Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures.
We investigate a novel approach for studying the kinetics of protein folding. Our framework has evolved from robotics motion planning techniques called probabilistic roadmap methods (PRMs) that have been applied in many diverse fields with great success. In our previous work, we presented our PRM-based technique and obtained encouraging results studying protein folding pathways for several small proteins. In this paper, we describe how our motion planning framework can be used to study protein folding kinetics. In particular, we present a refined version of our PRM-based framework and describe how it can be used to produce potential energy landscapes, free energy landscapes, and many folding pathways all from a single roadmap which is computed in a few hours on a desktop PC. Results are presented for 14 proteins. Our ability to produce large sets of unrelated folding pathways may potentially provide crucial insight into some aspects of folding kinetics, such as proteins that exhibit both two-state and three-state kinetics that are not captured by other theoretical techniques.
Amato
Nancy M
NM
Department of Computer Science, Texas A&M University, College Station, TX 77843-3112, USA. amato@cs.tamu.edu
Dill
Ken A
KA
Song
Guang
G
D019295:Computational Biology; D007700:Kinetics; D017510:Protein Folding