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.

该研究涉及对框架的设计和分析，以计算空间排列（也称为构象），其中氨基酸的蛋白质链具有生物学活性（在其本地状态）。这是理解蛋白质功能的重要目标。尽管蛋白质是许多生物化学过程的核心，但对于从有机基因组获得的数百万蛋白序列而言，知名度鲜为人知：这项工作的智力优点在于开发一种新型的计算框架，将新型计算框架与概率探索与统计学理论相结合的概念性探索结合在一起，以高效地增强天然综合型的采样。低维投影指导探索低能和几何多样性构象。其他智力优点在于，从生物物理理论和实验中出现了知识和观察结果，例如使用粗网，能量屏障高度和温度之间的关系以及第三级结构的等级组织。该框架的算法成分将进行系统评估，以提高效率，准确性，以及它们如何增强本地状态附近的构象空间的采样。BROADER的影响：这项研究的更广泛的影响将创建有效地计算出与蛋白质本地的多样性粗糙构象的过滤器，从而可以对蛋白质天然状态相关，从而可以进一步介绍了细化的细化。这项工作在于计算机科学与蛋白质生物物理学之间的界面，可以使这两个社区受益。在计算方面，这项工作将导致建模以连续高维搜索空间和复杂能量表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可以有效，准确地建模。这项工作将影响本科生和研究生。研究人员提出了新课程，这是为乔治·梅森大学（George Mason University）在计算机科学课程中介绍计算生物学的一部分。这项工作将被用作课程和教育外展场所中的教学设备，以产生并保持对计算机科学的兴趣，并特别关注妇女和少数民族。