Apaydin Mehmet Serkan, Brutlag Douglas L, Guestrin Carlos, Hsu David, Latombe Jean-Claude, Varma Chris
Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA.
J Comput Biol. 2003;10(3-4):257-81. doi: 10.1089/10665270360688011.
Classic molecular motion simulation techniques, such as Monte Carlo (MC) simulation, generate motion pathways one at a time and spend most of their time in the local minima of the energy landscape defined over a molecular conformation space. Their high computational cost prevents them from being used to compute ensemble properties (properties requiring the analysis of many pathways). This paper introduces stochastic roadmap simulation (SRS) as a new computational approach for exploring the kinetics of molecular motion by simultaneously examining multiple pathways. These pathways are compactly encoded in a graph, which is constructed by sampling a molecular conformation space at random. This computation, which does not trace any particular pathway explicitly, circumvents the local-minima problem. Each edge in the graph represents a potential transition of the molecule and is associated with a probability indicating the likelihood of this transition. By viewing the graph as a Markov chain, ensemble properties can be efficiently computed over the entire molecular energy landscape. Furthermore, SRS converges to the same distribution as MC simulation. SRS is applied to two biological problems: computing the probability of folding, an important order parameter that measures the "kinetic distance" of a protein's conformation from its native state; and estimating the expected time to escape from a ligand-protein binding site. Comparison with MC simulations on protein folding shows that SRS produces arguably more accurate results, while reducing computation time by several orders of magnitude. Computational studies on ligand-protein binding also demonstrate SRS as a promising approach to study ligand-protein interactions.
经典的分子运动模拟技术，如蒙特卡罗（MC）模拟，每次生成一条运动路径，并且大部分时间都花费在由分子构象空间定义的能量景观的局部最小值处。其高昂的计算成本使其无法用于计算系综性质（需要分析多条路径的性质）。本文介绍了随机路线图模拟（SRS），作为一种通过同时检查多条路径来探索分子运动动力学的新计算方法。这些路径被紧凑地编码在一个图中，该图是通过随机采样分子构象空间构建的。这种计算不明确追踪任何特定路径，从而规避了局部最小值问题。图中的每条边代表分子的一个潜在转变，并与一个表示该转变可能性的概率相关联。通过将该图视为马尔可夫链，可以在整个分子能量景观上高效地计算系综性质。此外，SRS收敛到与MC模拟相同的分布。SRS被应用于两个生物学问题：计算折叠概率，这是一个重要的序参量，用于衡量蛋白质构象与其天然状态的“动力学距离”；以及估计从配体 - 蛋白质结合位点逃逸的预期时间。与蛋白质折叠的MC模拟比较表明，SRS产生的结果可能更准确，同时将计算时间减少了几个数量级。对配体 - 蛋白质结合的计算研究也证明SRS是研究配体 - 蛋白质相互作用的一种有前途的方法。
