Stochastic roadmap simulation: an efficient representation and algorithm for analyzing molecular motion

  • Authors:

  • Mehmet Serkan Apaydin;Douglas L. Brutlag;Carlos Guestrin;David Hsu;Jean-Claude Latombe

  • Affiliations:

  • Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA;University of North Carolina at Chapel Hill, Chapel Hill, NC;Stanford University, Stanford, CA

  • Venue:
  • Proceedings of the sixth annual international conference on Computational biology
  • Year:
  • 2002

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Abstract

Classic techniques for simulating molecular motion, such as the Monte Carlo and molecular dynamics methods, generate individual motion pathways one at a time and spend most of their time trying to escape from the local minima of the energy landscape of a molecule. Their high computational cost prevents them from being used to analyze many pathways. We introduce Stochustic Roadmap Sirrrcllation (SRS), a new approach for exploring the kinetics of molecular motion by simultaneously examining multiple pathways encoded compactly in a graph, called a roadmap. A roadmap is computed by sampling a molecule's conformation space at random. The computation does not suffer from the localminima problem encountered with existing methods. Each path in the roadmap represents a potential motion pathway and is associated with a probability indicating the likelihood that the molecule follows this pathway. By viewing the roadmap as a Markov chain, we can efficiently compute kinetic properties of molecular motion over the entire molecular energy landscape. We also prove that, in the limit, SRS converges to the same distribution as Monte Carlo simulation. To test the effectiveness of our approach, we apply it to the computation of the transmission coefficients for protein folding, an important order parameter that measures the "kinetic distance" of a protein's conformation to its native state Our computational studies show that SRS obtains more accurate results and achieves several orders- of- magnitude reduction in computation time, compared with Monte Carlo simulatio.