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Importance sampling of chemical compound space: Thermodynamic properties from high-throughput coarse-grained simulations

化合物空间的重要性采样：高通量粗粒度模拟的热力学性质

基本信息

批准号：
285228850
负责人：
Dr. Tristan Bereau
金额：
--
依托单位：
Max-Planck-Institut für Polymerforschung
依托单位国家：
德国
项目类别：
Independent Junior Research Groups
财政年份：
2016
资助国家：
德国
起止时间：
2015-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/285228850?language=en
关键词：
Importance sampling chemical compound space

项目摘要

The computer-assisted rational design of small molecules to optimize the function of a biomolecular process is a challenging endeavor that holds many promises. In this context, the use of computer simulations is still impinged by computational resources, limiting the typical number of compounds within a single project to 10-100. The underlying concept is the exploration of chemical space, and how molecular structures impact the desired function. A more systematic approach to the problem necessarily requires much larger numbers of molecules. This project aims at developing the realm of high-throughput biomolecular simulations to evaluate free energies of a large number of compounds subject to a certain environment. The goal is made possible by the combination of two complementary techniques. First, coarse-grained modeling alleviates the computational investment of each compound, as well as the size of chemical space. The parallel between coarse-graining's role as the means to reduce both phase space and chemical space is highlighted, suggesting a promising route to more systematically explore the variety of molecular structures. Further, the introduction of an importance-sampling scheme helps the directed evolution of small molecules to optimize a given function. The novel methodology is applied to two biomolecular scenarios: small-molecule insertion in the membrane (i.e., anaesthetics) and protein-ligand binding.

计算机辅助小分子的合理设计以优化生物分子过程的功能是一项具有挑战性的努力，拥有许多希望。在这种情况下，计算机模拟的使用仍然受到计算资源的影响，将单个项目中的典型化合物数量限制在10-100。潜在的概念是化学空间的探索，以及分子结构如何影响期望的功能。解决这个问题的更系统的方法必然需要更多的分子。本项目旨在发展高通量生物分子模拟领域，以评估受一定环境影响的大量化合物的自由能。这一目标是通过两种互补技术的结合而实现的。首先，粗粒度建模减轻了每种化合物的计算投入，以及化学空间的大小。强调了粗粒化作为减少相空间和化学空间的手段之间的平行作用，为更系统地探索分子结构的多样性提供了一条有希望的途径。此外，引入重要采样方案有助于小分子的定向进化以优化给定函数。新方法应用于两种生物分子场景：小分子插入膜（即麻醉剂）和蛋白质配体结合。