theoretical chemistry. Most work in this direction uses the concept of a force field. This is just the po- tential energy of a collection of atoms as a function of their coordinates. In principle, this energy can be calculated by quantumchemical methods for a free molecule; even for an entire crystal computations are beginning to be feasible. But for nearly all work a parameterized functional form for the energy is necessary. An ab initio force field is derived from the abovementioned calculations on small model systems, which can hopefully be generalized to other related substances. This is a relatively new devel- opment, and most force fields are empirical: they have been developed to reproduce observed properties as well as possible. There exists a number of more or less time- honored force fields: MM3, CHARMM, AMBER, GROMOS, OPLS, DREIDING.. -
Molecular Dynamics Simulations: What Is the Effect of a Spin Probe
Department of Mathematics and Computer Science Institute of Bioinformatics Masters Thesis Molecular Dynamics Simulations: What is the E↵ect of a Spin Probe on the Drug Loading of a Nanocarrier? Marthe Solleder Matriculation number: 4449223 Supervisors: Dr. Marcus Weber, Zuse Institute Berlin Prof. Dr. Susanna R¨oblitz, Freie Universit¨at Berlin Submitted: March 14, 2016 Abstract In this study a nanoparticle that is used for drug delivery is investigated. The main components under investigation are a dendritic core-multishell nanoparticle and a drug that will be loaded into the carrier. The loaded drug is dexamethasone, a steroid structure, and will be complexed in two variations with the polymer: the first complex consists of the unaltered dexamethasone structure whereas the second comprises of dexamethasone with an attached spin probe. The underlying research for this study is the following: a spin probe is attached to the structure to perform an electron spin resonance (ESR) spectroscopy, carried out to determine whether the loading of the drug was successful and at which position inside the carrier it can be found. It is presumed that the spin probe might influence the drug’s behav- ior during loading and inside the carrier. This study is performed to investigate di↵erences in the behavior of the two systems. The method of molecular dynamics simulations is applied on the two complexes, as well as free energy calculations and estimation of binding affinity, to determine if the attached spin probe is a↵ecting the drug loading of the nanocarrier. Acknowledgment I would like to give my sincere gratitude to everyone who supported me during my master thesis. -
Macromodel Quick Start Guide
MacroModel Quick Start Guide MacroModel 9.9 Quick Start Guide Schrödinger Press MacroModel Quick Start Guide Copyright © 2012 Schrödinger, LLC. All rights reserved. While care has been taken in the preparation of this publication, Schrödinger assumes no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein. BioLuminate, Canvas, CombiGlide, ConfGen, Epik, Glide, Impact, Jaguar, Liaison, LigPrep, Maestro, Phase, Prime, PrimeX, QikProp, QikFit, QikSim, QSite, SiteMap, Strike, and WaterMap are trademarks of Schrödinger, LLC. Schrödinger and MacroModel are registered trademarks of Schrödinger, LLC. MCPRO is a trademark of William L. Jorgensen. DESMOND is a trademark of D. E. Shaw Research, LLC. Desmond is used with the permission of D. E. Shaw Research. All rights reserved. This publication may contain the trademarks of other companies. Schrödinger software includes software and libraries provided by third parties. For details of the copyrights, and terms and conditions associated with such included third party software, see the Legal Notices, or use your browser to open $SCHRODINGER/docs/html/third_party_legal.html (Linux OS) or %SCHRODINGER%\docs\html\third_party_legal.html (Windows OS). This publication may refer to other third party software not included in or with Schrödinger software ("such other third party software"), and provide links to third party Web sites ("linked sites"). References to such other third party software or linked sites do not constitute an endorsement by Schrödinger, LLC or its affiliates. Use of such other third party software and linked sites may be subject to third party license agreements and fees. Schrödinger, LLC and its affiliates have no responsibility or liability, directly or indirectly, for such other third party software and linked sites, or for damage resulting from the use thereof. -
GROMACS-CP2K Interface Tutorial (Introduction to QM/MM Simulations)
GROMACS-CP2K Interface Tutorial (Introduction to QM/MM simulations) Dmitry Morozov University of Jyväskylä, Finland [email protected] Practical: GROMACS + CP2K Part I 1. Lecture recap 2. Gromacs-CP2K interface for QM/MM MM QM 3. Setting up a QM/MM calculation 4. CP2K input and output 2 GROMACS-CP2K Interface Tutorial 22-23.04.2021 Lecture Recap: Forcefield (MM) - GROMACS § Force field description of MM region V(r1, r2, . rN) = Vbonded(r1, r2, . rN) + Vnon−bonded(r1, r2, . rN) MM 1 1 1 V k r r 2 k θ θ 2 k ξ ξ 2 k nϕ ϕ bonded = ∑ b( − 0) + ∑ θ( − 0) + ∑ ξ( − 0) + ∑ ϕ[1 + cos( − 0)] bonds 2 angles 2 torsions torsions 2 QM 1 1 1 V k r r 2 k θ θ 2 k ξ ξ 2 k nϕ ϕ bonded = ∑ b( − 0) + ∑ θ( − 0) + ∑ ξ( − 0) + ∑ ϕ[1 + cos( − 0)] bonds 2 angles 2 torsions torsions 2 (12) (6) Cij Cij qiqj V = 4ϵ − + non−bonded ∑ ij r12 r6 ∑ r LJ ( ij ij ) Coul. ij 3 GROMACS-CP2K Interface Tutorial 22-23.04.2021 Lecture Recap: Quickstep (QM) - CP2K QM region as CP2K input Guess density from Gaussian basis Construct KS matrix and energy functional MM Map basis onto Real Space multi-grids (Collocation and Interpolation) QM FFT to pass RS onto Reciprocal Space (G) Minimize energy to obtain new density matrix NO YES Energy, Forces and other Convergence? properties 4 GROMACS-CP2K Interface Tutorial 22-23.04.2021 Practical: GROMACS + CP2K Part I 1. Lecture recap 2. Gromacs-CP2K interface for QM/MM 3. -
CORINA Classic Program Manual
CORINA Classic Generation of Three-Dimensional Molecular Models Version 4.4.0 Program Description Molecular Networks GmbH September 2021 www.mn-am.com Molecular Networks GmbH Altamira LLC Neumeyerstraße 28 470 W Broad St, Unit #5007 90411 Nuremberg Columbus, OH 43215 Germany USA mn-am.com This document is copyright © 1998-2021 by Molecular Networks GmbH Computerchemie and Altamira LLC. All rights reserved. Except as permitted under the terms of the Software Licensing Agreement of Molecular Networks GmbH Computerchemie, no part of this publication may be reproduced or distributed in any form or by any means or stored in a database retrieval system without the prior written permission of Molecular Networks GmbH or Altamira LLC. The software described in this document is furnished under a license and may be used and copied only in accordance with the terms of such license. (Document version: 4.4.0-2021-09-30) Content Content 1 Introducing CORINA Classic 1 1.1 Objective of CORINA Classic 1 1.2 CORINA Classic in Brief 1 2 Release Notes 3 2.1 CORINA (Full Version) 3 2.2 CORINA_F (Restricted FlexX Interface Version) 25 3 Getting Started with CORINA Classic 26 4 Using CORINA Classic 29 4.1 Synopsis 29 4.2 Options 29 5 Use Cases of CORINA Classic 60 6 Supported File Formats and Interfaces 64 6.1 V2000 Structure Data File (SD) and Reaction Data File (RD) 64 6.2 V3000 Structure Data File (SD) and Reaction Data File (RD) 67 6.3 SMILES Linear Notation 67 6.4 InChI file format 69 6.5 SYBYL File Formats 70 6.6 Brookhaven Protein Data Bank Format (PDB) -
Energy Minimization
mac har olo P gy Zhang et al., Biochem Pharmacol (Los Angel) 2015, 4:4 : & O y r p t e s DOI: 10.4172/2167-0501.1000175 i n A m c e c h e c s Open Access o i s Biochemistry & Pharmacology: B ISSN: 2167-0501 Research Article Open Access The Hybrid Idea of (Energy Minimization) Optimization Methods Applied to Study PrionProtein Structures Focusing on the beta2-alpha2 Loop Jiapu Zhang1,2* 1Molecular Model Discovery Laboratory, Department of Chemistry and Biotechnology, Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn Campus, Hawthorn, Victoria 3122, Australia 2Graduate School of Sciences, Information Technology and Engineering and Centre of Informatics and Applied Optimization, Faculty of Science, The Federation University Australia, Mount Helen Campus, Mount Helen, Ballarat, Victoria 3353, Australia Abstract In molecular mechanics, current generation potential energy functions provide a reasonably good compromise between accuracy and effectiveness. This paper firstly reviewed several most commonly used classical potential energy functions and their optimization methods used for energy minimization. To minimize a potential energy function, about 95% efforts are spent on the Lennard-Jones potential of van der Waals interactions; we also give a detailed review on some effective computational optimization methods in the Cambridge Cluster Database to solve the problem of Lennard- Jones clusters. From the reviews, we found the hybrid idea of optimization methods is effective, necessary and efficient for solving the potential energy minimization problem and the Lennard-Jones clusters problem. An application to prion protein structures is then done by the hybrid idea. We focus on the β2-α2 loop of prion protein structures, and we found (i) the species that has the clearly and highly ordered β2-α2 loop usually owns a 310 -helix in this loop, (ii) a “π-circle” Y128–F175–Y218–Y163–F175–Y169– R164–Y128(–Y162) is around the β2-α2 loop. -
Trends in Atomistic Simulation Software Usage [1.3]
A LiveCoMS Perpetual Review Trends in atomistic simulation software usage [1.3] Leopold Talirz1,2,3*, Luca M. Ghiringhelli4, Berend Smit1,3 1Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingenierie Chimiques, Valais, École Polytechnique Fédérale de Lausanne, CH-1951 Sion, Switzerland; 2Theory and Simulation of Materials (THEOS), Faculté des Sciences et Techniques de l’Ingénieur, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; 3National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; 4The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society and Humboldt University, Berlin, Germany This LiveCoMS document is Abstract Driven by the unprecedented computational power available to scientific research, the maintained online on GitHub at https: use of computers in solid-state physics, chemistry and materials science has been on a continuous //github.com/ltalirz/ rise. This review focuses on the software used for the simulation of matter at the atomic scale. We livecoms-atomistic-software; provide a comprehensive overview of major codes in the field, and analyze how citations to these to provide feedback, suggestions, or help codes in the academic literature have evolved since 2010. An interactive version of the underlying improve it, please visit the data set is available at https://atomistic.software. GitHub repository and participate via the issue tracker. This version dated August *For correspondence: 30, 2021 [email protected] (LT) 1 Introduction Gaussian [2], were already released in the 1970s, followed Scientists today have unprecedented access to computa- by force-field codes, such as GROMOS [3], and periodic tional power. -
Chem3d 17.0 User Guide Chem3d 17.0
Chem3D 17.0 User Guide Chem3D 17.0 Table of Contents Recent Additions viii Chapter 1: About Chem3D 1 Additional computational engines 1 Serial numbers and technical support 3 About Chem3D Tutorials 3 Chapter 2: Chem3D Basics 5 Getting around 5 User interface preferences 9 Background settings 10 Sample files 10 Saving to Dropbox 10 Chapter 3: Basic Model Building 12 Default settings 12 Selecting a display mode 12 Using bond tools 13 Using the ChemDraw panel 15 Using other 2D drawing packages 15 Building from text 16 Adding fragments 18 Selecting atoms and bonds 18 Atom charges 21 Object position 23 Substructures 24 Refining models 27 Copying and printing 29 Finding structures online 32 Chapter 4: Displaying Models 35 © Copyright 1998-2017 PerkinElmer Informatics Inc., All rights reserved. ii Chem3D 17.0 Display modes 35 Atom and bond size 37 Displaying dot surfaces 38 Serial numbers 38 Displaying atoms 39 Atom symbols 40 Rotating models 41 Atom and bond properties 44 Showing hydrogen bonds 45 Hydrogens and lone pairs 46 Translating models 47 Scaling models 47 Aligning models 47 Applying color 49 Model Explorer 52 Measuring molecules 59 Comparing models by overlay 62 Molecular surfaces 63 Using stereo pairs 72 Stereo enhancement 72 Setting view focus 73 Chapter 5: Building Advanced Models 74 Dummy bonds and dummy atoms 74 Substructures 75 Bonding by proximity 78 Setting measurements 78 Atom and building types 81 Stereochemistry 85 © Copyright 1998-2017 PerkinElmer Informatics Inc., All rights reserved. iii Chem3D 17.0 Building with Cartesian -
Molecular Modeling: a Powerful Tool for Drug Design and Molecular Docking
GENERAL I ARTICLE Molecular Modeling: A Powerful Tool for Drug Design and Molecular Docking Rama Rao Nadendla Molecular modeling has become a valuable and essential tool to medicinal chemists in the drug design process. Molecular modeling describes the generation, manipula tion or representation of three-dimensional structures of molecules and associated physico-chemical properties. It involves a range of computerized techniques based on theoretical chemistry methods and experimental data to predict molecular and biological properties. Depending on Rama Rao Nadendla has the context and the rigor, the subject is often referred to as been working in the 'molecular graphics', 'molecular visualizations', 'computa KVSR College of tional chemistry', or 'computational quantum chemistry'. Pharmaceutical Sciences, The molecular modeling techniques are derived from the Vijaywada as a Professor concepts of molecular orbitals of Huckel, Mullikan and of pharmaceutical chemistry. He is involved 'classical mechanical programs' of Westheimer, Wiberg in the synthesis of new and Boyd. biodynamic agents. He also has authored three 1. Why Modeling and Molecular Modeling? books on pharmaceutical organic chemistry and medicinal chemistry. Modeling is a tool for doing chemistry. Models are central for understanding of chemistry. Molecular modeling allows us to do and teach chemistry better by providing better tools for investi gating, interpreting, explaining and discovering new phenom ena. Like experimental chemistry, it is a skill-demanding sci ence and must be learnt by doing and not just reading. Molecular modeling is easy to perform with currently available software, but the difficulty lies in getting the right model and proper interpretation. 2. Molecular Modeling Tools Keywords Molecular modeling, molecu The tools of the trade have gradually evolved from physical lar docking, drug design, com putational chemistry, molecu models (Dreiding, CPK, etc.) and calculators, including the use lar visulatization. -
“One Ring to Rule Them All”
OpenMM library “One ring to rule them all” https://simtk.org/home/openmm CHARMM * Abalone Presto * ACEMD NAMD * ADUN * Ascalaph Gromacs AMBER * COSMOS ??? HOOMD * Desmond * Culgi * ESPResSo Tinker * GROMOS * GULP * Hippo LAMMPS * Kalypso MD * LPMD * MacroModel * MDynaMix * MOLDY * Materials Studio * MOSCITO * ProtoMol The OpenM(olecular)M(echanics) API * RedMD * YASARA * ORAC https://simtk.org/home/openmm * XMD Goals of OpenMM l Complete library l provide what is most needed l easy to learn and use l Fast, general, extensible l optimize for speed l hide hardware specifics l support new hardware l add new force fields, integration methods etc. l Available APIs in C++, Fortran95, Python l Advantages l Object oriented l Modular l Extensible l Disadvantages l Programs written in other languages need to invoke it through a layer of C++ code Features • Electrostatics: cut-off, Ewald, PME • Implicit solvent • Integrators: Verlet, Langevin, Brownian, custom • Thermostat: Andersen • Barostat: MonteCarlo • Others: energy minimization, virtual sites 5 The OpenMM Architecture Public Interface OpenMM Public API Platform Independent Code OpenMM Implementation Layer Platform Abstraction Layer OpenMM Low Level API Computational Kernels CUDA/OpenCL/Brook/MPI etc. Public API Classes (1) l System l A collection of interaction particles l Defines the mass of each particle (needed for integration) l Specifies distance restraints l Contains a list of Force objects that define the interactions l OpenMMContext l Contains all state information for a particular simulation − Positions, velocities, other parmeters Public API Classes (2) l Force l Anything that affects the system's behavior: forces, barostats, thermostats, etc. l A Force may: − Apply forces to particles − Contribute to the potential energy − Define adjustable parameters − Modify positions, velocities, and parameters at the start of each time step l Existing Force subclasses − HarmonicBond, HarmonicAngle, PeriodicTorsion, RBTorsion, Nonbonded, GBSAOBC, AndersenThermostat, CMMotionRemover.. -
Chemistry Packages at CHPC
Chemistry Packages at CHPC Anita M. Orendt Center for High Performance Computing [email protected] Fall 2014 Purpose of Presentation • Identify the computational chemistry software and related tools currently available at CHPC • Present brief overview of these packages • Present how to access packages on CHPC • Information on usage of Gaussian09 http://www.chpc.utah.edu Ember Lonepeak /scratch/lonepeak/serial 141 nodes/1692 cores 16 nodes/256 cores Infiniband and GigE Large memory, GigE General 70 nodes/852 cores 12 GPU nodes (6 General) Kingspeak 199 nodes/4008 cores Ash cluster (417 nodes) Infiniband and GigE Switch General 44 nodes/832 cores Apex Arch Great Wall PFS Telluride cluster (93 nodes) /scratch/ibrix meteoXX/atmosXX nodes NFS Home NFS Administrative Directories & /scratch/kingspeak/serial Group Nodes Directories 10/1/14 http://www.chpc.utah.edu Slide 3 Brief Overview CHPC Resources • Computational Clusters – kingspeak, ember – general allocation and owner-guest on owner nodes – lonepeak – no allocation – ash and telluride – can run as smithp-guest and cheatham-guest, respectively • Home directory – NFS mounted on all clusters – /uufs/chpc.utah.edu/common/home/<uNID> – generally not backed up (there are exceptions) • Scratch systems – /scratch/ibrix/chpc_gen – kingspeak, ember, ash, telluride – 56TB – /scratch/kingspeak/serial – kingspeak, ember, ash – 175TB – /scratch/lonepeak/serial – lonepeak – 33 TB – /scratch/local on compute nodes • Applications – /uufs/chpc.utah.edu/sys/pkg (use for lonepeak, linux desktops if chpc