CCF: EAGER: Dimension Reduction and Optimization Methods for Flexible Refinement of Protein Docking

CCF：EAGER：蛋白质对接灵活细化的降维和优化方法

• 批准号: 1546278
• 负责人: Yang Shen
• 金额: $ 8.86万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546278&HistoricalAwards=false
• 关键词: CCF; EAGER; Dimension; Reduction; Optimization

Proteins are the 'workhorse' molecules in cells. Their interactions with each other, nucleic acids, ligands, and other molecules underlie many important cellular processes. Therefore, elucidating protein interactions, preferentially at the atomic level, is important to understanding these processes and treating them in diseased cells. Protein docking achieves the purpose computationally by finding the protein conformation of the lowest free energy values. Solving such a free energy minimization problem is challenging for two reasons. First, the search space is extremely high-dimensional because proteins are not rigid bodies but rather flexible when interacting. Secondly, the free energy function is very costly to evaluate and rugged to optimize. Intellectual MeritThe proposed research directly addresses these challenges to protein docking at the refinement stage. First, the dimension of the search space will be substantially lowered. Although proteins consist of hundreds to thousands of atoms, not all collective motions of those atoms are physically meaningful. The proposed methods will decompose the space of collective atomic motions into that of rigid-body motions and that of a few relevant flexibility motions by applying novel normal mode analysis (NMA) tools. Second, the search efficiency for the energy minimum will be substantially improved. Energy landscape in the reduced search space will be characterized and state-of-the-art search methods will be applied for energy minimization in the space. The proposed research will also produce insights on the reduced conformational space of protein interactions and the landscape of free energy functions in the reduced space. Broader ImpactsThe ability to predict how proteins interact with efficiency and accuracy is of tremendous value to understanding the structural and functional organization of life, developing diagnosis and therapeutics tools to cure diseases, and discovering approaches to improve bioenergy yields. The proposed research is interdisciplinary and thus expected to be useful to biologists who need protein docking tools to aid their study as well as computer scientists who need to apply or develop dimension reduction and optimization methods. Project will provide training opportunities to students in developing interdisciplinary skills.

蛋白质是细胞中的“主力”分子。 它们之间、核酸之间、配体之间以及其他分子之间的相互作用构成了许多重要细胞过程的基础。 因此，阐明蛋白质相互作用，优先在原子水平上，是重要的理解这些过程，并在患病细胞中治疗它们。 蛋白质对接通过计算找到最低自由能值的蛋白质构象来实现目的。 由于两个原因，解决这样的自由能最小化问题是具有挑战性的。 首先，搜索空间是非常高维的，因为蛋白质不是刚体，而是在相互作用时非常灵活。 其次，自由能函数的评估成本非常高，并且难以优化。智力优点拟议的研究直接解决了这些挑战，蛋白质对接在细化阶段。 首先，搜索空间的维度将大大降低。 虽然蛋白质由数百到数千个原子组成，但并不是所有这些原子的集体运动都有物理意义。 所提出的方法将集体原子运动的空间分解为刚体运动和一些相关的柔性运动，通过应用新的正常模式分析（NMA）工具。 第二，能量最小值的搜索效率将大大提高。 在减少搜索空间的能量景观的特点和国家的最先进的搜索方法将被应用于空间中的能量最小化。 拟议的研究还将产生关于蛋白质相互作用的简化构象空间和简化空间中自由能函数的景观的见解。 更广泛的影响预测蛋白质如何有效和准确地相互作用的能力对于理解生命的结构和功能组织、开发治疗疾病的诊断和治疗工具以及发现提高生物能源产量的方法具有巨大价值。 拟议的研究是跨学科的，因此预计将是有用的生物学家谁需要蛋白质对接工具，以帮助他们的研究，以及计算机科学家谁需要应用或开发降维和优化方法。 项目将为学生提供培训机会，培养跨学科技能。