Chiang Tsung-Han, Apaydin Mehmet Serkan, Brutlag Douglas L, Hsu David, Latombe Jean-Claude
School of Computing, National University of Singapore, Singapore.
J Comput Biol. 2007 Jun;14(5):578-93. doi: 10.1089/cmb.2007.R004.
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 Phi-values for protein folding. The new method was tested on 16 proteins, whose rates and Phi-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 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 results further validate the SRS method and indicate its potential as a general tool for studying protein folding kinetics.
本文提出了一种研究蛋白质折叠动力学的新方法。它使用最近引入的随机路线图模拟（SRS）方法来估计过渡态系综（TSE），并预测蛋白质折叠的速率和Phi值。该新方法在16种蛋白质上进行了测试，这些蛋白质的速率和Phi值已通过实验确定。与实验数据的比较表明，我们的方法比基于动态规划的现有方法更准确地估计了TSE。这种改进导致了更好的折叠速率预测。我们还计算了未折叠状态的平均首次通过时间，并表明计算值与实验确定的折叠速率相关。Phi值预测的结果好坏参半，可能是由于测试中使用的简单能量模型。这是首次将从SRS获得的结果与大量实验数据进行比较。结果进一步验证了SRS方法，并表明其作为研究蛋白质折叠动力学的通用工具的潜力。
