Optimizing and Learning Strategies for Protein Docking

蛋白质对接的优化和学习策略

• 批准号: 10021016
• 负责人: Pirooz Vakili
• 金额: $ 18.21万
• 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/10021016
• 关键词: Address; Algorithms; Area; Behavior; Binding; Classification; Complex; Consumption; Detection; Docking; Drug Design; Elements; Formulation; Frequencies; Knowledge; Lead; Learning; Methods; Molecular Conformation; Performance; Phase; Potential Energy; Process; Proteins; Protocols documentation; Research; Structure; Techniques; Time; base; improved; learning strategy; novel; small molecule; three dimensional structure

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.

蛋白质对接定义为根据蛋白质分子的结构预测对接复合物的三维结构。 了解组件的结构。用于此目的的实验技术通常是昂贵的， 耗时，在某些情况下，是不可行的;因此需要计算对接方法。的 找到对接构象的问题通常被公式化为基于能量的评分的最小化 功能该函数由多个能量项组成，这些能量项作用于不同的空间尺度， 多频率行为导致大量的局部最小值。此外，本发明的方法 对接/结合涉及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。这些特征使得优化问题极其困难。 大多数最先进的对接协议采用多阶段和多尺度方法。它们开始是 使用简化的评分函数对构象空间进行全局搜索，以鉴定构象的有希望区域。 空间，然后使用更详细和完整的评分功能进行局部优化，以消除冲突。在 最后所谓的细化阶段，在前两个阶段中发现的有希望的领域将使用 中等空间尺度搜索以提供一组最终解决方案。最近，由于 由于评分函数/能量势的不准确性，上面概述的优化阶段总是生成 最后阶段的假阳性数量，即1个分数较低但远离天然构象的构象 构象这促使在该建议中引入将联合收割机能量与 附加特征，以便在细化阶段对构象簇进行排序并改进最终解决方案。 该提案有两个不同的目标：优化和学习。在优化方面，项目团队 在过去的研究中，已经将对接问题定义为流形上的优化。在这个项目中，两部小说 在歧管优化配方中的元素被引入，预计将导致显着的 对接算法性能的改进。在学习方面，使用新的鲁棒优化， 技术，一种新的，更严格的方法，强大的回归，分类和离群点检测，是 介绍，以便（i）获得改进的排名聚类在细化阶段，和（ii）解决 区分粘合剂和非粘合剂的重要问题。 该项目旨在提高计算对接的性能，用于预测是否，如果是这样， 蛋白质之间以及与小分子之间的相互作用。理解和预测蛋白质-蛋白质 蛋白质-小分子相互作用是合理药物设计过程的重要组成部分。更 因此，期望有效的蛋白质对接算法导致改进合理的药物设计过程。