Research in Computational Molecular Biology

Volume 3909 of the series Lecture Notes in Computer Science pp 410-424

Predicting Experimental Quantities in Protein Folding Kinetics Using Stochastic Roadmap Simulation

  • Tsung-Han ChiangAffiliated withNational University of Singapore
  • , Mehmet Serkan ApaydinAffiliated withDartmouth College
  • , Douglas L. BrutlagAffiliated withStanford University
  • , David HsuAffiliated withNational University of Singapore
  • , Jean-Claude LatombeAffiliated withStanford University

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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 Φ-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 Φ-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.