Bayesian Statistics and Algorithms for Homology Modeling

简介(申请人提供)：为改善人类健康， 人类基因组计划是将基因组序列转化为对 人类生物学的。这一过程中的一个重要步骤是了解 人类蛋白质的结构及序列多态对蛋白质结构的影响 结构和功能。目前，只有1000个人类蛋白质的结构 是已知的，但多达三分之一左右的人类蛋白质的结构可能是 基于蛋白质数据中同源蛋白质的结构进行建模 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，在同源模型中什么是有效的，什么是通用原则 并不是一直难以捉摸。出现这种情况的原因有几个：1) 对大多数预测方法的基准确定不足；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执行 统计方法：3)大多数建模方法假设水平相对较高 模板结构和序列之间的序列同源性(&gt；35%) 当大多数未知结构的蛋白质只是远距离相关时，被建模 到已知结构的蛋白质。PI提出了标杆，新的统计 同调的三个主要方面的分析，以及新的算法 建模：对齐，构建用于插入和删除的主干坐标 面域和边链放置。主要的工具将是贝叶斯 统计分析，包括层次模型和非参数方法 基于狄利克雷特过程。序列大小的增加和 结构化数据库使新的统计分析更及时，这既是因为 新数据提供的强大功能以及众多应用程序 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 The reasons for this are several: 1) insufficient benchmarking of most prediction methods; 2) reliance on out-of-date statistical analysis of protein structures, performed without modem methods of statistics: 3) most modeling methods assume a relatively high level of sequence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.