Evaluation of Fast Electrostatics Algorithms Alice N. Ko and Jesús A. Izaguirre with Thierry Matthey Department of Computer Science and Engineering University.

Slides:



Advertisements
Similar presentations
Scientific & technical presentation Structure Visualization with MarvinSpace Oct 2006.
Advertisements

Formulation of an algorithm to implement Lowe-Andersen thermostat in parallel molecular simulation package, LAMMPS Prathyusha K. R. and P. B. Sunil Kumar.
Transfer FAS UAS SAINT-PETERSBURG STATE UNIVERSITY COMPUTATIONAL PHYSICS Introduction Physical basis Molecular dynamics Temperature and thermostat Numerical.
The Role of Long-Range Forces in Porin Channel Conduction S. Aboud Electrical and Computer Engineering Department, Worcester Polytechnic Institute, Worcester.
N-Body I CS 170: Computing for the Sciences and Mathematics.
Computational Issues in Modeling Ion Transport in Biological Channels: Self- Consistent Particle-Based Simulations S. Aboud 1,2, M. Saraniti 1,2 and R.
Deterministic Global Parameter Estimation for a Budding Yeast Model T.D Panning*, L.T. Watson*, N.A. Allen*, C.A. Shaffer*, and J.J Tyson + Departments.
Molecular Dynamics: Review. Molecular Simulations NMR or X-ray structure refinements Protein structure prediction Protein folding kinetics and mechanics.
Computational methods in molecular biophysics (examples of solving real biological problems) EXAMPLE I: THE PROTEIN FOLDING PROBLEM Alexey Onufriev, Virginia.
Ion Solvation Thermodynamics from Simulation with a Polarizable Force Field Gaurav Chopra 07 February 2005 CS 379 A Alan GrossfeildPengyu Ren Jay W. Ponder.
Sampath Koppole. Brief outline of the Talk: Summary Introduction to Continuum Electrostatics: Continuum Electrostatics --- What is it ?? Solvation free.
An image-based reaction field method for electrostatic interactions in molecular dynamics simulations Presented By: Yuchun Lin Department of Mathematics.
Two important lines of research in the behavior of proteins are - Given the sequence : predict the structure of the protein - Given the structure : predict.
The Calculation of Enthalpy and Entropy Differences??? (Housekeeping Details for the Calculation of Free Energy Differences) first edition: p
Prof. Jesús A. Izaguirre Department of Computer Science and Engineering Computational Biology and Bioinformatics at Notre Dame.
Case Studies Class 5. Computational Chemistry Structure of molecules and their reactivities Two major areas –molecular mechanics –electronic structure.
An improved hybrid Monte Carlo method for conformational sampling of large biomolecules Department of Computer Science and Engineering University of Notre.
Separation of Water and Alcohols using 1-hexyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide 1 Alexandre Chapeaux, Luke D. Simoni, Mark A. Stadtherr,
MDSimAid: Automatic optimization of fast electrostatics in molecular simulations Jesús A. Izaguirre, Michael Crocker, Alice Ko, Thierry Matthey and Yao.
1 Parallel multi-grid summation for the N-body problem Jesús A. Izaguirre with Thierry Matthey Department of Computer Science and Engineering University.
1 Improved Hybrid Monte Carlo method for conformational sampling Jesús A. Izaguirre with Scott Hampton Department of Computer Science and Engineering University.
1 Nonlinear Instability in Multiple Time Stepping Molecular Dynamics Jesús Izaguirre, Qun Ma, Department of Computer Science and Engineering University.
1 An improved hybrid Monte Carlo method for conformational sampling of proteins Jesús A. Izaguirre and Scott Hampton Department of Computer Science and.
Molecular Dynamics Simulations
CompuCell Software Current capabilities and Research Plan Rajiv Chaturvedi Jesús A. Izaguirre With Patrick M. Virtue.
Orthogonal moments Motivation for using OG moments Stable calculation by recurrent relations Easier and stable image reconstruction - set of orthogonal.
Algorithms and Software for Large-Scale Simulation of Reactive Systems _______________________________ Ananth Grama Coordinated Systems Lab Purdue University.
Matthew Fischels Aerospace Engineering Department Major Professor : Dr. R. Ganesh Rajagopalan REDUCING RUNTIME OF WIND TURBINE SIMULATION Los Alamos National.
1 Effect of the Range of Interactions on the Properties of Fluids Equilibria of CO 2, Acetone, Methanol and Water Ivo Nezbeda 1,2, Ariel A. Chialvo 3,2,
Scheduling Many-Body Short Range MD Simulations on a Cluster of Workstations and Custom VLSI Hardware Sumanth J.V, David R. Swanson and Hong Jiang University.
Combined Central and Subspace Clustering for Computer Vision Applications Le Lu 1 René Vidal 2 1 Computer Science Department, Johns Hopkins University,
The Geometry of Biomolecular Solvation 2. Electrostatics Patrice Koehl Computer Science and Genome Center
E-science grid facility for Europe and Latin America E2GRIS1 André A. S. T. Ribeiro – UFRJ (Brazil) Itacuruça (Brazil), 2-15 November 2008.
Computer Simulation of Biomolecules and the Interpretation of NMR Measurements generates ensemble of molecular configurations all atomic quantities Problems.
NIH Resource for Biomolecular Modeling and Bioinformatics Beckman Institute, UIUC NAMD Development Goals L.V. (Sanjay) Kale Professor.
NIH Resource for Biomolecular Modeling and Bioinformatics Beckman Institute, UIUC NAMD Development Goals L.V. (Sanjay) Kale Professor.
Plan Last lab will be handed out on 11/22. No more labs/home works after Thanksgiving. 11/29 lab session will be changed to lecture. In-class final (1hour):
An FPGA Implementation of the Ewald Direct Space and Lennard-Jones Compute Engines By: David Chui Supervisor: Professor P. Chow.
A Technical Introduction to the MD-OPEP Simulation Tools
DNA structure simulation based on sequence-structure relationship HaYoung Jang
Molecular simulation methods Ab-initio methods (Few approximations but slow) DFT CPMD Electron and nuclei treated explicitly. Classical atomistic methods.
Biological Signal Detection for Protein Function Prediction Investigators: Yang Dai Prime Grant Support: NSF Problem Statement and Motivation Technical.
Molecular Dynamics Simulations of Compressional Metalloprotein Deformation Andrew Hung 1, Jianwei Zhao 2, Jason J. Davis 2, Mark S. P. Sansom 1 1 Department.
Molecular Simulation of Reactive Systems. _______________________________ Sagar Pandit, Hasan Aktulga, Ananth Grama Coordinated Systems Lab Purdue University.
1/20 Study of Highly Accurate and Fast Protein-Ligand Docking Method Based on Molecular Dynamics Reporter: Yu Lun Kuo
Molecular Modelling - Lecture 2 Techniques for Conformational Sampling Uses CHARMM force field Written in C++
Dipole Moments of Diatomics QEq benchmarked against experimental values for 94 molecules Qualitatively correct trend Poor agreement for high bond orders.
Mayur Jain, John Verboncoeur, Andrew Christlieb [jainmayu, johnv, Supported by AFOSR Abstract Electrostatic particle based models Developing.
Role of Theory Model and understand catalytic processes at the electronic/atomistic level. This involves proposing atomic structures, suggesting reaction.
1 Statistical Mechanics and Multi- Scale Simulation Methods ChBE Prof. C. Heath Turner Lecture 20 Some materials adapted from Prof. Keith E. Gubbins:
Molecular dynamics (4) Treatment of long-range interactions Computing properties from simulation results.
Theory of dilute electrolyte solutions and ionized gases
Dynameomics: Protein Mechanics, Folding and Unfolding through Large Scale All-Atom Molecular Dynamics Simulations INCITE 6 David A. C. Beck Valerie Daggett.
1 Targeted Langevin Stabilization of Molecular Dynamics Qun (Marc) Ma and Jesús A. Izaguirre Department of Computer Science and Engineering University.
D. M. Ceperley, 2000 Simulations1 Neighbor Tables Long Range Potentials A & T pgs , Today we will learn how we can handle long range potentials.
ITR/AP: Tools and Methods for Multiscale Biomolecular Simulations PI: Celeste Sagui – DMR NC State, UNC, Duke biomolecular simulations are notoriously.
1 Calculation of Radial Distribution Function (g(r)) by Molecular Dynamic.
Image Charge Optimization for the Reaction Field by Matching to an Electrostatic Force Tensor Wei Song Donald Jacobs University of North Carolina at Charlotte.
A Computational Study of RNA Structure and Dynamics Rhiannon Jacobs and Dr. Harish Vashisth Department of Chemical Engineering, University of New Hampshire,
A Computational Study of RNA Structure and Dynamics Rhiannon Jacobs and Harish Vashisth Department of Chemical Engineering, University of New Hampshire,
Implementation of the TIP5P Potential
Department of Chemistry
FTCS Explicit Finite Difference Method for Evaluating European Options
Algorithms and Software for Large-Scale Simulation of Reactive Systems
Supported by the National Science Foundation.
Molecular simulation methods
Algorithms and Software for Large-Scale Simulation of Reactive Systems
Computer simulation studies of forced rupture kinetics of
New tricks for modelers from the crystallography toolkit: the particle mesh Ewald algorithm and its use in nucleic acid simulations  Tom Darden, Lalith.
Introduction to Scientific Computing II
Presentation transcript:

Evaluation of Fast Electrostatics Algorithms Alice N. Ko and Jesús A. Izaguirre with Thierry Matthey Department of Computer Science and Engineering University of Notre Dame, USA Department of Informatics University of Bergen, NORWAY

Executive summary How to choose the best among Particle Mesh Ewald (PME), Multi-Grid (MG) summation, Ewald sum, for molecular dynamics of biological molecules. Why should your next simulation consider using MG?

Problem: Full Electrostatic Energy

Motivation u Fast evaluation of full electrostatics in molecular dynamics (MD) of biological molecules important for accuracy in many applications n Structural stability of DNA and proteins n Ionic environments u Many methods exist to do explicit evaluation of fast electrostatics n Fast Multipole Method O(N) Greengard, 1987 n Particle Mesh Ewald O(N log N) Darden, 1993 n Multi-grid summation O(N) Brandt, 1990 Skeel, 2002 u Which one to use for a given system and accuracy?

Objectives 1. Provide practical guidelines for choosing parameters for each algorithm 2. Evaluate competitive algorithms 3. Evaluate suitability of MG to MD simulations

Particle Mesh Ewald u Following Ewald, separates the electrostatic interactions into two parts: n Direct-space short range evaluation n Fourier-space evaluation u The Fourier term is approximated by using fast Fourier transforms on a grid u Method parameters are grid size and cutoff of direct-space

Multigrid I

Multigrid II

Related Work I n Darden et al., J. Chem. Phys l effect of varying parameters of Particle Mesh Ewald n Petersen et al., J. Chem. Phys l accuracy and efficiency of Particle Mesh Ewald (PME) n Krasny et al., J. Chem. Phys l used FMM to compute direct part of Ewald sum n Skeel et al., J. Comp. Chem l study of parameters for multigrid (MG) method. Compared MG to Fast Multipole Method (FMM). MG faster than FMM for low accuracy

Related Work II u Most published results n fail to suggest how to determine the specific values n provide general trends only n contain unknown constants in equations that model performance

Summary u General contributions of this study n Practical guidelines for choosing parameters for each algorithm, and to choose among different algorithms l Implemented important algorithms with reasonable efficiency in ProtoMol l Tested algorithms for various system sizes and accuracy l Tested quality of these methods for MD of solvated proteins n Encapsulated results of this study on a tool called MDSimAid

Experimental protocol u These methods were tested and implemented: 1. Smooth Particle Mesh Ewald 2. Multigrid summation 3. Ewald summation u Testing protocol: n Methods (1) and (2) above were compared against (3) to determine accuracy and relative speedup n Tested on water boxes and protein systems ranging from 1,000 to 100,000 atoms, and low and high accuracies n CHARMM used to prepare systems, NAMD and ProtoMol used for simulations n Determined optimal parameters for each method for a given accuracy and system size n For selected protein systems, structural and transport properties were computed (e.g., Melittin, pdb id 2mlt, in water, atoms)

Solvated Melittin

Results u Big picture n Multi-grid summation is an effective method for low accuracy computation of full electrostatics l For low accuracy, Multi-Grid is faster than PME and Ewald for all system sizes tested (from 1000 to 100,000) l For medium accuracy, Multi-Grid is faster than PME for systems of 8,000 atoms or more n Multi-grid with low accuracy produces correct structural and dynamic properties

Results (10 -4 rPE)

Results (10 -5 rPE)

RDF

Multigrid III u Complex relationship among method parameters: n Cutoff and softening distances for potential evaluation at the particle and grid levels n Grid size and interpolation order n Number of levels u Rules extracted from extensive evaluation encapsulated in MDSimAid n Fine tuned at run-time by running selected tests n Makes method easier to use

Simulation Results for Melittin u PME requires about 3% of the CPU time (17 days 20 hours) when measured against Ewald u MG in pbc requires only about 1% u MG is about 66% faster than PME

Discussion u MG is a competitive method for low accuracy MD simulations u Accuracy not a great concern for long time simulations u MG would be natural choice for multiple time stepping integrators u To choose among methods, and good parameters for each method, MDSimAid is a useful tool u For further reference: n n n n

Acknowledgements This research was supported by an NSF Biocomplexity grant and an NSF CAREER award