Faculty of Engineering and Natural Sciences
Predicting Experimental Quantities in Protein Folding Kinetics using Stochastic Roadmap Simulation
Mehmet Serkan Apaydın
This paper presents a new method for studying protein folding kinetics. It uses the recently introduced Stochastic Roadmap Simulation (SRS) method to estimate the transition state ensemble (TSE) and predict the rates and phi values for protein folding. The new method was tested on 16 proteins. Comparison with experimental data shows that it estimates the TSE much more accurately than an existing method based on dynamic programming. This leads to better folding-rate predictions. The results on phi value predictions are mixed, possibly due to the simple energy model used in the tests. This is the first time that results obtained from SRS have been compared against a substantial amount of experimental data. The success further validates the SRS method and indicates its potential as a general tool for studying protein folding kinetics.
Joint work with Tsung-Han Chiang, David Hsu, Doug Brutlag and Jean-Claude Latombe.
Mehmet Serkan Apaydın is currently a post-doctoral researcher in Professor Bruce R.Donald's group at Dartmouth College, Computer Science Department. He works on bio-computation, using tools from computer science and engineering to study bio-molecules and processes.
During his Ph.D. he was a graduate student at the Stanford Robotics Lab. His advisors were Prof. Jean-Claude Latombe from Computer Science, and Prof. Doug Brutlag from Biochemistry. He graduated at the end of September 2004. During his Ph.D. he worked on the modeling and study of molecular motion in ligand-protein binding and protein folding.
July 13, 2006, 15:40, FENS G032