Using Motion Planning to Study Protein Folding Pathways

Susan Lin, Guang Song and Nancy M. AmatoDepartment of Computer Science

Texas A&M University

http://www.cs.tamu.edu/faculty/amato/

Protein folding is a “grand challenge” problem in biology - the deciphering of the second half of the genetic code, of pressing practical significance

Problem 1: given a protein’s amino acid sequence, predict its 3D structure, which is related to its function

Problem 2: “… use the protein’s known 3D structure to predict the kinetics and mechanism of folding” [Munoz & Eaton, PNAS’99]

–Finding protein folding pathways - OUR FOCUS - will assist in understanding folding and function, and eventually may lead to prediction.

Protein Folding

PRMs for Protein FoldingNode Generation [Singh,Latombe,Brutleg 99]

• randomly generate conformations (determine all atoms’ coordinates)• compute potential energy E of conformation and retain node with probability P(E):

Querying the Roadmap• Add start (extended conformation) and goal (native fold) to the roadmap•Extract smallest weight path (energetically most feasible)

Roadmap Connection• find k closest nodes to each roadmap node• calculate weight of straightline path between node pairs - weight reflects the probability of moving between nodes (the smaller the weight the lower the energy)

Validating Folding Pathways

Protein GB1 (56 amino acids)— 1 alpha helix & 4 beta-strands

Hydrogen Exchange Results first helix, and beta-4 & beta-3

Our Paths 60%: helix, beta 3-4, beta 1-2, beta 1-440%: helix, beta 1-2, beta 3-4, beta 1-4

hypothetical roadmap for Protein A

‘funnel’ for RMSD< 10 A, suggests packing of secondary structure (similar potentials)

Protein A:Potential Energy vs. RMSD for roadmap nodes

goal: native fold

funnel

start: amino acid string