Using distance geometry algorithms to derive molecular conformational ensembles

动机：当配体(如药物分子)与蛋白质的结合部位对接时，两个分子都会发生构象变化。这些变化很难预测，这使最终组合结构的必要几何建模变得复杂。为了简化分析，药物设计者可能会做出过于乐观的假设，认为结合位置是刚性的，只考虑了配体的灵活性。一种更复杂的分析涉及配体和结合部位的灵活性：我们认为结合部位的构象变化与对应于蛋白质最低能量构象的结构有微小的偏离。这一“诱导契合假说”为对接事件提供了更真实的解释。然而，这并不是完全足够的，有许多蛋白质-配体复合体，其中的结合位置经历了广泛的构象变化。最近，不同的研究人员设计了更复杂的几何描述，以努力更全面地捕捉结合事件的复杂性。这些研究假设蛋白质通常处于低能构象，但由于热搅动或拥挤，它可能偶尔会采用不同的构象，对应于附近“折叠漏斗”底部的局部能量最小值。与全球最小的能量相比，这种构象的能量只会稍微大一点。由于很难收集为每一种未知构象提供足够样本的训练数据，这种“构象集合”的物理存在极大地复杂化了为药物设计工作建立预测机器学习算法的任何尝试。我们的目标是研究结合位点的几何建模，目的是设计生成构象系综的算法，作为配体对接的前奏。 意义：总体而言，这项研究将为制药业提供重要的实用价值，因为阐明交替结合位点构象可以导致设计新型药物“支架”的算法，这将导致新的专利地位。这项研究在评估药物副作用方面也很重要。

Motivation: When a ligand (such as a drug molecule) docks with the binding site of a protein, both molecules undergo conformational changes. These changes are difficult to predict and this complicates the necessary geometric modeling of the final combined structure. To simplify the analysis, a drug designer may make the over-optimistic assumption that the binding site is rigid and flexibility is only considered for the ligand. A more complicated analysis deals with the flexibility of both the ligand and the binding site: We consider conformational changes in the binding site to be small deviations from the structure that corresponds to the lowest energy conformation of the protein. This "induced fit hypothesis" provides a more realistic account of the docking event. Nonetheless, it is not fully adequate and there are many protein-ligand complexes in which the binding site has undergone extensive conformational changes. Recently, various researchers have devised more complicated geometrical descriptions in an effort to more fully capture the complexity of a binding event. These studies assume that the protein is usually in a low energy conformation but, due to thermal agitation or crowding, it may occasionally adopt a different conformation that corresponds to a nearby local energy minimum at the bottom of the "folding funnel". This conformation would have an energy that is only slightly larger when compared with the energy of the global minimum. The physical presence of this "conformational ensemble" greatly complicates any attempt to build predictive machine learning algorithms for drug design efforts because it is difficult to collect training data that provides enough samples for each of the unknown conformations. Our goal is to study the geometrical modeling of binding sites with the goal of devising algorithms for the generation of conformational ensembles as a prelude to ligand docking. Significance: Overall, the research will provide a significant utility for the pharmaceutical industry because the elucidation of alternate binding site conformations can lead to algorithms that design novel drug "scaffolds" and this leads to new patent positions. The research is also important in the assessment of drug side effects.