Solving the sampling problem in molecular simulations by Sequential Monte Carlo

分子模拟是新药物和新材料设计的重要工具，被广泛用于提供原子细节，以增加低分辨率的实验数据。在分子模拟中，系统中的原子根据作用于它们的能量和力而移动，通过检查所采用的分子排列，设计出新的优化分子和材料。例如，通过探索药物与其受体结合的可能几何形状，可以识别和利用新的相互作用，从而产生具有更高亲和力或更好选择性的更好的药物。目前的模拟能够探索这些新的结合几何形状的程度是非常有限的。传统的分子动力学在对特定的结合几何形状进行采样时是非常有效的，但是分离其他可能的结合几何形状的大的动力学屏障意味着这些在模拟中很少被观察到，如果有的话——模拟是短视的和被困住的。对于一些人来说，使用蛮力——买一台更大的电脑——是一种解决方案，但这意味着巨大的财政投资，超出了绝大多数工人的能力。因此，我们需要变得更聪明。有一系列增强的采样算法，试图解决这一问题的不良采样。它们通常以两种方式工作。它们要么减少可能的稳定结合几何之间的能量屏障，使模拟可以在它们之间顺利移动，要么为模拟增加能量，使障碍可以自然地跨越。然而，所有这些方法都有缺点，它们效率低下，需要大量的系统特定优化。这个建议寻求解决抽样问题，通过发展和应用广泛使用的抽样程序从统计学-顺序蒙特卡罗。这种方法具有通用性和适应性。在这样做的过程中，这个高风险和冒险的项目将提供强大的新分子模拟方法，以改变新分子和材料的发现。分子smc是自适应的，高效的，并且不需要预先知道蛋白质配体系统的详细结构重排。在这里，该方法将被开发并应用于药物发现中的两个紧迫问题——蛋白质-配体对接，其中可变水合作用的特殊问题将被解决，以及在更严格的结合自由能计算领域，对配体的细微修改可以带来结合几何结构的实质性变化。

Molecular simulations are an essential tool in the design of new drugs and materials, and are widely used to provide atomistic detail to augment low-resolution experimental data. In a molecular simulation, the atoms in the system move in response to the energy and forces acting on them, and by examining the molecular arrangements adopted, new optimised molecules and materials are designed. For example, by exploring possible geometries of a drug binding to its receptor, new interactions may be identified and exploited, leading to better drugs with higher affinity or better selectivity.The extent to which current simulations are able to explore these new binding geometries is very limited. Conventional molecular dynamics is very efficient at sampling a particular binding geometry, but the large kinetic barriers separating other possible binding geometries mean that these are seldom observed in the simulations, if at all - the simulation is myopic and trapped. Brute force - getting a bigger computer - is a solution for some, but this represents a massive financial investment that is beyond the capability of the overwhelming majority of workers. We therefore need to be smarter. There are a range of enhanced sampling algorithms, which seek to solve this problem of poor sampling. They typically work in one of two ways. They either reduce the energy barrier between the possible stable binding geometries, so that the simulation can smoothly move between them, or they add energy to the simulation, so that the barriers may be crossed naturally. However, all these methods suffer from disadvantages that make them inefficient and requiring considerable system-specific optimisation.This proposal seeks to solve the sampling problem, by developing and applying a widely used sampling procedure from statistics - Sequential Monte Carlo. This approach will be general and adaptable. In doing so this high-risk and adventurous project will deliver robust new molecular simulation methodology to transform the discovery of new molecules and materials.Molecular-SMC is adaptive, efficient, and free of the need to know a priori the detailed structural rearrangements of protein-ligand systems. Here the method will be developed and applied to two pressing problems in drug discovery - in protein-ligand docking, where the particular problem of variable hydration will be addressed, and in the more rigorous area of binding free energy calculations, where subtle modifications to the ligand can bring about substantial changes in binding geometry.