scispace - formally typeset
Proceedings ArticleDOI

Copernicus: a new paradigm for parallel adaptive molecular dynamics

TLDR
A new paradigm for parallel adaptive molecular dynamics and a publicly available implementation: Copernicus, which combines performance-leading molecular dynamics parallelized on three levels (SIMD, threads, and message-passing) with kinetic clustering, statistical model building and real-time result monitoring.
Abstract
Biomolecular simulation is a core application on supercomputers, but it is exceptionally difficult to achieve the strong scaling necessary to reach biologically relevant timescales. Here, we present a new paradigm for parallel adaptive molecular dynamics and a publicly available implementation: Copernicus. This framework combines performance-leading molecular dynamics parallelized on three levels (SIMD, threads, and message-passing) with kinetic clustering, statistical model building and real-time result monitoring. Copernicus enables execution as single parallel jobs with automatic resource allocation. Even for a small protein such as villin (9,864 atoms), Copernicus exhibits near-linear strong scaling from 1 to 5,376 AMD cores. Starting from extended chains we observe structures 0.6 A from the native state within 30h, and achieve sufficient sampling to predict the native state without a priori knowledge after 80--90h. To match Copernicus' efficiency, a classical simulation would have to exceed 50 microseconds per day, currently infeasible even with custom hardware designed for simulations.

read more

Citations
More filters
Book ChapterDOI

Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS

TL;DR: GROMACS as mentioned in this paper is a widely used package for biomolecular simulation, and over the last two decades it has evolved from small-scale efficiency to advanced heterogeneous acceleration and multi-level parallelism targeting some of the largest supercomputers in the world.
Journal ArticleDOI

HTMD: High-Throughput Molecular Dynamics for Molecular Discovery

TL;DR: HTMD, a programmable, extensible platform written in Python that aims to solve the data generation and analysis problem as well as increase reproducibility by providing a complete workspace for simulation-based discovery, is presented.
Journal ArticleDOI

On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations.

TL;DR: An automatic, iterative, on-the-fly method for learning and sampling molecular simulations in the context of ligand binding for the case of trypsin-benzamidine binding is demonstrated, achieving a converged binding affinity in approximately one microsecond, 1 order of magnitude faster than classical sampling.
Journal ArticleDOI

Automatic GROMACS topology generation and comparisons of force fields for solvation free energy calculations

TL;DR: A new framework is presented that enables fully automated generation of GROMACS topologies for any of these force fields and an automated setup for parallel adaptive optimization of high-throughput free energy calculation by adjusting lambda point placement on the fly.
Journal ArticleDOI

Evaluating the Effects of Cutoffs and Treatment of Long-range Electrostatics in Protein Folding Simulations

TL;DR: The results show that the free energy of folding is relatively insensitive to the choice of cutoff beyond 9 Å, and it is found that the structural properties of the unfolded state depend more strongly on the two approximations examined here.
References
More filters
Journal ArticleDOI

GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation

TL;DR: A new implementation of the molecular simulation toolkit GROMACS is presented which now both achieves extremely high performance on single processors from algorithmic optimizations and hand-coded routines and simultaneously scales very well on parallel machines.
Journal ArticleDOI

Understanding Molecular Simulation

Daan Frenkel, +1 more
- 01 Oct 2001 - 
Journal ArticleDOI

A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations.

TL;DR: A third‐generation point‐charge all‐atom force field for proteins is developed and initial tests on peptides demonstrated a high‐degree of similarity between the calculated and the statistically measured Ramanchandran maps for both Ace‐Gly‐nme and Ace‐Ala‐Nme di‐peptides.
Book

A Guide to Monte Carlo Simulations in Statistical Physics

TL;DR: A review of Monte Carlo methods of computer simulation can be found in this article, where a brief review of other methods of simulation can also be found, as well as a brief introduction to Monte Carlo studies of biological molecules.
Journal ArticleDOI

Atomic-Level Characterization of the Structural Dynamics of Proteins

TL;DR: Simulation of the folding of a WW domain showed a well-defined folding pathway and simulation of the dynamics of bovine pancreatic trypsin inhibitor showed interconversion between distinct conformational states.
Related Papers (5)