Bayesian Statistics and Algorithms for Homology Modeling

描述（由申请人提供）：为了改善人类健康， 人类基因组计划是将基因组序列转化为理解 人类生物学。这一过程中的一个重要步骤是了解 人类蛋白质的结构和序列多态性对 结构和功能。目前，只有1000种人类蛋白质的结构 已知的，但多达三分之一左右的人类蛋白质的结构可以被 基于Protein Data中同源蛋白质的结构建模 银行由于结构基因组学的努力，这一比例将迅速增加。 不幸的是，在同源建模中起作用的一般原则和 并不是难以捉摸的原因有几个：1） 大多数预测方法的基准不足; 2）依赖于 过时的蛋白质结构统计分析，在没有调制解调器的情况下进行 统计方法：3）大多数建模方法假设相对较高的水平 模板结构和序列之间的序列同一性（> 35%）， 当大多数未知结构的蛋白质只是远亲时， 已知结构的蛋白质。PI提出基准，新的统计 分析，并为每个新算法的三个主要方面的同源性 建模：对齐，为插入删除构建骨架坐标 区域和侧链放置。主要工具将是贝叶斯 统计分析，包括分层模型和非参数方法 基于Dirichlet过程。序列大小的增加， 结构化数据库使新的统计分析及时，这既是因为 新数据提供的更强大的功能，以及众多的应用 由更多的序列和结构提供。

DESCRIPTION (provided by applicant): To improve human health, a goal of the human genome project is to translate the genome sequence into an understanding of human biology. An important step in this process is knowledge of the structure of human proteins and the effects of sequence polymorphisms on structure and function. Currently, the structures of only 1000 human proteins are known, but the structures of up to one third or so of human proteins can be modeled based on the structures of homologous proteins in the Protein Data Bank. This fraction will increase rapidly due to structural genomics efforts. Unfortunately, general principles of what works in homology modeling and what does not have remained elusive. The reasons for this are several: 1) insufficient benchmarking of most prediction methods; 2) reliance on out-of-date statistical analysis of protein structures, performed without modem methods of statistics: 3) most modeling methods assume a relatively high level of sequence identity (>35 percent) between template structure and sequence to be modeled, when most proteins of unknown structure are only distantly related to proteins of known structure. The PI proposes benchmarking, new statistical analysis, and new algorithms for each of the three major aspects of homology modeling: alignment, building backbone coordinates for insertiondeletion regions, and sidechain placement. The primary tools will be Bayesian statistical analysis, including hierarchical models and non-parametric methods based on the Dirichlet process. The increase in size of the sequence and structure databases makes the new statistical analysis timely, both because of the increased power the new data provide, and the numerous applications afforded by more sequences and structures.