Rapid sampling of all-atom peptides using a library-based polymer-growth approach

Artem B. Mamonov, Xin Zhang, Daniel Zuckerman

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

We adapted existing polymer growth strategies for equilibrium sampling of peptides described by modern atomistic forcefields with a simple uniform dielectric solvent. The main novel feature of our approach is the use of precalculated statistical libraries of molecular fragments. A molecule is sampled by combining fragment configurations-of single residues in this study-which are stored in the libraries. Ensembles generated from the independent libraries are reweighted to conform with the Boltzmann-factor distribution of the forcefield describing the full molecule. In this way, high-quality equilibrium sampling of small peptides (4-8 residues) typically requires less than one hour of single-processor wallclock time and can be significantly faster than Langevin simulations. Furthermore, approximate, clash-free ensembles can be generated for larger peptides (up to 32 residues in this study) in less than a minute of single-processor computing. We discuss possible applications of our growth procedure to free energy calculation, fragment assembly protein-structure prediction protocols, and to "multi-resolution" sampling.

Original languageEnglish (US)
Pages (from-to)396-405
Number of pages10
JournalJournal of Computational Chemistry
Volume32
Issue number3
DOIs
StatePublished - Feb 1 2011
Externally publishedYes

Fingerprint

Peptides
Polymers
Fragment
Force Field
Sampling
Atoms
Ensemble
Molecules
Protein Structure Prediction
Multiresolution
Ludwig Boltzmann
Free energy
Free Energy
Proteins
Configuration
Computing
Libraries
Simulation
Strategy

Keywords

  • equilibrium sampling
  • fragment-based
  • library-based
  • peptides
  • polymer-growth

ASJC Scopus subject areas

  • Chemistry(all)
  • Computational Mathematics

Cite this

Rapid sampling of all-atom peptides using a library-based polymer-growth approach. / Mamonov, Artem B.; Zhang, Xin; Zuckerman, Daniel.

In: Journal of Computational Chemistry, Vol. 32, No. 3, 01.02.2011, p. 396-405.

Research output: Contribution to journalArticle

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