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Parallel-Tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling
1st Symposium on Advances in Approximate Bayesian Inference, 20181–6 Parallel-tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling ∗ Rui Luo [email protected] ∗ Qiang Zhang [email protected] Yuanyuan Liu [email protected] American International Group, Inc. Abstract We propose a new sampler that integrates the protocol of parallel tempering with the Nose-Hoover´ (NH) dynamics. The proposed method can efficiently draw representative samples from complex posterior distributions with multiple isolated modes in the presence of noise arising from stochastic gradient. It potentially facilitates deep Bayesian learning on large datasets where complex multi- modal posteriors and mini-batch gradient are encountered. 1. Introduction In Bayesian inference, one of the fundamental problems is to efficiently draw i.i.d. samples from the posterior distribution π¹θj Dº given the dataset D = fxg, where θ 2 D denotes the variable of interest. Provided the prior distribution π¹θº and the likelihood per datum `¹θ; xº, the posterior to be sampled can be formulated as Ö π¹θj Dº = π¹θº `¹θ; xº: (1) x 2D To facilitate posterior sampling, the framework of Markov chain Monte Carlo (MCMC) has been established, which has initiated a broad family of methods that generate Markov chains to propose new sample candidates and then apply tests of acceptance in order to guarantee the condition of detailed balance. Methods like the Metropolis-Hastings (MH) algorithm (Metropolis et al., 1953; Hastings, 1970), the Gibbs sampler (Geman and Geman, 1984), and the hybrid/Hamiltonian Monte Carlo (HMC) (Duane et al., 1987; Neal, 2011) are famous representatives for the MCMC family where different generating procedures of Markov chains are adopted; each of those methods has achieved great success on various tasks in statistics and related fields. -
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
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 -
Chapter 2 Monte Carlo Simulations
Chapter 2 Monte Carlo Simulations David J. Earl and Michael W. Deem Summary AdescriptionofMonteCarlomethodsforsimulationofproteinsisgiven. Advantages and disadvantages of the Monte Carlo approach are presented. The the- oretical basis for calculating equilibrium properties of biological molecules by the Monte Carlo method is presented. Some of the standard and some of the more re- cent ways of performing Monte Carlo on proteins are presented. A discussion of the estimation of errors in properties calculated by Monte Carlo is given. Keywords: Markov chain Metropolis algorithm Monte Carlo Protein simula- · · · tion Stochastic methods · 1 Introduction The term Monte Carlo generally applies to all simulations that use stochastic meth- ods to generate new configurations of a system of interest. In the context of mole- cular simulation, specifically, the simulation of proteins, Monte Carlo refers to importance sampling, which we describe in Sect. 2, of systems at equilibrium. In general, a Monte Carlo simulation will proceed as follows: starting from an initial configuration of particles in a system, a Monte Carlo move is attempted that changes the configuration of the particles. This move is accepted or rejected based on an ac- ceptance criterion that guarantees that configurations are sampled in the simulation from a statistical mechanics ensemble distribution, and that the configurations are sampled with the correct weight. After the acceptance or rejection of a move, one calculates the value of a property of interest, and, after many such moves, an accu- rate average value of this property can be obtained. With the application of statistical mechanics, it is possible to calculate the equilibrium thermodynamic properties of the system of interest in this way. -
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. -
LAMMPS – an Object Oriented Scientific Application
LAMMPS – An Object Oriented Scientific Application Dr. Axel Kohlmeyer (with a little help from several friends) Associate Dean for Scientific Computing College of Science and Technology Temple University, Philadelphia http://sites.google.com/site/akohlmey/ [email protected] Workshop on Advanced Techniques in Scientific Computing LAMMPS is a Collaborative Project A few lead developers and many significant contributors: ● Steve Plimpton, Aidan Thompson, Paul Crozier, Axel Kohlmeyer - Roy Pollock (LLNL), Ewald and PPPM solvers - Mike Brown (ORNL), GPU package - Greg Wagner (Sandia), MEAM package for MEAM potential - Mike Parks (Sandia), PERI package for Peridynamics - Rudra Mukherjee (JPL), POEMS package for rigid body motion - Reese Jones (Sandia), USER-ATC package for coupling to continuum - Ilya Valuev (JIHT), USER-AWPMD package for wave-packet MD - Christian Trott (Sandia), USER-CUDA package, KOKKOS package - A. Jaramillo-Botero (Caltech), USER-EFF electron force field package - Christoph Kloss (JKU), LIGGGHTS package for DEM and fluid coupling - Metin Aktulga (LBL), USER-REAXC package for C version of ReaxFF - Georg Gunzenmuller (EMI), USER-SPH package - Ray Shan (Sandia), COMB package, QEQ package - Trung Nguyen (ORNL), RIGID package, GPU package - Francis Mackay and Coling Denniston (U Western Ontario), USER-LB Workshop on Advanced Techniques in Scientific Computing LAMMPS is an Extensible Project ● ~2900 C/C++/CUDA files, 120 Fortran files, about 900,000 lines of code in core executable ● Only about 200 files are essential, about -
Molecular Dynamics Free Energy Simulations Reveal the Mechanism for the Antiviral Resistance of the M66I HIV-1 Capsid Mutation
viruses Article Molecular Dynamics Free Energy Simulations Reveal the Mechanism for the Antiviral Resistance of the M66I HIV-1 Capsid Mutation Qinfang Sun 1 , Ronald M. Levy 1,*, Karen A. Kirby 2,3 , Zhengqiang Wang 4 , Stefan G. Sarafianos 2,3 and Nanjie Deng 5,* 1 Center for Biophysics and Computational Biology and Department of Chemistry, Temple University, Philadelphia, PA 19122, USA; [email protected] 2 Laboratory of Biochemical Pharmacology, Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322, USA; [email protected] (K.A.K.); stefanos.sarafi[email protected] (S.G.S.) 3 Children’s Healthcare of Atlanta, Atlanta, GA 30322, USA 4 Center for Drug Design, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455, USA; [email protected] 5 Department of Chemistry and Physical Sciences, Pace University, New York, NY 10038, USA * Correspondence: [email protected] (R.M.L.); [email protected] (N.D.) Abstract: While drug resistance mutations can often be attributed to the loss of direct or solvent- mediated protein−ligand interactions in the drug-mutant complex, in this study we show that a resistance mutation for the picomolar HIV-1 capsid (CA)-targeting antiviral (GS-6207) is mainly due to the free energy cost of the drug-induced protein side chain reorganization in the mutant protein. Among several mutations, M66I causes the most suppression of the GS-6207 antiviral activity Citation: Sun, Q.; Levy, R.M.; Kirby, (up to ~84,000-fold), and only 83- and 68-fold reductions for PF74 and ZW-1261, respectively. To K.A.; Wang, Z.; Sarafianos, S.G.; understand the molecular basis of this drug resistance, we conducted molecular dynamics free energy Deng, N. -
Multiscale Simulations of Intrinsically Disordered Proteins
University of Massachusetts Amherst ScholarWorks@UMass Amherst Doctoral Dissertations Dissertations and Theses July 2019 Multiscale Simulations of Intrinsically Disordered Proteins Xiaorong Liu University of Massachusetts Amherst Follow this and additional works at: https://scholarworks.umass.edu/dissertations_2 Part of the Biophysics Commons, Physical Chemistry Commons, and the Structural Biology Commons Recommended Citation Liu, Xiaorong, "Multiscale Simulations of Intrinsically Disordered Proteins" (2019). Doctoral Dissertations. 1565. https://doi.org/10.7275/14020756 https://scholarworks.umass.edu/dissertations_2/1565 This Open Access Dissertation is brought to you for free and open access by the Dissertations and Theses at ScholarWorks@UMass Amherst. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. Multiscale Simulations of Intrinsically Disordered Proteins A Dissertation Presented by XIAORONG LIU Submitted to the Graduate School of the University of Massachusetts Amherst in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY May 2019 Chemistry © Copyright by Xiaorong Liu 2019 All Rights Reserved Multiscale Simulations of Intrinsically Disordered Proteins A Dissertation Presented by XIAORONG LIU Approved as to style and content by: ____________________________________ Jianhan Chen, Chair ____________________________________ Scott Auerbach, Member ____________________________________ Craig Martin, Member ____________________________________ Li-Jun Ma, Member __________________________________ Richard Vachet, Department Head Department of Chemistry DEDICATION To my parents, husband, advisor and other educators who light the way forward for me. ACKNOWLEDGMENTS First and foremost, I would like to express my wholehearted gratitude to my research advisor Dr. Jianhan Chen. Dr. Chen is a great scientist, who is extremely knowledgeable, wise and passionate about science. -
Maestro 10.2 User Manual
Maestro User Manual Maestro 10.2 User Manual Schrödinger Press Maestro User Manual Copyright © 2015 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. 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, BioLuminate, 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, use your browser to open third_party_legal.html, which is in the docs folder of your Schrödinger software installation. 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. -
Kepler Gpus and NVIDIA's Life and Material Science
LIFE AND MATERIAL SCIENCES Mark Berger; [email protected] Founded 1993 Invented GPU 1999 – Computer Graphics Visual Computing, Supercomputing, Cloud & Mobile Computing NVIDIA - Core Technologies and Brands GPU Mobile Cloud ® ® GeForce Tegra GRID Quadro® , Tesla® Accelerated Computing Multi-core plus Many-cores GPU Accelerator CPU Optimized for Many Optimized for Parallel Tasks Serial Tasks 3-10X+ Comp Thruput 7X Memory Bandwidth 5x Energy Efficiency How GPU Acceleration Works Application Code Compute-Intensive Functions Rest of Sequential 5% of Code CPU Code GPU CPU + GPUs : Two Year Heart Beat 32 Volta Stacked DRAM 16 Maxwell Unified Virtual Memory 8 Kepler Dynamic Parallelism 4 Fermi 2 FP64 DP GFLOPS GFLOPS per DP Watt 1 Tesla 0.5 CUDA 2008 2010 2012 2014 Kepler Features Make GPU Coding Easier Hyper-Q Dynamic Parallelism Speedup Legacy MPI Apps Less Back-Forth, Simpler Code FERMI 1 Work Queue CPU Fermi GPU CPU Kepler GPU KEPLER 32 Concurrent Work Queues Developer Momentum Continues to Grow 100M 430M CUDA –Capable GPUs CUDA-Capable GPUs 150K 1.6M CUDA Downloads CUDA Downloads 1 50 Supercomputer Supercomputers 60 640 University Courses University Courses 4,000 37,000 Academic Papers Academic Papers 2008 2013 Explosive Growth of GPU Accelerated Apps # of Apps Top Scientific Apps 200 61% Increase Molecular AMBER LAMMPS CHARMM NAMD Dynamics GROMACS DL_POLY 150 Quantum QMCPACK Gaussian 40% Increase Quantum Espresso NWChem Chemistry GAMESS-US VASP CAM-SE 100 Climate & COSMO NIM GEOS-5 Weather WRF Chroma GTS 50 Physics Denovo ENZO GTC MILC ANSYS Mechanical ANSYS Fluent 0 CAE MSC Nastran OpenFOAM 2010 2011 2012 SIMULIA Abaqus LS-DYNA Accelerated, In Development NVIDIA GPU Life Science Focus Molecular Dynamics: All codes are available AMBER, CHARMM, DESMOND, DL_POLY, GROMACS, LAMMPS, NAMD Great multi-GPU performance GPU codes: ACEMD, HOOMD-Blue Focus: scaling to large numbers of GPUs Quantum Chemistry: key codes ported or optimizing Active GPU acceleration projects: VASP, NWChem, Gaussian, GAMESS, ABINIT, Quantum Espresso, BigDFT, CP2K, GPAW, etc. -
Desmond Users Guide Release 3.4.0 / 0.7
Desmond Users Guide Release 3.4.0 / 0.7 D. E. Shaw Research December 18, 2012 CONTENTS 1 Front matter 3 1.1 Notice..................................................3 1.2 Copyright.................................................3 1.3 Trademarks................................................3 2 Preface 5 2.1 Intended audience............................................5 2.2 Prerequisites...............................................5 2.3 Format conventions...........................................5 2.4 About the equations...........................................6 3 Key Concepts 7 3.1 What is Desmond?............................................7 3.2 Forces..................................................8 3.3 Particles.................................................9 3.4 Force fields................................................9 3.5 Space................................................... 10 3.6 Time................................................... 10 3.7 Dynamics................................................. 11 3.8 Using Desmond............................................. 12 4 Running Desmond 15 4.1 About configuration........................................... 15 4.2 Invoking Desmond............................................ 16 4.3 Running Desmond in parallel...................................... 19 4.4 Configuring Desmond applications................................... 20 4.5 Naming output files........................................... 25 4.6 Configuring the built-in plugins..................................... 25 4.7 Configuring -
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.