Optimizing and Learning Strategies for Protein Docking

蛋白质对接的优化和学习策略

• 批准号: 9903730
• 负责人: Pirooz Vakili
• 金额: $ 19.71万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-20 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9903730
• 关键词: Address; Algorithms; Area; Behavior; Binding; Biological; Classification; Collaborations; Communities; Complex; Consumption; Detection; Development; Dimensions; Docking; Formulation; Frequencies; Goals; Hand; Individual; Lead; Learning; Methodology; Methods; Molecular Conformation; National Institute of General Medical Sciences; Neighborhoods; Phase; Potential Energy; Process; Proteins; Protocols documentation; Research Personnel; Structure; Techniques; Time; United States National Institutes of Health; base; improved; learning strategy; mathematical algorithm; mathematical methods; mathematical model; next generation; novel; structural biology; three dimensional structure; tool

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.

蛋白质对接定义为预测基于对接配合物的三维结构 了解组件的结构。用于此目的的实验技术通常很昂贵， 耗时，在某些情况下是不可行的；因此，需要计算对接方法。这 寻找对接构象的问题通常被认为是基于能量的评分的最小化 功能。此功能由多种能量术语组成，这些术语以不同的空间尺度起作用并证明 多频行为导致了大量局部最小值。此外， 对接/结合涉及对组分分子的构象变化，导致高度复杂的搜索 优化问题的空间。这些功能使优化问题极为困难。 大多数最先进的对接协议采用多阶段和多尺度方法。他们以一个 使用简化的评分功能对构象空间进行全球搜索，以识别 空间，然后使用更详细和完整的评分功能进行本地优化，以消除冲突。在 最后两个阶段中发现的最终所谓改进阶段，使用A进一步探讨了 中等空间尺度搜索以提供一组最终解决方案。最近，由于 评分函数/能量电位的不准确性，上面概述的优化阶段总是会产生 在最后阶段的假阳性数量，即得分较低但远离本地的构象 构象。这激发了这种将能量与能量与 其他功能，以便在改进阶段对构象的簇进行排名并改善最终解决方案。 该提案有两个不同的推力：优化和学习。在优化方面，项目团队 在过去的研究中，将停靠问题定义为对流形的优化。在这个项目中，两本小说 引入了歧管优化公式中的元素，这些元素有望导致重要 对接算法的性能的改进。在学习方面，使用新颖的强大优化 技术是一种新的，更严格的方法来鲁棒回归，分类和异常检测，是 引入（i）在改进阶段获得簇的排名改进，（ii）解决 区分粘合剂和非绑定器的重要问题。 该项目旨在提高用于预测是否以及是否这样的计算对接的性能 蛋白质如何相互相互作用，并与小分子相互作用。理解和预测蛋白质蛋白 蛋白质 - 小分子相互作用是理性药物设计过程的重要组成部分。更多的 因此，有效蛋白质对接算法有望导致改善合理的药物设计过程。