Using Motion Planning to Study Protein Folding Pathways Susan Lin, Guang Song and Nancy M. Amato Department of Computer Science Texas A&M University

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Using Motion Planning to Study Protein Folding Pathways Susan Lin, Guang Song and Nancy M. Amato Department of Computer Science Texas A&M University

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 Folding Node 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 %: 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