Atomistic Modeling of DNA and Protein Structures
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MODELLER 10.1 Manual
MODELLER A Program for Protein Structure Modeling Release 10.1, r12156 Andrej Saliˇ with help from Ben Webb, M.S. Madhusudhan, Min-Yi Shen, Guangqiang Dong, Marc A. Martı-Renom, Narayanan Eswar, Frank Alber, Maya Topf, Baldomero Oliva, Andr´as Fiser, Roberto S´anchez, Bozidar Yerkovich, Azat Badretdinov, Francisco Melo, John P. Overington, and Eric Feyfant email: modeller-care AT salilab.org URL https://salilab.org/modeller/ 2021/03/12 ii Contents Copyright notice xxi Acknowledgments xxv 1 Introduction 1 1.1 What is Modeller?............................................. 1 1.2 Modeller bibliography....................................... .... 2 1.3 Obtainingandinstallingtheprogram. .................... 3 1.4 Bugreports...................................... ............ 4 1.5 Method for comparative protein structure modeling by Modeller ................... 5 1.6 Using Modeller forcomparativemodeling. ... 8 1.6.1 Preparinginputfiles . ............. 8 1.6.2 Running Modeller ......................................... 9 2 Automated comparative modeling with AutoModel 11 2.1 Simpleusage ..................................... ............ 11 2.2 Moreadvancedusage............................... .............. 12 2.2.1 Including water molecules, HETATM residues, and hydrogenatoms .............. 12 2.2.2 Changing the default optimization and refinement protocol ................... 14 2.2.3 Getting a very fast and approximate model . ................. 14 2.2.4 Building a model from multiple templates . .................. 15 2.2.5 Buildinganallhydrogenmodel -
20 Years of Polarizable Force Field Development
20 YEARS OF POLARIZABLE FORCEFIELDDEVELOPMENTfor biomolecular systems thor van heesch Supervised by Daan P. Geerke and Paola Gori-Giorg 1 contents 2 1 Introduction 3 2contentsForce fields: Basics, Caveats and Extensions 4 2.1 The Classical Approach . 4 2.2 The caveats of point-charge electrostatics . 8 2.3 Physical phenomenon of polarizability . 10 2.4 Common implementation methods of electronic polarization . 11 2.5 Accounting for anisotropic interactions . 14 3 Knitting the reviews and perspectives together 17 3.1 Polarizability: A smoking gun? . 17 3.2 New branches of electronic polarization . 18 3.3 Descriptions of electrostatics . 19 3.4 Solvation and polarization . 20 3.5 The rise of new challenges . 21 3.6 Parameterization or polarization? . 22 3.7 Enough response: how far away? . 23 3.8 The last perspectives . 23 3.9 A new hope: the next-generation force fields . 29 4 Learning with machines 29 4.1 Replace the functional form with machine learned force fields . 31 4.2 A different take on polarizable force fields . 33 4.3 Are transferable parameters an universal requirement? . 33 4.4 The difference between derivation and prediction . 34 4.5 From small molecules to long range interactions . 36 4.6 Enough knowledge to fold a protein? . 37 4.7 Boltzmann generators, a not so hypothetical machine anymore . 38 5 Summary: The Red Thread 41 introduction 3 In this literature study we aimed to answer the following question: What has changedabstract in the outlook on polarizable force field development during the last 20 years? The theory, history, methods, and applications of polarizable force fields have been discussed to address this question. -
Secondary Structure Propensities in Peptide Folding Simulations: a Systematic Comparison of Molecular Mechanics Interaction Schemes
Biophysical Journal Volume 97 July 2009 599–608 599 Secondary Structure Propensities in Peptide Folding Simulations: A Systematic Comparison of Molecular Mechanics Interaction Schemes Dirk Matthes and Bert L. de Groot* Department of Theoretical and Computational Biophysics, Computational Biomolecular Dynamics Group, Max-Planck-Institute for Biophysical Chemistry, Go¨ttingen, Germany ABSTRACT We present a systematic study directed toward the secondary structure propensity and sampling behavior in peptide folding simulations with eight different molecular dynamics force-field variants in explicit solvent. We report on the combi- national result of force field, water model, and electrostatic interaction schemes and compare to available experimental charac- terization of five studied model peptides in terms of reproduced structure and dynamics. The total simulation time exceeded 18 ms and included simulations that started from both folded and extended conformations. Despite remaining sampling issues, a number of distinct trends in the folding behavior of the peptides emerged. Pronounced differences in the propensity of finding prominent secondary structure motifs in the different applied force fields suggest that problems point in particular to the balance of the relative stabilities of helical and extended conformations. INTRODUCTION Molecular dynamics (MD) simulations are routinely utilized to that study, simulations of relatively short lengths were per- study the folding dynamics of peptides and small proteins as formed and the natively folded state was used as starting well as biomolecular aggregation. The critical constituents of point, possibly biasing the results (13). such molecular mechanics studies are the validity of the under- For folding simulations, such a systematic test has not lyingphysical models together with theassumptionsofclassical been carried out so far, although with growing computer dynamics and a sufficient sampling of the conformational power several approaches toward the in silico folding space. -
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 -
Melissa Gajewski & Jonathan Mane
Melissa Gajewski & Jonathan Mane Molecular Mechanics (MM) Methods Force fields & potential energy calculations Example using noscapine Molecular Dynamics (MD) Methods Ensembles & trajectories Example using 18-crown-6 Quantum Mechanics (QM) Methods Schrödinger’s equation Semi-empirical (SE) Wave Functional Theory (WFT) Density Functional Theory (DFT) Hybrid QM/MM & MD Methods Comparison of hybrid methods 2 3 Molecular Mechanics (MM) Methods Force fields & potential energy calculations Example using noscapine Molecular Dynamics (MD) Methods Ensembles & trajectories Example using 18-crown-6 Quantum Mechanics (QM) Methods Schrödinger’s equation Semi-empirical (SE) Wave Functional Theory (WFT) Density Functional Theory (DFT) Hybrid QM/MM & MD Methods Comparison of hybrid methods 4 Useful for all system size ◦ Small molecules, proteins, material assemblies, surface science, etc … ◦ Based on Newtonian mechanics (classical mechanics) d F = (mv) dt ◦ The potential energy of the system is calculated using a force field € 5 An atom is considered as a single particle Example: H atom Particle variables: ◦ Radius (typically van der Waals radius) ◦ Polarizability ◦ Net charge Obtained from experiment or QM calculations ◦ Bond interactions Equilibrium bond lengths & angles from experiment or QM calculations 6 All atom approach ◦ Provides parameters for every atom in the system (including hydrogen) Ex: In –CH3 each atom is assigned a set of data MOLDEN MOLDEN (Radius, polarizability, netMOLDENMOLDENMOLDENMOLDENMOLDEN charge, -
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. -
Introduction to GROMOS
CSCPB - Introduction to GROMOS Information: Prof. Dr. W.F. van Gunsteren Laboratory of Physical Chemistry, ETH-Z¨urich, 8092 Z¨urich, Switzerland Tel: +41-44-6325501 Fax: +41-44-6321039 e-mail: [email protected] Note: This MD tutorial is still under development. Please report any errors, incon- sistencies, obscurities or suggestions for improvements to [email protected]. Purpose: This molecular dynamics (MD) tutorial serves a number of objectives. These are to obtain experience with: • computer simulation in general • executing and understanding GROMOS • interpreting the results of computer simulations Version: SVN revision December 5, 2007 W.F.v.G., Z.G., J.D., A-P.K., C.C., N.S. 1 Required knowledge: Some knowledge about the following is required: • Computer programming languages: ability to read simple code (C++). • Computer operating system language of the machine on which the tutorial will be carried out: UNIX. Good books on MD are (Frenkel & Smit, 2002) and (Allen & Tildesley, 1987). 2 Contents 1 INTRODUCTION 4 1.1 The Lennard-Jones interaction . 4 1.2 Electrostatic interaction . 5 1.3 Periodic boundary conditions . 5 1.4 Newton’s equations of motion . 6 1.5 The leap-frog integration scheme . 7 1.6 Coupling to a temperature bath . 8 1.7 Pressure and the virial . 9 1.8 Center of mass motion . 9 1.9 Gaussian or Maxwellian distributions . 10 1.10 The radial distribution function g(r).................. 10 1.11 Units . 11 2 INSTALLATION OF GROMOS 12 2.1 System Requirements . 12 2.2 Getting and Installing GROMOSXX . 13 2.3 Getting and Installing GROMOS++ . -
Qwikmd-Tutorial.Pdf
University of Illinois at Urbana-Champaign Beckman Institute for Advanced Science and Technology Theoretical and Computational Biophysics Group Computational Biophysics Workshop QwikMD - Easy Molecular Dynamics with NAMD and VMD Tutorial by Rafael C. Bernardi, Till Rudack, Joao V. Ribeiro, Angela Barragan, Muyun Lihan, Rezvan Shahoei and Yi Zhang July 12 2017 QwikMD Developers: Joao V. Ribeiro Rafael C. Bernardi Till Rudack A current version of this tutorial is available at http://www.ks.uiuc.edu/Training/Tutorials/ CONTENTS 2 Contents 1 Introduction 3 1.1 NAMD . 3 1.2 QwikMD . 4 2 Required programs 4 2.1 For Linux/Mac Users: . 5 2.2 For Windows Users: . 5 3 Getting Started 5 4 Installing the Required Programs 6 4.1 VMD . 6 4.2 QwikMD . 6 4.3 NAMD . 6 5 Running my First Molecular Dynamics Simulation 7 5.1 Ubiquitin in implicit solvent . 8 5.1.1 Preparing structures and starting simulations . 9 5.1.2 Analyzing during a Live Simulation . 11 5.2 Ubiquitin in a Water Box . 12 5.2.1 Starting a New Simulation . 12 5.2.2 Creating a Salt Solution . 13 5.3 Running your Simulation outside of QwikMD . 14 6 Tackling common scientific problems 15 6.1 Cancer mutation in Ras . 16 6.2 HIV protease . 19 6.3 Proton transport through a membrane by bactheriorhodopsin . 25 7 Steered Molecular Dynamics 31 7.1 Biomolecular interactions during protein unfolding . 31 7.1.1 Preparing a SMD system . 32 7.1.2 Analyzing during a Live Simulation . 33 7.2 Setting-up steered molecular dynamics to study protein complex interaction . -
Density Functional Theory for Protein Transfer Free Energy
Density Functional Theory for Protein Transfer Free Energy Eric A Mills, and Steven S Plotkin∗ Department of Physics & Astronomy, University of British Columbia, Vancouver, British Columbia V6T1Z4 Canada E-mail: [email protected] Phone: 1 604-822-8813. Fax: 1 604-822-5324 arXiv:1310.1126v1 [q-bio.BM] 3 Oct 2013 ∗To whom correspondence should be addressed 1 Abstract We cast the problem of protein transfer free energy within the formalism of density func- tional theory (DFT), treating the protein as a source of external potential that acts upon the sol- vent. Solvent excluded volume, solvent-accessible surface area, and temperature-dependence of the transfer free energy all emerge naturally within this formalism, and may be compared with simplified “back of the envelope” models, which are also developed here. Depletion contributions to osmolyte induced stability range from 5-10kBT for typical protein lengths. The general DFT transfer theory developed here may be simplified to reproduce a Langmuir isotherm condensation mechanism on the protein surface in the limits of short-ranged inter- actions, and dilute solute. Extending the equation of state to higher solute densities results in non-monotonic behavior of the free energy driving protein or polymer collapse. Effective inter- action potentials between protein backbone or sidechains and TMAO are obtained, assuming a simple backbone/sidechain 2-bead model for the protein with an effective 6-12 potential with the osmolyte. The transfer free energy dg shows significant entropy: d(dg)=dT ≈ 20kB for a 100 residue protein. The application of DFT to effective solvent forces for use in implicit- solvent molecular dynamics is also developed. -
Tinker-HP : Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields Using Gpus and Multi-Gpus Systems
Tinker-HP : Accelerating Molecular Dynamics Simulations of Large Complex Systems with Advanced Point Dipole Polarizable Force Fields using GPUs and Multi-GPUs systems Olivier Adjoua,y Louis Lagardère*,y,z Luc-Henri Jolly,z Arnaud Durocher,{ Thibaut Very,x Isabelle Dupays,x Zhi Wang,k Théo Jaffrelot Inizan,y Frédéric Célerse,y,? Pengyu Ren,# Jay W. Ponder,k and Jean-Philip Piquemal∗,y,# ySorbonne Université, LCT, UMR 7616 CNRS, F-75005, Paris, France zSorbonne Université, IP2CT, FR2622 CNRS, F-75005, Paris, France {Eolen, 37-39 Rue Boissière, 75116 Paris, France xIDRIS, CNRS, Orsay, France kDepartment of Chemistry, Washington University in Saint Louis, USA ?Sorbonne Université, CNRS, IPCM, Paris, France. #Department of Biomedical Engineering, The University of Texas at Austin, USA E-mail: [email protected],[email protected] arXiv:2011.01207v4 [physics.comp-ph] 3 Mar 2021 Abstract We present the extension of the Tinker-HP package (Lagardère et al., Chem. Sci., 2018,9, 956-972) to the use of Graphics Processing Unit (GPU) cards to accelerate molecular dynamics simulations using polarizable many-body force fields. The new high-performance module allows for an efficient use of single- and multi-GPUs archi- 1 tectures ranging from research laboratories to modern supercomputer centers. After de- tailing an analysis of our general scalable strategy that relies on OpenACC and CUDA , we discuss the various capabilities of the package. Among them, the multi-precision possibilities of the code are discussed. If an efficient double precision implementation is provided to preserve the possibility of fast reference computations, we show that a lower precision arithmetic is preferred providing a similar accuracy for molecular dynamics while exhibiting superior performances. -
Multiscale Friction Simulation of Dry Polymer Contacts: Reaching Experimental Length Scales by Coupling Molecular Dynamics and Contact Mechanics
Multiscale Friction Simulation of Dry Polymer Contacts: Reaching Experimental Length Scales by Coupling Molecular Dynamics and Contact Mechanics Daniele Savio ( [email protected] ) Freudenberg Technology Innovation SE & Co. KG https://orcid.org/0000-0003-1908-2379 Jannik Hamann Fraunhofer Institute for Mechanics of Materials: Fraunhofer-Institut fur Werkstoffmechanik IWM Pedro A. Romero Freudenberg Technology Innovation SE & Co. KG Christoph Klingshirn Freudenberg FST GmbH Ravindrakumar Bactavatchalou Freudenberg Technology Innovation SE & Co. KG Martin Dienwiebel Fraunhofer Institute for Mechanics of Materials: Fraunhofer-Institut fur Werkstoffmechanik IWM Michael Moseler Fraunhofer Institute for Mechanics of Materials: Fraunhofer-Institut fur Werkstoffmechanik IWM Research Article Keywords: Coupling Molecular Dynamics, Contact Mechanics, Multiscale Friction Posted Date: February 23rd, 2021 DOI: https://doi.org/10.21203/rs.3.rs-223573/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Multiscale friction simulation of dry polymer contacts: reaching experimental length scales by coupling molecular dynamics and contact mechanics Daniele Savio1*, Jannik Hamann2, Pedro A. Romero1, Christoph Klingshirn3, Ravindrakumar Bactavatchalou1, Martin Dienwiebel2,4, Michael Moseler2,5 1 Freudenberg Technology Innovation SE & Co. KG, Weinheim, Germany 2 µTC Microtribology Center, Fraunhofer Institute for Mechanics of Materials IWM, Freiburg, Germany 3 Freudenberg FST GmbH, Weinheim, Germany 4 Karlsruhe Institute of Technology, Institute for Applied Materials, IAM-CMS, Karlsruhe, Germany 5 Institute of Physics, University of Freiburg, Freiburg, Germany *Corresponding author: [email protected] Abstract This work elucidates friction in Poly-Ether-Ether-Ketone (PEEK) sliding contacts through multiscale simulations. At the nanoscale, non-reactive classical molecular dynamics (MD) simulations of dry and water-lubricated amorphous PEEK-PEEK interfaces are performed. -
Computational Redox Potential Predictions: Applications to Inorganic and Organic Aqueous Complexes, and Complexes Adsorbed to Mineral Surfaces
Minerals 2014, 4, 345-387; doi:10.3390/min4020345 OPEN ACCESS minerals ISSN 2075-163X www.mdpi.com/journal/minerals Review Computational Redox Potential Predictions: Applications to Inorganic and Organic Aqueous Complexes, and Complexes Adsorbed to Mineral Surfaces Krishnamoorthy Arumugam and Udo Becker * Department of Earth and Environmental Sciences, University of Michigan, 1100 North University Avenue, 2534 C.C. Little, Ann Arbor, MI 48109-1005, USA; E-Mail: [email protected] * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +1-734-615-6894; Fax: +1-734-763-4690. Received: 11 February 2014; in revised form: 3 April 2014 / Accepted: 13 April 2014 / Published: 24 April 2014 Abstract: Applications of redox processes range over a number of scientific fields. This review article summarizes the theory behind the calculation of redox potentials in solution for species such as organic compounds, inorganic complexes, actinides, battery materials, and mineral surface-bound-species. Different computational approaches to predict and determine redox potentials of electron transitions are discussed along with their respective pros and cons for the prediction of redox potentials. Subsequently, recommendations are made for certain necessary computational settings required for accurate calculation of redox potentials. This article reviews the importance of computational parameters, such as basis sets, density functional theory (DFT) functionals, and relativistic approaches and the role that physicochemical processes play on the shift of redox potentials, such as hydration or spin orbit coupling, and will aid in finding suitable combinations of approaches for different chemical and geochemical applications. Identifying cost-effective and credible computational approaches is essential to benchmark redox potential calculations against experiments.