Amato Nancy M, Dill Ken A, Song Guang
Department of Computer Science, Texas A&M University, College Station, TX 77843-3112, USA.
J Comput Biol. 2003;10(3-4):239-55. doi: 10.1089/10665270360688002.
We investigate a novel approach for studying the kinetics of protein folding. Our framework has evolved from robotics motion planning techniques called probabilistic roadmap methods (PRMs) that have been applied in many diverse fields with great success. In our previous work, we presented our PRM-based technique and obtained encouraging results studying protein folding pathways for several small proteins. In this paper, we describe how our motion planning framework can be used to study protein folding kinetics. In particular, we present a refined version of our PRM-based framework and describe how it can be used to produce potential energy landscapes, free energy landscapes, and many folding pathways all from a single roadmap which is computed in a few hours on a desktop PC. Results are presented for 14 proteins. Our ability to produce large sets of unrelated folding pathways may potentially provide crucial insight into some aspects of folding kinetics, such as proteins that exhibit both two-state and three-state kinetics that are not captured by other theoretical techniques.
我们研究了一种用于研究蛋白质折叠动力学的新方法。我们的框架源自被称为概率地图方法（PRMs）的机器人运动规划技术，该技术已在许多不同领域成功应用。在我们之前的工作中，我们展示了基于PRM的技术，并在研究几种小蛋白质的折叠途径方面取得了令人鼓舞的结果。在本文中，我们描述了如何使用我们的运动规划框架来研究蛋白质折叠动力学。特别是，我们展示了基于PRM框架的改进版本，并描述了如何使用它从单个路线图生成势能景观、自由能景观以及许多折叠途径，该路线图在台式计算机上只需几个小时即可计算出来。文中给出了14种蛋白质的结果。我们生成大量不相关折叠途径的能力可能会为折叠动力学的某些方面提供关键见解，比如那些展现出两态和三态动力学且未被其他理论技术所捕捉的蛋白质。
