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所谓的多阶段方法。在这种方法的第一阶段，粗略的能量势被用来记录数十亿种构象。在第二阶段，保留具有最佳分数的数千个构象，并基于某一相似性度量对其进行聚集。集群中心对应于假定的预测/模型。提议团队最近的工作表明，通过一种称为精化的过程适当地探索这些星系团，可以实现更高的预测质量。这项工作导致了一种原型求精方法--基于半定规划的低估方法(SDU)的发展。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炼协议，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展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成适合于所提出的细化策略的簇；(2)表征与每个簇对应的多维漏斗的结构，并开发有效的细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；以及(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。预计与其他蒙特卡罗方法相比，改进协议的计算效率将提高两个数量级以上，同时，与以前的改进方法相比，精度有了显著的提高。拟议工作的一个新奇之处在于它使用了来自优化和决策理论领域的复杂机械，这些机械是专门为对接问题的生物物理性质量身定做的。来自凸优化和组合优化、机器学习和马尔可夫随机场的技术被用于多级蛋白质对接方法的精化阶段。这项工作的一个重要内容是对多维结合能漏斗的系统表征。长期以来，人们一直在猜测这种漏斗的存在，但到目前为止，它还没有带来新的对接方法。提出的算法基本上通过设计有效的策略来识别、表征和探索这些漏斗，从而实现了这一目标。

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.