CAREER: Probabilistic Methods for Addressing Complexity and Constraints in Protein Systems

职业：解决蛋白质系统复杂性和约束的概率方法

• 批准号: 1144106
• 负责人: Amarda Shehu
• 金额: $ 54.99万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-03-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1144106&HistoricalAwards=false
• 关键词: CAREER; Probabilistic; Methods; Addressing; Complexity

The proposed activity involves a research environment and educational curriculum dedicated to dealing efficiently with the complexity and constraints that protein molecules pose to computational studies. The emphasis is on elucidating the motions that proteins employ for biological function. This is a fundamental issue in the understanding of proteins and biology due to the central role of proteins in cellular processes.The research addresses fundamental issues in protein modeling. Understanding proteins in silico involves searching a vast high-dimensional conformational space of inherently flexible systems with numerous inter-related degrees of freedom, complex geometry, physical constraints, and continuous motion. Three core research directions are identified. (1) Geometric constraints underlying protein motion are not trivial to identify or address. The proposed research exploits mechanistic analogies between proteins and robot kinematic linkages and investigates inverse kinematics techniques to efficiently formulate and address complex geometric constraints arising in diverse protein studies. (2) The funnel-like protein energy landscape exposes physics-based energetic constraints that are often demanding to address in silico. The proposed research pursues a multiscale treatment of energetic constraints in the context of probabilistic search, supporting coarse- and fine-grained levels of protein representational detail and converting between them with information gathered during exploration. (3) The conformational ensemble view of the protein state relevant for function necessitates search algorithms capable of exploring the high-dimensional conformational space and its rugged energy landscape. A novel probabilistic search framework is proposed that gathers information about the space it explores and employs this information to advance towards promising unexplored regions of the space. Taken together, these research directions allow addressing complexity in proteins by formulating and exploiting geometric and energetic constraints, thus narrowing the search space of interest to regions where the constraints are satisfied, and by employing a novel probabilistic framework with enhanced sampling capability able to feasibly search the relevant regions of the space.The proposed activity promises to advance discovery and understanding both in the computer science and protein biophysics communities. Since most problems of practical interest are high-dimensional and often exhibit complex non-linear spaces, the proposed research cuts across and spans multiple areas in computer science, such as robot motion planning, optimization in complex non-linear spaces, and modeling and simulation of complex physics-based systems. In particular, the research will reveal effective probabilistic search strategies for continuous high-dimensional search spaces. Analogies with articulated mechanisms will offer insight on how to generate valid robot configurations in the presence of constraints. On the biophysical side, the research promises to advance protein modeling and understanding across diverse applications. The proposed activity involves interdisciplinary collaborations with computer scientists, biophysicists, and chemists. Findings and data will be disseminated broadly to enhance scientific understanding across diverse communities. Specific educational objectives focusing on curriculum design and outreach activities are formulated to employ the proposed research for broadening the participation of college and pre-college students, with a particular emphasis on underrepresented groups.

提议的活动包括一个研究环境和教育课程，致力于有效地处理蛋白质分子对计算研究的复杂性和限制。重点是阐明蛋白质为生物功能所采用的运动。由于蛋白质在细胞过程中的核心作用，这是理解蛋白质和生物学的一个基本问题。该研究解决了蛋白质建模中的基本问题。在计算机上理解蛋白质需要搜索一个巨大的高维构象空间，这个空间具有许多相互关联的自由度、复杂的几何形状、物理约束和连续运动的内在柔性系统。确定了三个核心研究方向。(1)蛋白质运动背后的几何约束很难识别或解决。提出的研究利用蛋白质和机器人运动学联系之间的机制类比，并研究逆运动学技术，以有效地制定和解决各种蛋白质研究中出现的复杂几何约束。(2)漏斗状的蛋白质能量景观暴露了基于物理的能量限制，这些限制通常需要在计算机中解决。提出的研究在概率搜索的背景下追求能量约束的多尺度处理，支持粗粒度和细粒度的蛋白质表征细节水平，并在探索过程中收集信息在它们之间进行转换。(3)与功能相关的蛋白质状态的构象集合视图需要能够探索高维构象空间及其崎岖能量景观的搜索算法。提出了一种新的概率搜索框架，它收集关于它所探索的空间的信息，并利用这些信息向空间中有前途的未探索区域推进。综上所述，这些研究方向允许通过制定和利用几何和能量约束来解决蛋白质的复杂性，从而将感兴趣的搜索空间缩小到满足约束的区域，并通过采用具有增强采样能力的新型概率框架来可行地搜索空间的相关区域。提议的活动有望促进计算机科学和蛋白质生物物理学社区的发现和理解。由于大多数实际问题都是高维的，并且经常表现出复杂的非线性空间，因此提出的研究跨越并跨越了计算机科学的多个领域，例如机器人运动规划，复杂非线性空间中的优化以及基于复杂物理系统的建模和仿真。特别是，该研究将揭示连续高维搜索空间的有效概率搜索策略。与铰接机构的类比将提供关于如何在约束存在的情况下生成有效的机器人配置的见解。在生物物理方面，这项研究有望在不同的应用中推进蛋白质建模和理解。拟议的活动涉及与计算机科学家、生物物理学家和化学家的跨学科合作。研究结果和数据将广泛传播，以加强不同社区之间的科学理解。制定了侧重于课程设计和外联活动的具体教育目标，以便利用拟议的研究扩大大学和大学预科学生的参与，特别强调代表性不足的群体。