Optimizing and Learning Strategies for Protein Docking

蛋白质对接的定义是预测对接的复合体的三维结构 了解组件的结构。用于此目的的实验技术通常是昂贵的， 这很耗时，而且在某些情况下是不可行的；因此需要计算性对接方法。这个 寻找对接构象的问题通常被表示为基于能量的得分的最小化 功能。这个函数是由多个能量项组成的，这些能量项在不同的空间尺度上起作用并表现出 多频率行为导致了大量的局部极小值。此外，这一过程 对接/结合涉及组成分子的构象变化，从而导致高度复杂的搜索 优化问题的空间。这些特点使得优化问题变得极其困难。 大多数最先进的对接协议采用多阶段和多尺度的方法。它们以一个字母开头 使用简化的评分函数对构象空间进行全局搜索，以识别 空间，然后使用更详细和完整的评分函数进行局部优化，以消除碰撞。在……里面 最后一个所谓的改进阶段，前两个阶段中发现的有希望的领域将使用 中等空间规模的搜索，以提供一套最终的解决方案。最近有迹象表明，由于 计分函数/能量势不准确时，上述优化阶段总是产生 最后阶段的假阳性数，即分数较低但远离原始构象的1个构象 构象。这促使在这项建议中引入了将能量和 其他特征，以便在精炼阶段对构象簇进行排序并改进最终解决方案。 该提议有两个不同的主旨：优化和学习。在优化方面，项目团队 在过去的研究中，对接问题定义为流形上的优化问题。在这个项目中，两部小说 流形优化公式中的元素被引入，预计将导致显著的 对接算法性能的改进。在学习方面，使用新的稳健优化 技术，一种新的更严格的稳健回归、分类和离群值检测方法，是 引入是为了(I)在改进阶段获得更好的集群排名，以及(Ii)解决 区分粘结剂和非粘结剂的重要问题。 该项目旨在提高用于预测是否以及如果是这样的计算对接的性能 蛋白质如何相互作用以及与小分子相互作用。理解和预测蛋白质-蛋白质 而蛋白质-小分子相互作用是合理药物设计过程中的重要组成部分。更多 因此，有效的蛋白质对接算法有望改善合理的药物设计过程。

Protein docking is defined as predicting the three-dimensional structure of the docked complex based on knowledge of the structure of the components. Experimental techniques for this purpose are often expensive, time-consuming, and in some cases, not feasible; hence the need for computational docking methods. The problem of finding the docked conformation is generally formulated as a minimization of an energy-based scoring function. This function is composed of multiple energy terms that act in different space scales and demonstrate multi-frequency behavior leading to an enormous number of local minima. Furthermore, the process of docking/binding involves conformational changes to the component molecules leading to a highly complex search space for the optimization problem. These features render the optimization problem extremely difficult. Most state-of-the art docking protocols employ a multi-stage and multi-scale approach. They begin with a global search of the conformational space using a simplified scoring function to identify promising areas of the space, followed by local optimization using a more detailed and complete scoring function to remove clashes. In the final so-called refinement stage, promising areas found in the first two stages are explored further using a medium space-scale search to provide a set of final solutions. It has recently become evident that due to the inaccuracy of the scoring function/energy potentials, the optimization stage outlined above invariably generates a number of false positives at the final phase, namely1 conformations that have low score but are far from the native conformation. This motivates the introduction in this proposal of learning methods that combine energy with additional features in order to rank clusters of conformations at the refinement stage and improve final solutions. The proposal has two distinct thrusts: optimization and learning. On the optimization front, the project team in its past research has defined the docking problem as an optimization on manifolds. In this project, two novel elements in the manifold optimization formulation are introduced that are expected to lead to significant improvements in the performance of docking algorithms. On the learning front, using novel robust optimization techniques, a new and more rigorous approach to robust regression, classification, and outlier detection, is introduced in order to (i) obtain improved ranking of clusters in the refinement stage, and (ii) address the important problem of distinguishing between binders and non-binders. The project aims to improve the performance of computational docking used to predict whether, and if so how, proteins interact with each other and with small molecules. Understanding and predicting protein-protein and protein-small molecule interactions is an important component of the process of rational drug design. More effective protein docking algorithms, therefore, is expected to lead to improving the rational drug design process.