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Constrained Sequential Monte Carlo and Its Applications

约束序列蒙特卡罗及其应用

基本信息

批准号：
7072632
负责人：
RONG CHEN
金额：
$ 29.67万
依托单位：
UNIVERSITY OF ILLINOIS AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2003
资助国家：
美国
起止时间：
2003-06-01 至 2008-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7072632
关键词：
computer program /software conformation informatics mathematical model model design /development protein folding protein protein interaction protein structure function proteomics surface property

项目摘要

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）：分子生物学中的一个基本问题是蛋白质的结构-功能关系。为了理解结构如何决定蛋白质的功能，必须：（1）识别蛋白质上功能重要的表面。在基因组和蛋白质组水平，也是至关重要的：（2）确定蛋白质表面模式之间的显着相似性，可能有不同的折叠结构的蛋白质。结构-功能关系的逆问题是：（3）蛋白质的功能如何影响蛋白质的折叠和稳定性？一个相关的一般性问题是（4）：几何性质，如包装缺陷，是否影响蛋白质的稳定性和功能，例如，嗜热微生物中的蛋白质在高温下能存活吗该项目开发了新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。最近出现的序贯蒙特卡罗（SMC）方法在统计显示出很大的希望。该项目开发了约束序贯蒙特卡罗（CSMC）方法，专门用于解决这些具有严格约束的高维复杂统计推断问题。在设计的关键部件的一般策略和理论开发成功的CSMC实施。已实施的CSMC工具免费分发给研究界。该项目的结果使空间表面基序的发现和发现新的蛋白质的功能关系，重要的药物发现。当结构信息不可用时，发现的新模式可用于搜索功能相关的蛋白质序列。此外，这项研究为定量评估蛋白质功能如何影响蛋白质折叠和稳定性提供了重要工具。深入了解包装缺陷如何影响蛋白质的稳定性。