| Stochastic Roadmap Simulation: An Efficient Representation and Algorithm for Analyzing Molecular Motion (2003) | |||||||||||||||||
Abstract | |||||||||||||||||
| Classic molecular motion simulation techniques, such as Monte Carlo (MC) simulation, generate motion pathways one at a time and spend most of their time in the local minima of the energy landscape defined over a molecular conformation space. Their high computational cost prevents them from being used to compute ensemble properties; properties requiring the analysis of many pathways. This paper introduces Stochastic Roadmap Simulation (SRS) as a new computational approach for exploring the kinetics of molecular motion by simultaneously examining multiple pathways. These pathways are compactly encoded in a graph, which is constructed by sampling a molecular conformation space at random. This computation, which does not trace any particular pathway explicitly, circumvents the local-minima problem. Each edge in the graph represents a potential transition of the molecule and is associated with a probability indicating the likelihood of this transition. By viewing the graph as a Department of Electrical Engineering, Stanford University, Stanford CA 94305 Department of Biochemistry, Stanford University, Stanford CA 94305 Department of Computer Science, Stanford University, Stanford CA 94305 Department of Computer Science, National University of Singapore, Singapore corresponding author. e-mail : latombe@cs.stanford.edu Address: Department of Computer Science, Stanford University, Stanford CA 94305 Phone: (650) 723-0350 Fax: (650) 725-1449 Department of EECS, MIT and Department of HST, Harvard Medical School, Cambridge, MA 02138 Markov chain, ensemble properties can be efficiently computed over the entire molecular energy landscape. | |||||||||||||||||
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