Stochastic roadmap simulation for the study of ligand-protein interactions

Understanding the dynamics of ligand-protein interactions is indispensable in the design of novel therapeutic agents. In this paper, we establish the use of Stochastic Roadmap Simulation (SRS) for the study of ligand-protein interactions through two studies. In our first study, we measure the effects of mutations on the catalytic site of a protein, a process called computational mutagenesis. In our second study, we focus on distinguishing the catalytic site from other putative binding sites. SRS compactly represents many Monte Carlo (MC) simulation paths in a compact graph structure, or roadmap. Furthermore, SRS allows us to analyze all the paths in this roadmap simultaneously. In our application of SRS to the domain of ligand-protein interactions, we consider a new parameter called escape time, the expected number of MC simulation steps required for the ligand to escape from the 'funnel of attraction' of the binding site, as a metric for analyzing such interactions. Although computing escape times would probably be infeasible with MC simulation, these computations can be performed very efficiently with SRS. Our results for six mutant complexes for the first study and seven ligand-protein complexes for the second study, are very promising: In particular, the first results agree well with the biological interpretation of the mutations, while the second results show that escape time is a good metric to distinguish the catalytic site for five out of seven complexes.

理解配体 - 蛋白质相互作用的动力学在新型治疗药物的设计中是必不可少的。在本文中，我们通过两项研究确立了随机路线图模拟（SRS）在配体 - 蛋白质相互作用研究中的应用。在我们的第一项研究中，我们测量了突变对一种蛋白质催化位点的影响，这一过程被称为计算诱变。在第二项研究中，我们专注于区分催化位点和其他假定的结合位点。SRS在一个紧凑的图结构（即路线图）中紧凑地表示许多蒙特卡罗（MC）模拟路径。此外，SRS使我们能够同时分析该路线图中的所有路径。在我们将SRS应用于配体 - 蛋白质相互作用领域时，我们考虑一个称为逃逸时间的新参数，即配体从结合位点的“吸引漏斗”中逃逸所需的预期MC模拟步数，作为分析此类相互作用的一个度量标准。尽管用MC模拟计算逃逸时间可能不可行，但使用SRS可以非常高效地进行这些计算。我们第一项研究中六个突变复合物以及第二项研究中七个配体 - 蛋白质复合物的结果非常有前景：特别是，第一项研究结果与突变的生物学解释非常吻合，而第二项研究结果表明，对于七个复合物中的五个，逃逸时间是区分催化位点的一个良好度量标准。