Solving the sampling problem in molecular simulations by Sequential Monte Carlo

用顺序蒙特卡罗解决分子模拟中的采样问题

• 批准号: EP/V048864/1
• 负责人: Jonathan Essex
• 金额: $ 25.68万
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV048864%2F1
• 关键词: Solving; sampling; problem; molecular; simulations

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.

分子模拟是设计新药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实验数据。在分子模拟中，系统中的原子响应于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药物。传统的分子动力学在采样特定的结合几何形状时非常有效，但是分隔其他可能的结合几何形状的大的动力学障碍意味着这些在模拟中很少被观察到，如果有的话-模拟是近视的和被困的。蛮力-获得更大的计算机-是一个解决方案，为一些，但这代表了一个巨大的财政投资，是超出了绝大多数工人的能力。因此，我们需要更聪明。存在一系列增强的采样算法，其寻求解决这种采样差的问题。它们通常以两种方式之一工作。它们要么减少可能的稳定结合几何结构之间的能量势垒，使得模拟可以在它们之间平滑地移动，要么它们向模拟添加能量，使得可以自然地跨越势垒。然而，所有这些方法都有缺点，使它们效率低下，需要相当多的系统特定的optimization.This建议旨在解决抽样问题，通过开发和应用广泛使用的抽样程序从统计-序贯蒙特卡罗。这种方法将是通用的和可适应的。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，不需要事先知道蛋白质-配体系统的详细结构重排。在这里，该方法将被开发和应用到两个紧迫的问题，在药物发现-在蛋白质-配体对接，其中变量水化的特定问题将得到解决，并在更严格的领域的结合自由能计算，其中微妙的修改的配体可以带来实质性的变化结合几何形状。

项目成果

Enhanced Grand Canonical Sampling of Occluded Water Sites Using Nonequilibrium Candidate Monte Carlo.

• DOI: 10.1021/acs.jctc.2c00823
• 发表时间: 2023-02-14
• 期刊: JOURNAL OF CHEMICAL THEORY AND COMPUTATION
• 影响因子: 5.5
• 作者: Melling, Oliver J.;Samways, Marley L.;Ge, Yunhui;Mobley, David L.;Essex, Jonathan W.
• 通讯作者: Essex, Jonathan W.

Enhancing Ligand and Protein Sampling Using Sequential Monte Carlo.

使用顺序的蒙特卡洛增强配体和蛋白质采样。

• DOI: 10.1021/acs.jctc.1c01198
• 发表时间: 2022-06-14
• 期刊: JOURNAL OF CHEMICAL THEORY AND COMPUTATION
• 影响因子: 5.5
• 作者: Suruzhon, Miroslav;Bodnarchuk, Michael S.;Ciancetta, Antonella;Wall, Ian D.;Essex, Jonathan W.
• 通讯作者: Essex, Jonathan W.

Supporting scripts and data for paper titled "Enhancing Torsional Sampling Using Fully Adaptive Simulated Tempering"

支持题为“使用完全自适应模拟回火增强扭转采样”的论文的脚本和数据

• DOI: 10.5281/zenodo.7688832
• 发表时间: 2023
• 作者: Suruzhon M