Implementation of the QUBE Force Field for High-Throughput Alchemical
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Introducing ONETEP: Linear-Scaling Density Functional Simulations on Parallel Computers Chris-Kriton Skylaris,A) Peter D
THE JOURNAL OF CHEMICAL PHYSICS 122, 084119 ͑2005͒ Introducing ONETEP: Linear-scaling density functional simulations on parallel computers Chris-Kriton Skylaris,a) Peter D. Haynes, Arash A. Mostofi, and Mike C. Payne Theory of Condensed Matter, Cavendish Laboratory, Madingley Road, Cambridge CB3 0HE, United Kingdom ͑Received 29 September 2004; accepted 4 November 2004; published online 23 February 2005͒ We present ONETEP ͑order-N electronic total energy package͒, a density functional program for parallel computers whose computational cost scales linearly with the number of atoms and the number of processors. ONETEP is based on our reformulation of the plane wave pseudopotential method which exploits the electronic localization that is inherent in systems with a nonvanishing band gap. We summarize the theoretical developments that enable the direct optimization of strictly localized quantities expressed in terms of a delocalized plane wave basis. These same localized quantities lead us to a physical way of dividing the computational effort among many processors to allow calculations to be performed efficiently on parallel supercomputers. We show with examples that ONETEP achieves excellent speedups with increasing numbers of processors and confirm that the time taken by ONETEP as a function of increasing number of atoms for a given number of processors is indeed linear. What distinguishes our approach is that the localization is achieved in a controlled and mathematically consistent manner so that ONETEP obtains the same accuracy as conventional cubic-scaling plane wave approaches and offers fast and stable convergence. We expect that calculations with ONETEP have the potential to provide quantitative theoretical predictions for problems involving thousands of atoms such as those often encountered in nanoscience and biophysics. -
Natural Bond Orbital Analysis in the ONETEP Code: Applications to Large Protein Systems Louis P
WWW.C-CHEM.ORG FULL PAPER Natural Bond Orbital Analysis in the ONETEP Code: Applications to Large Protein Systems Louis P. Lee,*[a] Daniel J. Cole,[a] Mike C. Payne,[a] and Chris-Kriton Skylaris[b] First principles electronic structure calculations are typically Generalized Wannier Functions of ONETEP to natural atomic performed in terms of molecular orbitals (or bands), providing a orbitals, NBO analysis can be performed within a localized straightforward theoretical avenue for approximations of region in such a way that ensures the results are identical to an increasing sophistication, but do not usually provide any analysis on the full system. We demonstrate the capabilities of qualitative chemical information about the system. We can this approach by performing illustrative studies of large derive such information via post-processing using natural bond proteins—namely, investigating changes in charge transfer orbital (NBO) analysis, which produces a chemical picture of between the heme group of myoglobin and its ligands with bonding in terms of localized Lewis-type bond and lone pair increasing system size and between a protein and its explicit orbitals that we can use to understand molecular structure and solvent, estimating the contribution of electronic delocalization interactions. We present NBO analysis of large-scale calculations to the stabilization of hydrogen bonds in the binding pocket of with the ONETEP linear-scaling density functional theory package, a drug-receptor complex, and observing, in situ, the n ! p* which we have interfaced with the NBO 5 analysis program. In hyperconjugative interactions between carbonyl groups that ONETEP calculations involving thousands of atoms, one is typically stabilize protein backbones. -
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. -
Density Functional Theory (DFT)
Herramientas mecano-cuánticas basadas en DFT para el estudio de moléculas y materiales en Materials Studio 7.0 Javier Ramos Biophysics of Macromolecular Systems group (BIOPHYM) Departamento de Física Macromolecular Instituto de Estructura de la Materia – CSIC [email protected] Webinar, 26 de Junio 2014 Anteriores webinars Como conseguir los videos y las presentaciones de anteriores webminars: Linkedin: Grupo de Química Computacional http://www.linkedin.com/groups/Química-computacional-7487634 Índice Density Functional Theory (DFT) The Jacob’s ladder DFT modules in Maretials Studio DMOL3, CASTEP and ONETEP XC functionals Basis functions Interfaces in Materials Studio Tasks Properties Example: n-butane conformations Density Functional Theory (DFT) DFT is built around the premise that the energy of an electronic system can be defined in terms of its electron probability density (ρ). (Hohenberg-Kohn Theorem) E 0 [ 0 ] Te [ 0 ] E ne [ 0 ] E ee [ 0 ] (easy) Kinetic Energy for ????? noninteracting (r )v (r ) dr electrons(easy) 1 E[]()()[]1 r r d r d r E e e2 1 2 1 2 X C r12 Classic Term(Coulomb) Non-classic Kohn-Sham orbitals Exchange & By minimizing the total energy functional applying the variational principle it is Correlation possible to get the SCF equations (Kohn-Sham) The Jacob’s Ladder Accurate form of XC potential Meta GGA Empirical (Fitting to Non-Empirical Generalized Gradient Approx. atomic properties) (physics rules) Local Density Approximation DFT modules in Materials Studio DMol3: Combine computational speed with the accuracy of quantum mechanical methods to predict materials properties reliably and quickly CASTEP: CASTEP offers simulation capabilities not found elsewhere, such as accurate prediction of phonon spectra, dielectric constants, and optical properties. -
Compact Orbitals Enable Low-Cost Linear-Scaling Ab Initio Molecular Dynamics for Weakly-Interacting Systems Hayden Scheiber,1, A) Yifei Shi,1 and Rustam Z
Compact orbitals enable low-cost linear-scaling ab initio molecular dynamics for weakly-interacting systems Hayden Scheiber,1, a) Yifei Shi,1 and Rustam Z. Khaliullin1, b) Department of Chemistry, McGill University, 801 Sherbrooke St. West, Montreal, QC H3A 0B8, Canada Today, ab initio molecular dynamics (AIMD) relies on the locality of one-electron density matrices to achieve linear growth of computation time with systems size, crucial in large-scale simulations. While Kohn-Sham orbitals strictly localized within predefined radii can offer substantial computational advantages over density matrices, such compact orbitals are not used in AIMD because a compact representation of the electronic ground state is difficult to find. Here, a robust method for maintaining compact orbitals close to the ground state is coupled with a modified Langevin integrator to produce stable nuclear dynamics for molecular and ionic systems. This eliminates a density matrix optimization and enables first orbital-only linear-scaling AIMD. An application to liquid water demonstrates that low computational overhead of the new method makes it ideal for routine medium-scale simulations while its linear-scaling complexity allows to extend first- principle studies of molecular systems to completely new physical phenomena on previously inaccessible length scales. Since the unification of molecular dynamics and den- LS methods restrict their use in dynamical simulations sity functional theory (DFT)1, ab initio molecular dy- to very short time scales, systems of low dimensions, namics (AIMD) has become an important tool to study and low-quality minimal basis sets6,18–20. On typical processes in molecules and materials. Unfortunately, the length and time scales required in practical and accurate computational cost of the conventional Kohn-Sham (KS) AIMD simulations, LS DFT still cannot compete with DFT grows cubically with the number of atoms, which the straightforward low-cost cubically-scaling KS DFT. -
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. -
An Overview on the Libxc Library of Density Functional Approximations
An overview on the Libxc library of density functional approximations Susi Lehtola Molecular Sciences Software Institute at Virginia Tech 2 June 2021 Outline Why Libxc? Recap on DFT What is Libxc? Using Libxc A look under the hood Wrapup GPAW 2021: Users' and Developers' Meeting Susi Lehtola Why Libxc? 2/28 Why Libxc? There are many approximations for the exchange-correlation functional. But, most programs I ... only implement a handful (sometimes 5, typically 10-15) I ... and the implementations may be buggy / non-standard GPAW 2021: Users' and Developers' Meeting Susi Lehtola Why Libxc? 3/28 Why Libxc, cont'd This leads to issues with reproducibility I chemists and physicists do not traditionally use the same functionals! Outdated(?) stereotype: B3LYP vs PBE I how to reproduce a calculation performed with another code? GPAW 2021: Users' and Developers' Meeting Susi Lehtola Why Libxc? 4/28 Why Libxc, cont'd The issue is compounded by the need for backwards and forwards compatibility: how can one I reproduce old calculations from the literature done with a now-obsolete functional (possibly with a program that is proprietary / no longer available)? I use a newly developed functional in an old program? GPAW 2021: Users' and Developers' Meeting Susi Lehtola Why Libxc? 5/28 Why Libxc, cont'd A standard implementation is beneficial! I no need to keep reinventing (and rebuilding) the wheel I use same collection of density functionals in all programs I new functionals only need to be implemented in one place I broken/buggy functionals only need to be fixed in one place I same implementation can be used across numerical approaches, e.g. -
Quantum Chemistry (QC) on Gpus Feb
Quantum Chemistry (QC) on GPUs Feb. 2, 2017 Overview of Life & Material Accelerated Apps MD: All key codes are GPU-accelerated QC: All key codes are ported or optimizing Great multi-GPU performance Focus on using GPU-accelerated math libraries, OpenACC directives Focus on dense (up to 16) GPU nodes &/or large # of GPU nodes GPU-accelerated and available today: ACEMD*, AMBER (PMEMD)*, BAND, CHARMM, DESMOND, ESPResso, ABINIT, ACES III, ADF, BigDFT, CP2K, GAMESS, GAMESS- Folding@Home, GPUgrid.net, GROMACS, HALMD, HOOMD-Blue*, UK, GPAW, LATTE, LSDalton, LSMS, MOLCAS, MOPAC2012, LAMMPS, Lattice Microbes*, mdcore, MELD, miniMD, NAMD, NWChem, OCTOPUS*, PEtot, QUICK, Q-Chem, QMCPack, OpenMM, PolyFTS, SOP-GPU* & more Quantum Espresso/PWscf, QUICK, TeraChem* Active GPU acceleration projects: CASTEP, GAMESS, Gaussian, ONETEP, Quantum Supercharger Library*, VASP & more green* = application where >90% of the workload is on GPU 2 MD vs. QC on GPUs “Classical” Molecular Dynamics Quantum Chemistry (MO, PW, DFT, Semi-Emp) Simulates positions of atoms over time; Calculates electronic properties; chemical-biological or ground state, excited states, spectral properties, chemical-material behaviors making/breaking bonds, physical properties Forces calculated from simple empirical formulas Forces derived from electron wave function (bond rearrangement generally forbidden) (bond rearrangement OK, e.g., bond energies) Up to millions of atoms Up to a few thousand atoms Solvent included without difficulty Generally in a vacuum but if needed, solvent treated classically -
Introduction to DFT and the Plane-Wave Pseudopotential Method
Introduction to DFT and the plane-wave pseudopotential method Keith Refson STFC Rutherford Appleton Laboratory Chilton, Didcot, OXON OX11 0QX 23 Apr 2014 Parallel Materials Modelling Packages @ EPCC 1 / 55 Introduction Synopsis Motivation Some ab initio codes Quantum-mechanical approaches Density Functional Theory Electronic Structure of Condensed Phases Total-energy calculations Introduction Basis sets Plane-waves and Pseudopotentials How to solve the equations Parallel Materials Modelling Packages @ EPCC 2 / 55 Synopsis Introduction A guided tour inside the “black box” of ab-initio simulation. Synopsis • Motivation • The rise of quantum-mechanical simulations. Some ab initio codes Wavefunction-based theory • Density-functional theory (DFT) Quantum-mechanical • approaches Quantum theory in periodic boundaries • Plane-wave and other basis sets Density Functional • Theory SCF solvers • Molecular Dynamics Electronic Structure of Condensed Phases Recommended Reading and Further Study Total-energy calculations • Basis sets Jorge Kohanoff Electronic Structure Calculations for Solids and Molecules, Plane-waves and Theory and Computational Methods, Cambridge, ISBN-13: 9780521815918 Pseudopotentials • Dominik Marx, J¨urg Hutter Ab Initio Molecular Dynamics: Basic Theory and How to solve the Advanced Methods Cambridge University Press, ISBN: 0521898633 equations • Richard M. Martin Electronic Structure: Basic Theory and Practical Methods: Basic Theory and Practical Density Functional Approaches Vol 1 Cambridge University Press, ISBN: 0521782856 -
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. -
ADP-Ribose) Polymerase-1 Catalytic Pocket Using Autogrow4, a Genetic Algorithm for De Novo Design
Targeting the Poly (ADP-Ribose) Polymerase-1 Catalytic Pocket Using AutoGrow4, a Genetic Algorithm for De Novo Design by Jacob Oscar Spiegel Bachelor of Engineering in Biomedical Engineering, State University of New York at Stony Brook, 2013 Submitted to the Graduate Faculty of the Dietrich School of Arts and Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2020 Committee Page UNIVERSITY OF PITTSBURGH DIETRICH SCHOOL OF ARTS AND SCIENCES This dissertation was presented by Jacob Oscar Spiegel It was defended on March 10, 2020 and approved by Dr. Andrew VanDemark, Associate Professor, Department of Biological Sciences Dr. Jeffrey Lawrence, Professor and Chair, Department of Biological Sciences Dr. Bennett Van Houten, Professor, Department of Pharmacology and Chemical Biology Dissertation Director: Dr. Jacob Durrant, Assistant Professor, Department of Biological Sciences ii Copyright © by Jacob Oscar Spiegel 2020 iii Targeting the Poly (ADP-Ribose) Polymerase-1 Catalytic Pocket Using AutoGrow4, a Genetic Algorithm for De Novo Design Jacob Oscar Spiegel, Ph.D. University of Pittsburgh, 2020 AutoGrow4 is a free and open-source program for de novo drug design that uses a genetic algorithm (GA) to create novel predicted small-molecule ligands for a given protein target without the constraints of a finite, pre-defined virtual library. By leveraging recent computational and cheminformatic advancements, AutoGrow4 is faster, more stable, and more modular than previous versions. Features such as docking-software compatibility, chemical filters, multithreading options, and selection methods have been expanded to support a wide range of user needs. This dissertation will cover the development and validation of AutoGrow4, as well as its application to poly (ADP-ribose) polymerase-1 (PARP-1). -
Enforced Presentation of an Extrahelical Guanine to the Lesion Recognition Pocket of Human 8- Oxoguanine Glycosylase, Hogg1
Enforced Presentation of an Extrahelical Guanine to the Lesion Recognition Pocket of Human 8- Oxoguanine Glycosylase, hOGG1 The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Crenshaw, Charisse M., Kwangho Nam, Kimberly Oo, Peter S. Kutchukian, Brian R. Bowman, Martin Karplus, and Gregory L. Verdine. 2012. “Enforced Presentation of an Extrahelical Guanine to the Lesion Recognition Pocket of Human 8-Oxoguanine Glycosylase, HOGG1.” Journal of Biological Chemistry287 (30): 24916–28. https:// doi.org/10.1074/jbc.M111.316497. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:41511240 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA THE JOURNAL OF BIOLOGICAL CHEMISTRY VOL. 287, NO. 30, pp. 24916–24928, July 20, 2012 © 2012 by The American Society for Biochemistry and Molecular Biology, Inc. Published in the U.S.A. Enforced Presentation of an Extrahelical Guanine to the Lesion Recognition Pocket of Human 8-Oxoguanine Glycosylase, hOGG1*□S Received for publication, October 25, 2011, and in revised form, March 17, 2012 Published, JBC Papers in Press, April 16, 2012, DOI 10.1074/jbc.M111.316497 Charisse M. Crenshaw‡¶, Kwangho Nam§¶, Kimberly Oo§, Peter S. Kutchukian§¶, Brian R. Bowman§¶, Martin Karplus§ʈ, and Gregory L. Verdine§¶**1 From the Departments