Predicting Experimental Quantities in Protein Folding Kinetics Using Stochastic Roadmap Simulation

  • Tsung-Han Chiang
  • Mehmet Serkan Apaydin
  • Douglas L. Brutlag
  • David Hsu
  • Jean-Claude Latombe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3909)


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.


Dynamic Programming Energy Landscape Native Conformation Folding Process Folding Pathway 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tsung-Han Chiang
    • 1
  • Mehmet Serkan Apaydin
    • 2
  • Douglas L. Brutlag
    • 3
  • David Hsu
    • 1
  • Jean-Claude Latombe
    • 3
  1. 1.National University of SingaporeSingaporeSingapore
  2. 2.Dartmouth CollegeHanoverUSA
  3. 3.Stanford UniversityStanfordUSA

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