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.

提出的活动涉及研究环境和教育课程，该课程致力于有效处理蛋白质分子对计算研究的复杂性和约束。重点是阐明蛋白质用于生物学功能的运动。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的基本问题。了解硅中的蛋白质涉及搜索固有柔性系统的巨大高维构象空间，该系统具有许多相关的自由度，复杂的几何学，物理约束和连续运动。确定了三个核心研究方向。 （1）蛋白质运动的几何约束并不是微不足道的。拟议的研究利用了蛋白质与机器人运动学联系之间的机械类比，并研究了反向运动学技术，以有效地制定和解决各种蛋白质研究中产生的复杂几何约束。 （2）类似漏斗的蛋白质能量景观暴露了基于物理的能量约束，这些约束通常要求在计算机中解决。拟议的研究在概率搜索的背景下追求对能量约束的多尺度处理，支持蛋白质代表性细节的粗糙和细粒度水平，并通过在探索过程中收集的信息进行转化。 （3）蛋白质状态与功能相关的蛋白质状态的构象合奏视图需要搜索算法能够探索高维构象空间及其坚固的能量景观。提出了一个新颖的概率搜索框架，该框架可以收集有关其探索空间的信息，并采用此信息来迈向有希望的未开发空间区域。综上所述，这些研究方向允许通过制定和利用几何和充满活力的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，并采用一种新颖的概率框架，并采用具有增强的采样能力来搜索空间的相关区域，从而可以预见的是开发的概况，从而可以预见到概述。由于大多数实践感兴趣的问题都是高维的，并且经常表现出复杂的非线性空间，因此拟议的研究跨越了计算机科学中的多个领域，例如机器人运动计划，在复杂的非线性空间中优化，以及对复杂物理系统的建模和模拟和模拟。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。具有铰接机制的类比将提供有关如何在约束存在下生成有效的机器人配置的见解。在生物物理方面，该研究有望提高跨不同应用的蛋白质建模和理解。提出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。发现和数据将被广泛传播，以增强各种社区的科学理解。专注于课程设计和外展活动的具体教育目标是采用拟议的研究来扩大大学和大学前学生的参与的，特别着重于代表性不足的群体。