Evotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK.
J Comput Aided Mol Des. 2017 Dec;31(12):1073-1083. doi: 10.1007/s10822-017-0089-3. Epub 2017 Nov 30.
Computational generation of conformational ensembles is key to contemporary drug design. Selecting the members of the ensemble that will approximate the conformation most likely to bind to a desired target (the bioactive conformation) is difficult, given that the potential energy usually used to generate and rank the ensemble is a notoriously poor discriminator between bioactive and non-bioactive conformations. In this study an approach to generating a focused ensemble is proposed in which each conformation is assigned multiple rankings based not just on potential energy but also on solvation energy, hydrophobic or hydrophilic interaction energy, radius of gyration, and on a statistical potential derived from Cambridge Structural Database data. The best ranked structures derived from each system are then assembled into a new ensemble that is shown to be better focused on bioactive conformations. This pluralistic approach is tested on ensembles generated by the Molecular Operating Environment's Low Mode Molecular Dynamics module, and by the Cambridge Crystallographic Data Centre's conformation generator software.
计算构象集合的生成是当代药物设计的关键。由于通常用于生成和排序集合的势能是区分生物活性和非生物活性构象的一个众所周知的不良指标，因此很难选择最接近与目标（生物活性构象）结合的构象的集合成员。在这项研究中，提出了一种生成聚焦集合的方法，其中每个构象不仅基于势能，还基于溶剂化能、疏水性或亲水性相互作用能、回转半径以及从剑桥结构数据库数据得出的统计势能，分配多个排名。然后，从每个系统中得出的最佳排名结构被组装成一个新的集合，该集合显示出更好地集中在生物活性构象上。这种多元化的方法在分子操作环境的低模式分子动力学模块和剑桥晶体学数据中心的构象生成软件生成的集合上进行了测试。
