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|Title:||Using Stochastic Roadmap Simulation to predict experimental quantities in protein folding kinetics: Folding rates and phi-values|
Protein folding kinetics
Transition state ensemble
|Citation:||Chiang, T.-H., Apaydin, M.S., Brutlag, D.L., Hsu, D., Latombe, J.-C. (2007). Using Stochastic Roadmap Simulation to predict experimental quantities in protein folding kinetics: Folding rates and phi-values. Journal of Computational Biology 14 (5) : 578-593. ScholarBank@NUS Repository. https://doi.org/10.1089/cmb.2007.R004|
|Abstract:||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 the Φ-values for protein folding. The new method was tested on 16 proteins, whose rates and Φ-values have been determined experimentally. Comparison with experimental data shows that our method estimates the TSE much more accurately than an existing method based on dynamic programming. This improvement leads to better folding-rate predictions. We also compute the mean first passage time of the unfolded states and show that the computed values correlate with experimentally determined folding rates. 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 results further validate the SRS method and indicate its potential as a general tool for studying protein folding kinetics. © Mary Ann Liebert, Inc.|
|Source Title:||Journal of Computational Biology|
|Appears in Collections:||Staff Publications|
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