Using distance geometry algorithms to derive molecular conformational ensembles

点击翻译按钮获取中文摘要

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.