Solving the sampling problem in molecular simulations by Sequential Monte Carlo

分子模拟是设计新药物和新材料的重要工具，被广泛用于提供原子细节，以增强低分辨率的实验数据。在分子模拟中，系统中的原子随着作用在它们身上的能量和力而运动，通过检查所采用的分子排列，设计出新的优化的分子和材料。例如，通过探索药物与其受体结合的可能几何结构，可以识别和利用新的相互作用，导致更好的药物具有更高的亲和力或更好的选择性。目前的模拟能够探索这些新的结合几何结构的程度非常有限。传统的分子动力学在采样特定的结合几何图形方面非常有效，但分隔其他可能的结合几何图形的巨大动力学势垒意味着，即使在模拟中观察到这些，也很少-模拟是短视和陷阱的。暴力--得到一台更大的电脑--对一些人来说是一个解决方案，但这代表着一项巨大的财务投资，超出了绝大多数工人的能力。因此，我们需要更聪明。有一系列增强的采样算法，它们试图解决采样不佳的问题。它们通常以两种方式之一发挥作用。它们可以减少可能稳定的绑定几何图形之间的能量障碍，以便模拟可以在它们之间平滑移动，或者它们增加了模拟的能量，从而可以自然地跨越障碍。然而，所有这些方法都有缺点，使它们的效率低下，需要相当大的系统特定优化。这项建议试图通过开发和应用统计学中广泛使用的抽样程序-序贯蒙特卡罗来解决抽样问题。这种方法将是通用的和可适应的。通过这样做，这个高风险和冒险的项目将提供强大的新分子模拟方法来改变新分子和材料的发现。分子SMC是自适应的、高效的，并且不需要事先知道蛋白质-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，其中将解决可变水化的特定问题，以及在更严格的结合自由能计算领域，其中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.