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

基于探索多维能量的蛋白质对接细化方法

基本信息

批准号：
8450066
负责人：
Dmytro Kozakov
金额：
$ 30.5万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-04-01 至 2015-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8450066
关键词：
Accounting Address Adopted Algorithms Benchmarking Binding Biological Communities Complex Computer software Computing Methodologies Data Data Quality Decision Theory Development Dill Dimensions Discrimination Docking Electrostatics Elements Evaluation Exhibits Fourier Transform Free Energy Gene Expression Regulation Generations Goals Grant Hand Health Immune response Knowledge Lead Libraries Ligands Link Machine Learning Maintenance Maps Metabolic Control Methods Metric Modeling Molecular Conformation Monte Carlo Method Motion Motivation Movement National Institute of General Medical Sciences Paper Pathway interactions Plant Roots Positioning Attribute Potential Energy Probability Process Property Proteins Protocols documentation Published Comment Reporting Research Rotation Sampling Scoring Method Shapes Side Signal Transduction Simulate Staging Structure Techniques Time Transduction Gene Translating Translations Ursidae Family Vertebral column Work Writing base combinatorial cost experience flexibility improved interest programs protein complex protein folding protein protein interaction prototype receptor research study screening success theories

项目摘要

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.

描述（由申请人提供）：所有成功的最先进的蛋白质对接方法采用了所谓的多阶段方法。在这种方法的第一阶段，粗糙的能量潜力用于得分数十亿个构象。在第二阶段，根据某些相似性度量，将数千种具有最佳分数的构象保留并聚集。聚类中心对应于推定的预测/模型。提议团队的最新工作表明，可以通过通过称为改进的过程正确探索这些簇来实现更高的预测质量。这项工作导致开发了一种原型改进方法 - 基于半准编程的低估方法（SDU）。该项目的核心目标是建立SDU成功，并开发一种新的高通量改进协议，能够以最有效的方式产生近乎结晶质量的预测。通过利用结合自由能电位表现出的漏斗样形状来实现效率。具体目的是：（1）开发一种新的聚类方法，该方法可以将从第一阶段方法保留的构象分类为适合拟议的改进策略的群集；（2）与每个集群相对应的多维漏斗结构的表征以及开发有效的改进策略以探索该漏斗；（3）通过利用马尔可夫随机场理论来对接的侧链定位算法的发展；（4）通过发布到软件包和自动改进服务器的研究社区开发的算法的传播。预计，替代蒙特卡洛方法对拟议的改进方案的计算效率提高将超过两个数量级，而同时，随着早期的改进方法所达到的准确性，可以显着提高。拟议工作的新颖性是它使用了从优化和决策理论领域的复杂机械，专门针对对接问题的生物物理特性量身定制的。来自凸和组合优化，机器学习和马尔可夫随机领域的技术在多阶段蛋白对接方法的改进阶段进行。该工作的一个重要要素是多维结合能量漏斗的系统表征。长期以来，这种漏斗的存在一直是猜想的，但迄今为止尚未导致新的对接方法。提出的算法实质上是通过制定有效的策略来识别，表征和探索这些漏斗的方法来实现这一目标。