Bayesian Statistics and Algorithms for Homology Modeling

用于同源建模的贝叶斯统计和算法

• 批准号: 6990509
• 负责人: ROLAND L DUNBRACK
• 金额: $ 24.61万
• 依托单位: RESEARCH INST OF FOX CHASE CAN CTR
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-12-14 至 2008-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6990509
• 关键词: artificial intelligence; computer assisted sequence analysis; computer simulation; functional /structural genomics; mathematical model; model design /development; molecular biology information system; molecular dynamics; protein sequence; protein structure function; statistics /biometry; transposon /insertion element

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 The reasons for this are several: 1) insufficient benchmarking of most prediction methods; 2) reliance on out-of-date statistical analysis of protein structures, performed without modem methods of statistics: 3) most modeling methods assume a relatively high level of sequence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.

描述（由申请人提供）：改善人类健康是该组织的目标 人类基因组计划是将基因组序列转化为理解 人类生物学。这个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的影响 结构和功能。目前，只有 1000 种人类蛋白质的结构 是已知的，但多达三分之一左右的人类蛋白质的结构可以被 基于蛋白质数据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，同源建模的一般原则是什么？ 并非一直难以捉摸。造成这种情况的原因有几个：1） 大多数预测方法的基准测试不足； 2）依赖 过时的蛋白质结构统计分析，无需调制解调器即可进行 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一性（>35%） 当大多数结构未知的蛋白质只是远缘相关时，可以对其进行建模 已知结构的蛋白质。 PI 提出了基准测试、新的统计方法 同源性三个主要方面的分析和新算法 建模：对齐、构建插入删除的主干坐标 区域和侧链放置。主要工具将是贝叶斯 统计分析，包括层次模型和非参数方法 基于狄利克雷过程。序列大小的增加和 结构数据库使新的统计分析变得及时，因为 新数据提供的增强功能以​​及众多应用程序 由更多的序列和结构提供。

项目成果

Improved prediction of protein side-chain conformations with SCWRL4.

• DOI: 10.1002/prot.22488
• 发表时间: 2009-12
• 期刊: PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
• 影响因子: 2.9
• 作者: Krivov, Georgii G.;Shapovalov, Maxim V.;Dunbrack, Roland L., Jr.
• 通讯作者: Dunbrack, Roland L., Jr.

Domain definition and target classification for CASP6.

CASP6 的域定义和目标分类。

• DOI: 10.1002/prot.20717
• 发表时间: 2005
• 期刊: Proteins
• 影响因子: 2.9
• 作者: Tress,Michael;Tai,Chin-Hsien;Wang,Guoli;Ezkurdia,Iakes;López,Gonzalo;Valencia,Alfonso;Lee,Byungkook;DunbrackJr,RolandL
• 通讯作者: DunbrackJr,RolandL

PISCES: recent improvements to a PDB sequence culling server.

• DOI: 10.1093/nar/gki402
• 发表时间: 2005-07-01
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Wang G;Dunbrack RL Jr
• 通讯作者: Dunbrack RL Jr