DICE: a Monte Carlo Code for Molecular Simulation Including the Configurational Bias Monte Carlo Method Henrique M
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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. -
Guidelines for the Analysis of Free Energy Calculations
Journal of Computer-Aided Molecular Design manuscript No. (will be inserted by the editor) Guidelines for the analysis of free energy calculations Pavel V. Klimovich, Michael R. Shirts, and David L. Mobley Received: date / Accepted: date Abstract Free energy calculations based on molecular dy- Python tool also handles output from multiple types of free namics (MD) simulations show considerable promise for ap- energy calculations, including expanded ensemble and Hamil- plications ranging from drug discovery to prediction of phys- tonian replica exchange, as well as standard fixed ensemble ical properties and structure-function studies. But these cal- calculations. We also survey a range of statistical and graph- culations are still difficult and tedious to analyze, and best ical ways of assessing the quality of the data and free energy practices for analysis are not well defined or propagated. Es- estimates, and provide prototypes of these in our tool. We sentially, each group analyzing these calculations needs to hope these tools and discussion will serve as a foundation decide how to conduct the analysis and, usually, develop its for more standardization of and agreement on best practices own analysis tools. Here, we review and recommend best for analysis of free energy calculations. practices for analysis yielding reliable free energies from molecular simulations. Additionally, we provide a Python Keywords hydration free energy · transfer free energy · tool, alchemical-analysis.py, freely available on free energy calculation · analysis tool · binding free energy · GitHub at https://github.com/choderalab/pymbar-examplesalchemical , that implements the analysis practices reviewed here for sev- eral reference simulation packages, which can be adapted to handle data from other packages. -
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. -
Solvation Free Energy of Protonated Lysine: Molecular Dynamics Study
Solvation Free Energy of Protonated Lysine: Molecular Dynamics Study S. P. Khanal, B. Poudel, R. P. Koirala and N. P. Adhikari Journal of Nepal Physical Society Volume 7, Issue 2, June 2021 ISSN: 2392-473X (Print), 2738-9537 (Online) Editors: Dr. Binod Adhikari Dr. Bhawani Joshi Dr. Manoj Kumar Yadav Dr. Krishna Rai Dr. Rajendra Prasad Adhikari Mr. Kiran Pudasainee JNPS, 7 (2), 69-75 (2021) DOI: https://doi.org/10.3126/jnphyssoc.v7i2.38625 Published by: Nepal Physical Society P.O. Box: 2934 Tri-Chandra Campus Kathmandu, Nepal Email: [email protected] JNPS 7 (2): 69-75 (2021) Research Article © Nepal Physical Society ISSN: 2392-473X (Print), 2738-9537 (Online) DOI: https://doi.org/10.3126/jnphyssoc.v7i2.38625 Solvation Free Energy of Protonated Lysine: Molecular Dynamics Study S. P. Khanal, B. Poudel, R. P. Koirala and N. P. Adhikari* Central Department of Physics, Tribhuvan University, Kathmandu, Nepal *Corresponding Email: [email protected]. Received: 16 April, 2021; Revised: 13 May, 2021; Accepted: 22 June, 2021 ABSTRACT In the present work, we have used an alchemical approach for calculating solvation free energy of protonated lysine in water from molecular dynamics simulations. These approaches use a non-physical pathway between two end states in order to compute free energy difference from the set of simulations. The solute is modeled using bonded and non-bonded interactions described by OPLS-AA potential, while four different water models: TIP3P, SPC, SPC/E and TIP4P are used. The free energy of solvation of protonated lysine in water has been estimated using thermodynamic integration, free energy perturbation, and Bennett acceptance ratio methods at 310 K temperature. -
Towards Accurate Free Energy Calculations in Ligand Protein-Binding Studies
Current Medicinal Chemistry, 2010, 17, 767-785 767 Towards Accurate Free Energy Calculations in Ligand Protein-Binding Studies Thomas Steinbrecher1 and Andreas Labahn*,2 1BioMaps Institute, Rutgers University, 610 Taylor Rd., 08854 Piscataway, NJ 2Institut für Physikalische Chemie, Universität Freiburg, Albertstr. 23a, 79104 Freiburg, Germany Abstract: Cells contain a multitude of different chemical reaction paths running simultaneously and quite independently next to each other. This amazing feat is enabled by molecular recognition, the ability of biomolecules to form stable and specific complexes with each other and with their substrates. A better understanding of this process, i.e. of the kinetics, structures and thermodynamic properties of biomolecule binding, would be invaluable in the study of biological systems. In addition, as the mode of action of many pharmaceuticals is based upon their inhibition or activation of biomolecule tar- gets, predictive models of small molecule receptor binding are very helpful tools in rational drug design. Since the goal here is normally to design a new compound with a high inhibition strength, one of the most important thermodynamic properties is the binding free energy G0. The prediction of binding constants has always been one of the major goals in the field of computational chemistry, be- cause the ability to reliably assess a hypothetical compound's binding properties without having to synthesize it first would save a tremendous amount of work. The different approaches to this question range from fast and simple empirical descriptor methods to elaborate simulation protocols aimed at putting the computation of free energies onto a solid foun- dation of statistical thermodynamics. -
Extending the Scope of Alchemical Perturbation Methods for Ligand Binding Free Energy Calculations
Extending the scope of alchemical perturbation methods for ligand binding free energy calculations Eric Fagerberg Supervisor: P¨arS¨oderhjelm 2016 1 Contents 1 Abstract 3 2 Introduction 3 2.1 Aim . 4 3 Theory 5 3.1 Statistical thermodynamics . 5 3.1.1 Canonical ensemble . 5 3.1.2 Helmholtz and Gibbs energies . 6 3.2 Molecular Dynamics . 7 3.3 Free Energy Perturbation . 8 3.3.1 Soft-core interactions . 10 4 Project 11 4.1 Previous work . 12 5 Methods 12 5.1 Simulation details . 12 5.2 System preparation . 13 5.3 Restraints . 13 5.4 System analysis . 14 6 Results and Discussion 14 6.1 Finding the optimal setup for low-variance simulations . 14 6.1.1 Restraining ten dihedrals in the ligand . 15 6.1.2 Positional restraint: restraint to Cα atoms in protein, non-hydrogen atoms in ligand . 20 6.1.3 Restraining all dihedrals in the ligand . 20 6.1.4 Full positional restraint: restraint to all non-hydrogen atoms in protein and ligand . 21 6.1.5 The major cause of fluctuations . 21 6.1.6 The 1-1-48 soft-core potential . 25 6.1.7 Evaluation of restraint . 26 6.2 The states of Hexa . 27 6.2.1 Changing the force field . 29 7 Conclusions 33 7.1 Future work . 33 7.2 Acknowledgements . 33 8 References for Theory section 36 9 Appendix 37 9.1 Evaluation of restraints . 37 2 1 Abstract Previously, a method for computing binding free energies between different poses of a ligand bound to a protein using alchemical perturbation was developed. -
Computing Free Energies with PLUMED 2.0
Computing free energies with PLUMED 2.0 Davide Branduardi Formerly at MPI Biophysics, Frankfurt a.M. Wednesday, October 16, 13 TyOutline of the talk Relevance of free energy computation Free-energy from histograms: issues Tackling the sampling problem Tackling the order-parameter problem 2 Wednesday, October 16, 13 TyRelevance of free energy calculations Free energy is a measure of probability A(s )= k T ln P (s ) 0 − B 0 Chemical find elusive structures Biophysics reactions and mechanisms! ∆A‡ ∆A Free Energy Free React Prod Molecular Phase s (RC) recognition transitions Drug binding 3 Wednesday, October 16, 13 TyRare event: troubles with ergodic hypothesis kBT ∆A‡/RT k(T )= e− ‡ 0 A hc 1 ∆ k T 2 B Free Energy Free React Prod RC Now I sampled all the two stop? states. Is it sufficient? property time Free energy requires statistics! 1 N1 property ∆A = ln time −β N2 [3] Eyring, Chem. Rev. 1935 vol. 17 pp 65 4 Wednesday, October 16, 13 TyA test on ATP-Mg2+ in water ATP4--Mg2+ is of fundamental importance in bioenergetics and regulation Free energy of α β γ to β γ α β γ β γ OW2 OW3 OW4 δ δ − OW1δ OW3 − δ − δ δ OW4 Mg − − − + + O1α δ OW2 O2α δ δ − − δ − δ − − δ O2α − δ − O1α [1] Liao, Sun, Chandler, Oster Eur. Biophys. J. 2004, vol. 33, p. 29 5 Wednesday, October 16, 13 TyErgodic hypothesis: am I visiting all the states? Classical MD, CHARMM27 force field, 915 TIP3P water molecules, GROMACS 4.5.5 code 6 Wednesday, October 16, 13 TyPossible solutions Replica exchange methods: multiple simulations at different temperatures or Hamiltonians Biased -
Article Titlle Iitmomleicullrr Sitoullrtmns Frmo Ynnroitics Rny Oeicarnitsos Lm Icmopullrtmnrl Rssrns Mf Iitmlm⪪Iticrl Rictiitln
Article Titlle iitmomleicullrr sitoullrtmns frmo ynnroitics rny oeicarnitsos lm icmopullrtmnrl rssrns mf iitmlm⪪iticrl rictiitln Article Tnpe vieriiteo Aullamrs *David J. Huggins and Adrian J. Mulholland are co-corresponding authors Driity J. Hul⪪⪪itns* University of Cambridge, TCM Group, Cavendish Laboratory, 19 J J Thomson Avenue, Cambridge CB3 0HE, United Kingdom Unilever Centre, Department of Chemistry, University of Cambridge, Lensfeld Road, Cambridge, UK CB2 1EW, United Kingdom [email protected] This author declares no confict of interest Paitlitp C. iit⪪⪪itn Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, United Kingdom [email protected] This author declares no confict of interest Mrric A. Däo⪪en Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, United Kingdom [email protected] This author declares no confict of interest Jmnrlarn W. Essex School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom [email protected] This author declares no confict of interest Srrra A. Hrrrits School of Physics and Astronomy and Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, LS2 9JT, United Kingdom [email protected] This author declares no confict of interest Riticarry H. Henicaorn Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, United Kingdom School of Chemistry, The University of Manchester, Oxford Road, M13 9PL, United Kingdom [email protected] This author declares no confict of interest Snor Karlity School of Chemistry, University of Southampton, Southampton SO17 1BJ, United Kingdom Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom [email protected] This author declares no confict of interest Anlmnitjr Kulzornitic Department of Chemistry, University College London, London WC1E 6BT, United Kingdom. -
Absolute Free Energy of Binding Calculations for Macrophage
Page 1 of 34 The Journal of Physical Chemistry 1 2 3 4 Absolute Free Energy of Binding Calculations for Macrophage 5 6 7 Migration Inhibitory Factor in Complex with a Drug-like Inhibitor 8 9 10 Yue Qian, Israel Cabeza de Vaca, Jonah Z. Vilseck† , Daniel J. Cole‡, Julian Tirado-Rives and 11 12 William L. Jorgensen* 13 14 15 Department of Chemistry, Yale University, New Haven, Connecticut 06520-8107, United States 16 17 18 ABSTRACT: Calculation of the absolute free energy of binding (ΔGbind) for a complex in 19 20 solution is challenging owing to the need for adequate configurational sampling and an accurate 21 22 energetic description, typically with a force field (FF). In this study, Monte Carlo (MC) 23 24 simulations with improved side-chain and backbone sampling are used to assess ΔGbind for the 25 26 complex of a drug-like inhibitor (MIF180) with the protein macrophage migration inhibitory 27 28 factor (MIF) using free energy perturbation (FEP) calculations. For comparison, molecular 29 30 dynamics (MD) simulations were employed as an alternative sampling method for the same 31 32 system. With the OPLS-AA/M FF and CM5 atomic charges for the inhibitor, the ΔGbind results 33 34 from the MC/FEP and MD/FEP simulations, -8.80 ± 0.74 and -8.46 ± 0.85 kcal/mol, agree well 35 36 with each other and with the experimental value of -8.98 ± 0.28 kcal/mol. The convergence of 37 38 the results and analysis of the trajectories indicate that sufficient sampling was achieved for both 39 40 approaches. -
Absolute Binding Free Energy Calculations for Highly Flexible Protein MDM2 and Its Inhibitors
International Journal of Molecular Sciences Article Absolute Binding Free Energy Calculations for Highly Flexible Protein MDM2 and Its Inhibitors Nidhi Singh 1,2 and Wenjin Li 1,* 1 Institute for Advanced Study, Shenzhen University, Shenzhen 518060, China; [email protected] 2 College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China * Correspondence: [email protected]; Tel.: +86-755-2694-2336 Received: 6 June 2020; Accepted: 2 July 2020; Published: 4 July 2020 Abstract: Reliable prediction of binding affinities for ligand-receptor complex has been the primary goal of a structure-based drug design process. In this respect, alchemical methods are evolving as a popular choice to predict the binding affinities for biomolecular complexes. However, the highly flexible protein-ligand systems pose a challenge to the accuracy of binding free energy calculations mostly due to insufficient sampling. Herein, integrated computational protocol combining free energy perturbation based absolute binding free energy calculation with free energy landscape method was proposed for improved prediction of binding free energy for flexible protein-ligand complexes. The proposed method is applied to the dataset of various classes of p53-MDM2 (murine double minute 2) inhibitors. The absolute binding free energy calculations for MDMX (murine double minute X) resulted in a mean absolute error value of 0.816 kcal/mol while it is 3.08 kcal/mol for MDM2, a highly flexible protein compared to MDMX. With the integration of the free energy landscape method, the mean absolute error for MDM2 is improved to 1.95 kcal/mol. Keywords: binding free energy; free energy perturbation; molecular dynamics; MDM2 1. -
Free Energy Calculations in Action: Theory, Applications and Challenges of Solvation Free Energies
UC Irvine UC Irvine Electronic Theses and Dissertations Title Free Energy Calculations in Action: Theory, Applications and Challenges of Solvation Free Energies Permalink https://escholarship.org/uc/item/1tr0c2p0 Author Duarte Ramos Matos, Guilherme Publication Date 2018 License https://creativecommons.org/licenses/by/4.0/ 4.0 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, IRVINE Free Energy Calculations in Action: Theory, Applications and Challenges of Solvation Free Energies DISSERTATION submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in Chemistry by Guilherme Duarte Ramos Matos Dissertation Committee: Professor David L. Mobley, Chair Professor Ioan Andricioaei Professor Thomas Poulos 2018 c 2018 Guilherme Duarte Ramos Matos DEDICATION To my grandparents, Ana and José, who passed away while I was living in a foreign land. ii TABLE OF CONTENTS Page LIST OF FIGURES vi LIST OF TABLES x ACKNOWLEDGMENTS xii CURRICULUM VITAE xiv ABSTRACT OF THE DISSERTATION xvi 1 Introduction 1 1.1 Free energies have a central role in understanding much of Chemistry, Bio- chemistry and Pharmaceutical Sciences . 1 1.2 The theory behind free energy calculations . 2 1.3 Free energy calculations have a wide range of applications . 5 2 Approaches for calculating solvation free energies and enthalpies demon- strated with an update of the FreeSolv database 8 2.1 Introduction . 9 2.2 Hydration and solvation free energies have a range of applications . 12 2.3 Theory and practical aspects of alchemical calculations . 13 2.3.1 Choice of alchemical pathway . 16 2.3.2 Considerations for successful alchemical calculations . -
Free Energy Simulations of Complex Biological Systems at Constant Ph
FREE ENERGY SIMULATIONS OF COMPLEX BIOLOGICAL SYSTEMS AT CONSTANT PH By JASON M. SWAILS A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2013 © 2013 Jason M. Swails 2 I dedicate this dissertation to the late Professor Frederick P. Arnold whose intelligence, excitement, and guidance propelled me into this field. 3 ACKNOWLEDGMENTS I would like to thank my parents, Mark and Mindy Swails, for their love, guidance, and constant encouragement. I thank my siblings, Kerri and Jeffrey Swails, for all the great times and their (vain) attempts to keep me humble. Thank you to my extended family for the incredible support system I’ve always had growing up. The Roitberg Group provided a great deal of support and comaraderie, and Adrian Roitberg provided guidance and instruction during my graduate studies. I also thank Quantum Theory Project for all the good times. And finally, I would like to thank my wife, Roxy Lowry Swails, for everything. 4 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................. 4 LIST OF TABLES ...................................... 9 LIST OF FIGURES ..................................... 10 LIST OF ABBREVIATIONS ................................ 13 LIST OF CONSTANTS AND OPERATORS ....................... 15 ABSTRACT ......................................... 16 CHAPTER 1 INTRODUCTION ................................... 18 1.1 Origins of Computational Chemistry ..................... 18 1.1.1 Quantum Mechanics .......................... 19 1.1.1.1 Born-Oppenheimer Approximation ............. 20 1.1.1.2 Computational Quantum Mechanics ............ 21 1.1.2 Statistical Mechanics .......................... 21 1.1.2.1 Monte Carlo ......................... 24 1.1.2.2 Molecular Dynamics and the Ergodic Hypothesis ..... 28 1.2 Molecular Mechanics .............................