Annotating Functional Sites in 3D Biological Structures

注释 3D 生物结构中的功能位点

• 批准号: 8076140
• 负责人: RUSS BIAGIO ALTMAN
• 金额: $ 1.83万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-15 至 2011-06-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8076140
• 关键词: Active Sites; Address; Algorithms; Automobile Driving; Binding; Binding Sites; Biological; Biomedical Research; Calcium Binding; Cell physiology; Cells; Chemicals; Clinical; Cluster Analysis; Code; Collaborations; Collection; Communities; Computer software; Computing Methodologies; Crystallography; DNA; Data; Data Set; Data Sources; Databases; Diagnostic; Disease; Drug Binding Site; Environment; Enzymes; Explosion; Genetic Polymorphism; Genomics; Grant; High Performance Computing; Human; Informatics; Investments; Knowledge; Korea; Label; Learning; Libraries; Link; Machine Learning; Maps; Methods; Mining; Molecular; Molecular Biology; Molecular Medicine; Molecular Structure; Morphologic artifacts; Motion; Pathologic; Pattern; Performance; Phenotype; Phosphorylation; Phosphorylation Site; Physics; Physiological; Process; Property; Proteins; Publishing; RNA; Research; Research Proposals; Resources; Rest; Retrieval; San Francisco; Signal Transduction; Site; Software Engineering; Source; Source Code; Specialist; Structure; Technology; Therapeutic; Time; Training; United States National Institutes of Health; United States National Library of Medicine; Universities; Work; abstracting; advanced simulation; base; biological systems; cost; design; improved; indexing; innovation; interest; macromolecule; molecular dynamics; novel; novel diagnostics; programs; protein function; protein structure; simulation; simulation software; structural genomics; three dimensional structure; tool

DESCRIPTION (provided by applicant): Project Summary/Abstract Dramatic advances in our understanding of molecular structure and function promise to accelerate the creation of new diagnostics and therapeutics. However the link between the structure of a biological macromolecule and its function is usually not obvious: fundamental to understanding how a molecule functions is an understanding of how its structure behaves over time. Recent advances in molecular dynamics simulations now allow the rapid collection of information about structural motion. These data sets are huge, and require statistical machine learning algorithms to characterize and recognize patterns relevant to function. The National Library of Medicine's new long-range plan calls for research in the use of advanced simulation and machine learning algorithms in support of biomedical research. This proposal focuses on annotating molecular structures with missing or incomplete functional information. We are particularly interested in identifying binding sites and active sites in proteins. We bring together simulation and machine learning, and hypothesize that the performance of structure- based function annotation methods will dramatically improve with the addition of information about dynamics. Thus, our specific aims are (1) to develop methods for recognizing function from structural dynamics and diversity, (2) to develop capabilities for large scale clustering and analysis tools for the discovery of novel functions, and (3) to apply our tools to challenging and important biological systems, while disseminating our software, data and capabilities to the biomedical research community. In particular, we will focus our new capabilities on three difficult function annotation challenges: ATP binding sites, phosphorylation sites, and metabolizing enzyme active sites.

描述(由申请人提供)： 项目摘要/摘要在我们对分子结构和功能的理解方面取得了巨大的进步，有望加速新的诊断和治疗方法的创造。然而，生物大分子的结构和其功能之间的联系通常并不明显：了解分子如何发挥功能的基础是了解其结构随时间的变化情况。分子动力学模拟的最新进展使人们能够快速收集有关结构运动的信息。这些数据集是巨大的，需要统计机器学习算法来表征和识别与功能相关的模式。国家医学图书馆的新的长期计划呼吁研究使用先进的模拟和机器学习算法来支持生物医学研究。 这一建议侧重于注释功能信息缺失或不完整的分子结构。我们对识别蛋白质中的结合部位和活性部位特别感兴趣。我们将模拟和机器学习结合在一起，并假设基于结构的函数标注方法的性能将随着动力学信息的添加而显著提高。因此，我们的具体目标是(1)开发从结构动力学和多样性中识别功能的方法，(2)开发发现新功能的大规模聚集和分析工具的能力，以及(3)将我们的工具应用于具有挑战性和重要的生物系统，同时向生物医学研究社区传播我们的软件、数据和能力。特别是，我们将把我们的新能力集中在三个困难的功能注释挑战上：ATP结合位点、磷酸化位点和代谢酶活性位点。