Using motion planning to study RNA folding kinetics

We propose a novel, motion planning based approach to approximately map the energy landscape of an RNA molecule. Our method is based on the successful probabilistic roadmap motion planners that we have previously successfully applied to protein folding. The key advantage of our method is that it provides a sparse map that captures the main features of the landscape and which can be analyzed to compute folding kinetics. In this paper, we provide evidence that this approach is also well suited to RNA. We compute population kinetics and transition rates on our roadmaps using the master equation for a few moderately sized RNA and show that our results compare favorably with existing methods. This research supported in part by NSF Grants ACI-9872126, EIA-9975018, EIA-0103742, EIA-9805823, ACR-0081510, ACR0113971, CCR-0113974, EIA-9810937, EIA-0079874, and by the DOE. Research supported in part by the CRA Distributed Mentor Project. Dept. Computer Science, Montana State Univ., Bozeman, MT...

• Type: Conference
• Year: 2004
• Where: RECOMB
• Authors: Xinyu Tang, Bonnie Kirkpatrick, Shawna L. Thomas, Guang Song, Nancy M. Amato
• Keywords: Computational Biology, CRA Distributed Mentor, Montana State Univ, RECOMB 2004, Successful Probabilistic Roadmap