AF: Small: A Unified Computational Framework to Enhance the Ab-Initio Sampling of Native-Like Protein Conformations

AF：小型：增强类天然蛋白质构象从头开始采样的统一计算框架

• 批准号: 1016995
• 负责人: Amarda Shehu
• 金额: $ 45万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016995&HistoricalAwards=false
• 关键词: AF; Small; Unified; Computational; Framework

The research involves the design and analysis of a framework to compute the spatial arrangements, also known as conformations, in which a protein chain of amino acids is biologically-active (in its native state). This is an important goal towards understanding protein function. While proteins are central to many biochemical processes, little is known about millions of protein sequences obtained from organismal genomes.Intellectual Merit: The intellectual merit of this work lies in the development of a novel computational framework that combines probabilistic exploration with the theory of statistical mechanics to efficiently enhance the sampling of the conformational space near the native state. Low-dimensional projections guide the exploration towards low-energy and geometrically-diverse conformations. Additional intellectual merit lies in the incorporation of knowledge and observations emerging from biophysical theory and experiment, such as the use of coarse graining, relation between energy barrier height and temperature, and hierarchical organization of tertiary structure. Algorithmic components of the framework will be systematically evaluated for efficiency, accuracy, and how they enhance the sampling of the conformational space near the native state.Broader Impact: The broader impact of this research will be the creation of a filter that efficiently computes diverse coarse-grained conformations relevant for the protein native state that can then be further refined through detailed biophysical studies. The work lies at the interface between computer science and protein biophysics and can benefit both communities. On the computational side, the work will lead to new algorithms on modeling articulated chains characterized by continuous high-dimensional search spaces and complex energy surfaces. On the biophysical side, the framework will elucidate which aspects of our understanding of proteins allow efficient and accurate modeling. The work will impact both undergraduate and graduate students. New courses are proposed by the investigator as part of efforts to introduce computational biology in the computer science curriculum at George Mason University. The work will be employed as a pedagogic device in courses and educational outreach venues to spawn and maintain interest in computer science, with a particular focus on women and minorities.

这项研究包括设计和分析一个框架来计算空间排列，也被称为构象，其中氨基酸的蛋白质链具有生物活性（在其天然状态下）。这是理解蛋白质功能的一个重要目标。虽然蛋白质是许多生化过程的核心，但人们对从生物体基因组中获得的数百万蛋白质序列知之甚少。知识价值：这项工作的知识价值在于开发了一种新的计算框架，该框架将概率探索与统计力学理论相结合，以有效地增强接近自然状态的构象空间的采样。低维投影引导着对低能量和几何多样构象的探索。额外的智力优势在于结合了来自生物物理理论和实验的知识和观察，例如粗粒化的使用，能量势垒高度和温度之间的关系，以及三级结构的分层组织。该框架的算法组件将被系统地评估其效率、准确性，以及它们如何增强对原生状态附近构象空间的采样。更广泛的影响：这项研究的更广泛的影响将是创建一个过滤器，有效地计算与蛋白质天然状态相关的各种粗粒度构象，然后可以通过详细的生物物理研究进一步完善。这项工作位于计算机科学和蛋白质生物物理学之间的接口，可以使两个社区受益。在计算方面，这项工作将导致以连续高维搜索空间和复杂能量面为特征的铰接链建模的新算法。在生物物理方面，该框架将阐明我们对蛋白质的理解的哪些方面允许有效和准确的建模。这项工作将影响本科生和研究生。作为将计算生物学引入乔治梅森大学计算机科学课程的努力的一部分，研究者提出了新的课程。这项工作将被用作课程和教育推广场所的教学手段，以培养和保持对计算机科学的兴趣，特别侧重于妇女和少数民族。