Nonparametric Bayesian Approaches to Modeling Protein Structure

蛋白质结构建模的非参数贝叶斯方法

• 批准号: 8446627
• 负责人: David B Dahl
• 金额: $ 35.08万
• 依托单位: BRIGHAM YOUNG UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8446627
• 关键词: Accounting; Amino Acids; Basic Science; Catalytic Domain; Chinese People; Communities; Data; Disease; Genetic; Genome; Higher Order Chromatin Structure; Individual; Knowledge; Methods; Modeling; Molecular Conformation; Pattern; Positioning Attribute; Probability; Process; Proteins; Restaurants; Role; Sampling; Scientific Advances and Accomplishments; Signaling Protein; Statistical Models; Tertiary Protein Structure; base; drug discovery; improved; indexing; insight; protein structure

DESCRIPTION (provided by applicant): This proposal's objective is to develop a new class of statistical models to advance scientific knowledge of protein tertiary structure and to extend template-based modeling to protein loop regions. As advancement in basic science, the improved modeling of protein structure will broadly impact biomedical fields. The following specific aims will be accomplished. The first aim (Random Partition Models Indexed by Pairwise Information) is to develop probability models for partitions that are explicitly non-exchangeable, utilizing available pairwise information to influence the clustering of data. Four distributions ar proposed, each using the pairwise information by modifying identities from the Chinese Restaurant Process, a popular probability model for clustering. Hierarchical clustering uses pairwise distance, but current methods for protein structure modeling do not. The proposed method provides a means to incorporate this type of information into Bayesian nonparametric models for protein structure. The second aim (Template-Based Modeling of Loop Conformation Space Using Partition Models) applies the proposed random partition models in loop modeling. This proposal will improve our previous estimation approach by accounting for the influences of individual amino acids as well as for influences from neighboring residues. New methods based on the random partition models will provide rigorous statistical modeling at and between residue positions allowing one to limit and precisely sample the conformational space. This will in turn allow for a clearer understanding of roles of loops in catalytic sites and protein signaling. The final aim (New Paradigm for Protein Packing and Higher-Order Structure Using Partition Models) applies the statistical modeling to estimate the propensities of a new model of protein packing called the "ball/socket." Statistical modeling of the amino acid propensities within the "ball/socket" motifs and between patterns of motifs will allow insights into the rules governing packing, filling a substantial gap in current understanding of protein structure. The statistical model estimating these propensities will exploit the known pairwise information by using the proposed random partition models. Such analysis is currently not available to the scientific community.

描述(由申请人提供)：这项提案的目标是开发一类新的统计模型，以促进蛋白质三级结构的科学知识，并将基于模板的建模扩展到蛋白质环区域。随着基础科学的进步，蛋白质结构建模的改进将对生物医学领域产生广泛的影响。实现以下具体目标。第一个目标(由配对信息索引的随机分区模型)是为明确不可交换的分区开发概率模型，利用可用的配对信息来影响数据的聚类。提出了四种分布，每一种分布都使用通过修改来自中国餐馆过程的身份的成对信息，该过程是一种流行的聚类概率模型。层次聚类使用成对距离，但目前的蛋白质结构建模方法不使用。该方法提供了一种将这类信息融入到蛋白质结构的贝叶斯非参数模型中的方法。第二个目标(基于划分模型的环构象空间模板建模)将所提出的随机划分模型应用于环建模。这一建议将改进我们以前的估计方法，考虑到单个氨基酸的影响以及邻近残基的影响。基于随机分配模型的新方法将在残基位置和残基位置之间提供严格的统计建模，从而允许限制和精确采样构象空间。这反过来将允许更清楚地了解环在催化部位和蛋白质信号中的作用。最终目标(蛋白质包装的新范例和使用分区模型的高阶结构)应用统计建模来估计一种名为“球/槽”的新蛋白质包装模型的倾向。对“球/窝”基序内和基序图案之间的氨基酸倾向进行统计建模，将使人们能够深入了解包装规则，填补目前对蛋白质结构理解的重大空白。估计这些倾向的统计模型将通过使用所提出的随机划分模型来利用已知的成对信息。科学界目前还无法获得这样的分析。