Parallel protein folding with STAPL

Shawna Thomas, Gabriel Tanase, Lucia K. Dale, Jose M. Moreira, Lawrence Rauchwerger, Nancy M. Amato

Research output: Contribution to journalArticle

Abstract

The protein-folding problem is a study of how a protein dynamically folds to its so-called native state-an energetically stable, three-dimensional conformation. Understanding this process is of great practical importance since some devastating diseases such as Alzheimer's and bovine spongiform encephalopathy (Mad Cow) are associated with the misfolding of proteins. We have developed a new computational technique for studying protein folding that is based on probabilistic roadmap methods for motion planning. Our technique yields an approximate map of a protein's potential energy landscape that contains thousands of feasible folding pathways. We have validated our method against known experimental results. Other simulation techniques, such as molecular dynamics or Monte Carlo methods, require many orders of magnitude more time to produce a single, partial trajectory. In this paper we report on our experiences parallelizing our method using STAPL (Standard Template Adaptive Parallel Library) that is being developed in the Parasol Lab at Texas A&M. An efficient parallel version will enable us to study larger proteins with increased accuracy. We demonstrate how STAPL enables portable efficiency across multiple platforms, ranging from small Linux clusters to massively parallel machines such as IBM's BlueGene/L, without user code modification.

Original languageEnglish (US)
Pages (from-to)1643-1656
Number of pages14
JournalConcurrency Computation Practice and Experience
Volume17
Issue number14
DOIs
StatePublished - Dec 10 2005
Externally publishedYes

Keywords

  • C++
  • Motion planning
  • Parallel libraries
  • Protein folding

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics

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