A molecular simulation protocol to avoid sampling redundancy and discover new states

For biomacromolecules or their assemblies, experimental knowledge is often restricted to specific states. Ambiguity pervades simulations of these complex systems because there is no prior knowledge of relevant phase space domains, and sampling recurrence is difficult to achieve. In molecular dynamic...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Biochimica et biophysica acta Jg. 1850; H. 5; S. 889 - 902
Hauptverfasser: Bacci, Marco, Vitalis, Andreas, Caflisch, Amedeo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 01.05.2015
Schlagworte:
Algorithms
Biomolecules
Enhanced sampling
Gibbs free energy
Kinetics
Molecular dynamics
Molecular Dynamics Simulation
peptides
Protein Stability
Protein Structure, Secondary
Proteins - chemistry
Proteins - metabolism
Reproducibility of Results
Scalable algorithm
Simulation convergence
Structure-Activity Relationship
Thermodynamics
Transition path
PIGS
REX
MSM
Transition path
Simulation convergence
Molecular dynamics
RMSD
Enhanced sampling
PCA
CS
Scalable algorithm
MC
MD
Biomolecules
CLD
PES
ISSN:0304-4165, 0006-3002, 1872-8006
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:For biomacromolecules or their assemblies, experimental knowledge is often restricted to specific states. Ambiguity pervades simulations of these complex systems because there is no prior knowledge of relevant phase space domains, and sampling recurrence is difficult to achieve. In molecular dynamics methods, ruggedness of the free energy surface exacerbates this problem by slowing down the unbiased exploration of phase space. Sampling is inefficient if dwell times in metastable states are large. We suggest a heuristic algorithm to terminate and reseed trajectories run in multiple copies in parallel. It uses a recent method to order snapshots, which provides notions of “interesting” and “unique” for individual simulations. We define criteria to guide the reseeding of runs from more “interesting” points if they sample overlapping regions of phase space. Using a pedagogical example and an α-helical peptide, the approach is demonstrated to amplify the rate of exploration of phase space and to discover metastable states not found by conventional sampling schemes. Evidence is provided that accurate kinetics and pathways can be extracted from the simulations. The method, termed PIGS for Progress Index Guided Sampling, proceeds in unsupervised fashion, is scalable, and benefits synergistically from larger numbers of replicas. Results confirm that the underlying ideas are appropriate and sufficient to enhance sampling. In molecular simulations, errors caused by not exploring relevant domains in phase space are always unquantifiable and can be arbitrarily large. Our protocol adds to the toolkit available to researchers in reducing these types of errors. This article is part of a Special Issue entitled “Recent Developments of Molecular Dynamics.” •We develop a numerical method to explore the conformational space of biomolecules.•It is scalable, unsupervised, and utilizes computational resources efficiently.•We achieve a faster detection of metastable states than with other methods.•Connectivity and dynamical pathways between molecular structures are preserved.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0304-4165
0006-3002
1872-8006
DOI:10.1016/j.bbagen.2014.08.013