Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures

Nancy M. Amato, Ken A. Dill, Guang Song

Research output: Contribution to conferencePaperpeer-review

Abstract

We present a novel approach for studying the kinetics of protein folding. The framework has evolved from robotics motion planning techniques called probabilistic roadmap methods (PRMs) that have been applied in many diverse fields with great success. In our previous work, we used a PRM-based technique to study protein folding pathways of several small proteins and obtained encouraging results. In this paper, we describe how our motion planning framework can be used to study protein folding kinetics. In particular, we present a refined version of our PRM-based framework and describe how it can be used to produce potential energy landscapes, free energy landscapes, and many folding pathways all from a single roadmap which is computed in a few hours on a desktop PC. Results are presented for 14 proteins. Our ability to produce large sets of unrelated folding pathways may potentially provide crucial insight into some aspects of folding kinetics, such as proteins that exhibit both two-state and three-state kinetics, that are not captured by other theoretical techniques.

Original languageEnglish (US)
Pages2-11
Number of pages10
DOIs
StatePublished - 2002
Externally publishedYes
EventRECOMB 2002: Proceedings of the Sixth Annual International Conference on Computational Biology - Washington, DC, United States
Duration: Apr 18 2002Apr 21 2002

Other

OtherRECOMB 2002: Proceedings of the Sixth Annual International Conference on Computational Biology
Country/TerritoryUnited States
CityWashington, DC
Period4/18/024/21/02

ASJC Scopus subject areas

  • General Computer Science
  • General Biochemistry, Genetics and Molecular Biology

Fingerprint

Dive into the research topics of 'Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures'. Together they form a unique fingerprint.

Cite this