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. A key feature of our method is that it provides a sparse map that captures the main features of the energy landscape which can be analyzed to compute folding kinetics. Our method is based on probabilistic roadmap motion planners that we have previously successfully applied to protein folding. 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 results of other existing methods.
Tang
Xinyu
X
Parasol Lab, Dept. of Computer Science, Texas A&M University, 301 Harvey R. Bright Building, College Station, TX 77843-3112, USA.
Kirkpatrick
Bonnie
B
Thomas
Shawna
S
Song
Guang
G
Amato
Nancy M
NM
D019295:Computational Biology; D007700:Kinetics; D008954:Models, Biological; D008956:Models, Chemical; D009690:Nucleic Acid Conformation; D012313:RNA / Q000737:chemistry* / Q000378:metabolism*; D013816:Thermodynamics
D012313:RNA