Constrained Sequential Monte Carlo and Its Applications

约束序列蒙特卡罗及其应用

• 批准号: 6901789
• 负责人: RONG CHEN
• 金额: $ 30.38万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-06-01 至 2007-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6901789
• 关键词: computer program /software; conformation; informatics; mathematical model; model design /development; protein folding; protein protein interaction; protein structure function; proteomics; surface property

DESCRIPTION (provided by applicant): A fundamental problem in molecular biology is the structure-function relationship of proteins. To understand how structure dictates the function of a protein, it is essential to: (1) Identify functionally important surfaces on protein. At genomic and proteomic scale, it is also critical to: (2) Identify significant similarity of protein surface patterns among proteins which may have different fold structures. The inverse problem of the structure-function relationship asks: (3) How does protein function influence the folding and stability of proteins? A related general question is (4): Do geometric properties such as packing defects influence the stability and functions of proteins, e.g., for proteins from thermophilic microbes that thrive at high temperature? This project develops novel statistical models and computational methods that helps to solve these four important biological problems. The sequential Monte Calo (SMC) methodologies recently emerged in statistics show great promises. This project develops Constrained Sequential Monte Carlo (CSMC) methods specifically designed to solve these high dimensional and complex statistical inference problems with severe constraints. General strategies and theory in designing the key components are developed for successful CSMC implementation. Implemented CSMC tools are disseminated to research community freely. The results of this project enable the discovery of spatial surface motifs and uncover novel functional relations of proteins important for drug discovery. New patterns discovered can be employed to search for functionally related protein sequences, when structural information is not available. In addition, this research provides important tools for quantitatively assessing how protein function influence protein folding and stability. Insights are gained towards understanding how packing defects influence proteins stability.

描述（由申请人提供）：分子生物学的基本问题是蛋白质的结构 - 功能关系。要了解结构如何决定蛋白质的功能，必须：（1）识别蛋白质功能上重要的表面。在基因组和蛋白质组学量表上，对于：（2）确定蛋白质表面模式的显着相似性可能具有不同的折叠结构。结构功能关系关系的反问题要问：（3）蛋白质功能如何影响蛋白质的折叠和稳定性？一个相关的一般问题是（4）：诸如填料缺陷之类的几何特性是否会影响蛋白质的稳定性和功能，例如，对于在高温下繁衍生长的蛋白质的蛋白质？ 该项目开发了新颖的统​​计模型和计算方法，有助于解决这四个重要的生物学问题。统计数据中最近出现的顺序蒙特卡洛（SMC）方法论表明了巨大的承诺。该项目开发了限制的顺序蒙特卡洛（CSMC）方法，专门设计用于解决具有严重约束的高维和复杂的统计推断问题。设计关键组件的一般策略和理论是为成功实施的。实施的CSMC工具被传播到自由研究社区。 该项目的结果使得可以发现空间表面基序，并发现对药物发现重要的蛋白质的新功能关系。当没有结构信息时，可以使用发现的新模式来搜索与功能相关的蛋白质序列。此外，这项研究还提供了用于定量评估蛋白质功能如何影响蛋白质折叠和稳定性的重要工具。洞察力是为了了解包装缺陷如何影响蛋白质稳定性的见解。