AF: SMALL: Computational Framework for Characterizing Protein Conformational Landscapes

蛋白质几乎参与了生命中的每一个过程。蛋白质结构、动力学和功能之间的关系多年来一直是实验生物学家和计算机科学家的挑战，但由于蛋白质折叠、结合和结构域运动等事件的复杂性，许多研究问题仍然悬而未决。由于蛋白质折叠和结合等事件的高复杂性和高维性，现有的计算方法来分析蛋白质构象空间的结构和功能特性是有限的。教育和推广活动将通过以下方式实施：a）与马萨诸塞大学波士顿分校内外的研究人员进行跨学科合作。B）培训和指导本科生和研究生的研究，包括妇女和来自科学领域代表性不足群体的学生。积极的工作与妇女和学生的代表性不足的群体将通过桥梁学士学位计划，在科学俱乐部和IMSD计划在马萨诸塞大学波士顿分校，UMB妇女追求提出的算法利用蛋白质的几何和生物物理特性，有效地表征其构象空间和检测有趣的区域，可能是功能上重要的，但很难确定实验。本文将探讨以下三个相关的研究问题：1.表征蛋白质的灵活性和约束：将开发的方法，通过结合计算几何和生物物理学的概念，有效地采样的蛋白质构象景观。本部分的重点是刚度分析和概率方法.构象空间的有效低维表示：将测试用于可靠表示构象空间的有效特征选择和降维技术。这些方法使用较少数量的变量表示高维复杂数据，同时保留基本信息。这将有助于蛋白质动力学的分析和表征.表征中间构象：将开发结合联合收割机几何、数学和拓扑工具的强大算法方法，以有效地分析蛋白质构象空间并发现高度聚集的区域。重点将放在聚类方法，检测离群值和处理噪声和多重约束。目标是确定蛋白质构象空间的重要结构和功能特性，例如低能极小值、高能势垒和中间态的形状和数量。

Proteins are involved in virtually every process in life. The relationship between protein structure, dynamics and function has challenged experimental biologists and computer scientists for years, but many research questions remain open due to the complexity of events such as protein folding, binding and domain motion. Existing computational methods to analyze the structural and functional properties of protein conformational spaces are limited due to the high complexity and high dimensionality of events such as protein folding and binding. Educational and outreach activities will be implemented through the following: a) Interdisciplinary collaborations with researchers inside and outside UMass Boston.b) Training and mentoring the research of undergraduate and graduate students, including women and students from under-represented groups in science. Active work with women and students from under-represented groups will be pursued through the Bridges to the baccalaureate program, the UMB women in science club and the IMSD program at UMass Boston.Proposed algorithms exploit the geometric and biophysical properties of proteins to efficiently characterize their conformational space and detect interesting regions that may be functionally important but are hard to determine experimentally. The three following related research problems will be explored:1. Characterize Protein Flexibility and Constraints: Methods will be developed for effective sampling of protein conformational landscape by combining concepts from computational geometry and biophysics. The emphasis of this part will be on rigidity analysis and probabilistic methods.2. Effective Low-Dimensional Representation of the Conformational Space: Effective feature selection and dimensionality reduction techniques for reliable representation of the conformational space will be tested. These methods represent high dimensional, complex data using a smaller number of variables while preserving essential information. This will facilitate the analysis and characterization of protein dynamics.3. Characterizing Intermediate Conformations: Powerful algorithmic methods that combine geometry, mathematical and topological tool will be developed to effectively analyze the protein conformational spaces and discover highly populated regions. The emphasis will be on clustering methods, detection of outliers and handling noise and multiple constrains. The goal is to identify important structural and functional properties of protein conformational spaces such as the shape and number of low energy minima, high energy barriers and intermediate states.