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: 1347865
• 负责人: Yang Shen
• 金额: $ 17.5万
• 依托单位: Toyota Technological Institute at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1347865&HistoricalAwards=false
• 关键词: CCF; EAGER; Dimension; Reduction; Optimization; 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。 它们相互相互作用，核酸，配体和其他分子是许多重要的细胞过程。 因此，在原子水平上优先阐明蛋白质相互作用对于理解这些过程并在患病细胞中治疗它们很重要。 蛋白质对接通过找到最低自由能值的蛋白质构象来计算实现目的。 解决这样的自由能最小化问题是具有挑战性的，原因有两个。 首先，搜索空间是极高的，因为蛋白质不是刚性的身体，而是相互作用时灵活的。 其次，自由能函数评估和坚固以优化是非常昂贵的。知识分子优点拟议的研究直接解决了在改进阶段蛋白质对接的这些挑战。 首先，搜索空间的尺寸将大大降低。 尽管蛋白质由数百至数千种原子组成，但并非所有这些原子的集体运动在物理上都是有意义的。 所提出的方法将通过应用新颖的正常模式分析（NMA）工具将集体原子运动的空间分解为刚体运动的空间以及一些相关的柔韧性运动的空间。 其次，最低能量的搜索效率将大大提高。 将在减少的搜索空间中表征能量景观，并将最先进的搜索方法用于该空间中的能量最小化。 提出的研究还将对蛋白质相互作用的减少构象空间以及在减少空间中自由能函数的景观产生见解。 更广泛的影响能够预测蛋白质如何与效率和准确性相互作用的能力对于理解生活的结构和功能组织，开发诊断和治疗工具以治愈疾病以及发现提高生物能源产量的方法具有巨大的价值。 拟议的研究是跨学科的，因此对需要蛋白质对接工具的生物学家以及需要应用或开发降低尺寸降低和优化方法的生物学家有用。 项目将为学生提供跨学科技能的培训机会。