Refinement Methods for Protein Docking based on Exploring Multi-Dimensional Energ

描述（由申请人提供）：所有成功的最先进的蛋白质对接方法都采用了所谓的多阶段方法。在这种方法的第一阶段，使用粗略的能量势来记录数十亿个构象。在第二阶段，保留得分最高的数千个构象，并根据一定的相似性度量对其进行聚类。聚类中心对应于假定的预测/模型。提出建议的团队最近的工作表明，通过一个称为细化的过程对这些集群进行适当的探索，可以获得更高的预测质量。这项工作导致了原型改进方法的发展-基于半确定规划的低估方法（SDU）。该项目的中心目标是在SDU成功的基础上，开发一种新的高通量精化方案，能够以最有效的计算方式产生近晶体质量的预测。效率将通过利用结合自由能势所显示的漏斗状形状来实现。具体目标是：(1)开发一种新的聚类方法，该方法可以将第一阶段方法保留的构象分类为适合所提出的改进策略的聚类；(2)对每个集群对应的多维漏斗结构进行表征，并制定有效的优化策略来探索该漏斗；(3)利用马尔可夫随机场理论，开发适合对接的侧链定位算法；(4)通过向研究社区发布软件包和自动细化服务器来传播所开发的算法。预计所提出的改进协议与其他蒙特卡罗方法相比，计算效率的提高将超过两个数量级，同时，大大提高了先前改进方法所达到的精度。提出的工作的一个新颖之处在于它使用了优化和决策理论领域的复杂机制，专门针对对接问题的生物物理特性。从凸优化和组合优化，机器学习和马尔可夫随机场的技术被引入到多级蛋白质对接方法的细化阶段。这项工作的一个重要组成部分是对多维结合能漏斗的系统表征。这种漏斗的存在一直被推测，但迄今为止还没有导致新的对接方法。所提出的算法基本上通过设计有效的策略来识别、表征和探索这些渠道来实现这一目标。

DESCRIPTION (provided by applicant): All successful state-of-the-art protein docking methods employ a so called multistage approach. At the first stage of such approaches a rough energy potential is used to score billions of conformations. At a second stage, thousands of conformations with the best scores are retained and clustered based on a certain similarity metric. Cluster centers correspond to putative predictions/models. Recent work by the proposing team demonstrated that greater prediction quality can be achieved by properly exploring these clusters through a process called refinement. This work resulted in the development of a prototype refinement approach - the Semi-Definite programming-based Underestimation method (SDU). The central goal of the project is to build on the SDU success and develop a new high-throughput refinement protocol able to produce predictions of near-crystallographic quality in the most computationally efficient manner. Efficiency will be achieved by leveraging the funnel-like shape that binding free energy potentials exhibit. The specific aims are: (1) the development of a new clustering method that can classify the conformations retained from a first-stage method into clusters suitable for the proposed refinement strategy; (2) the characterization of the structure of the multi-dimensional funnel corresponding to each cluster and the development of an efficient refinement strategy to explore this funnel; (3) the development of a side-chain positioning algorithm appropriate for docking by leveraging Markov random field theory; and (4) the dissemination of the algorithms developed through the release to the research community of a software package and an automated refinement server. It is anticipated that the computational efficiency gains of the proposed refinement protocol over alternative Monte Carlo methods will exceed two orders of magnitude, while, at the same time, significantly improve upon the accuracy achieved by earlier refinement approaches. A novelty of the proposed work is in its use of sophisticated machinery from the fields of optimization and decision theory specially tailored to the biophysical properties of the docking problem. Techniques from convex and combinatorial optimization, machine learning, and Markov random fields are brought to bear on the refinement stage of multistage protein docking approaches. An important element of the work is the systematic characterization of multi-dimensional binding energy funnels. The existence of such funnels has been long conjectured but it has not led to new docking approaches so far. The proposed algorithms essentially achieve this goal by devising efficient strategies to identify, characterize, and explore these funnels.