MULTIPLE REPRESENTATIONS OF BIOLOGICAL SEQUENCES

生物序列的多重表示

基本信息

批准号：
6146063
负责人：
DOUGLAS L BRUTLAG
金额：
$ 5.6万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
1994
资助国家：
美国
起止时间：
1994-09-01 至 1999-12-06
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/6146063
关键词：
artificial intelligence biochemical evolution computer assisted sequence analysis hydrogen bond hydropathy ionic bond model design /development molecular biology information system physical model protein sequence protein structure function structural biology

项目摘要

The long term goal of our research is to understand the flow of information from the genome to the phenotype of organisms. In this proposal, we will attempt to use Bayesian networks and near-optimal sequence alignments to represent protein secondary structures and motifs. A Bayesian network describes the likelihood of amino acids at each position in a motif as well as the dependence of amino acids in one position on the amino acids at other position. Hence, Bayesian networks can describe both the conservation of amino acids at single positions and the conservation of correlations between two positions simultaneously. Conserved amino acids result from evolutionary selection for a specific amino acid or type of amino acid at one position in a protein structure. These positions often have important functional or structural requirements. Correlated changes between amino acids generally result from side-chain side-chain interactions between pairs of amino acids in a protein's structure. The types of correlations we have represented with Bayesian networks include electrostatic charges, hydrophobicity, hydrogen- bond donor and acceptor and inversely correlated packing volumes among others. These Bayesian networks can be used to 1) discover side-chain side--chain interactions within protei motifs and 2) to search sequence databases for motifs showing both correlations and conserved amino acids. Near-optimal alignments between two sequences can display regions that have been more highly conserved or less highly conserved using the information contained in only two sequences. The most highly conserved region correspond to the most highly structured regions and the most highly variable regions correspond to loops and coils and other hypervariable regions. We propose to use near-optimal alignments to display conserved secondary structures of proteins and hypervariable regions. We will use secondary-structure specific amino acid substitution matrices to provide specificity. The goals of this proposal are to 1) build a database of Bayesian networks that represent protein motifs, 2) test these networks for their ability to detect motifs using test sets and crossvalidation methods, 3) compare these networks with other methods for searching protein databases , 4) build an integrated set of Bayesian networks to predict protein secondary structure, 5) compare the prediction of protein secondary structure with existing method 6) build a near-optimal sequence alignment workbench, and 7) predict structured and unstructured regions in proteins from near- optimal alignments.

我们研究的长期目标是了解从基因组到生物表型的信息。在这个提案，我们将尝试使用贝叶斯网络和近乎最佳的网络代表蛋白质二级结构和基序的序列比对。贝叶斯网络描述了每种氨基酸的可能性在一个基序中的位置以及氨基酸在一个中的依赖性位于其他位置的氨基酸上。因此，贝叶斯网络可以描述单个位置的氨基酸的保护两个位置之间的相关性守恒同时。保守的氨基酸是由特定的进化选择而产生的氨基酸或类型的氨基酸在蛋白质结构中的一个位置。这些位置通常具有重要的功能或结构性要求。氨基酸之间的相关变化通常是由一对氨基酸之间的侧链侧链相互作用蛋白质的结构。我们代表的相关类型贝叶斯网络包括静电电荷，疏水性，氢气债券捐赠者和受体以及成反比的包装量其他的。这些贝叶斯网络可用于1）发现侧链侧 - 蛋白基序内的链相互作用和2）搜索序列图序的数据库显示相关和保守的氨基酸。两个序列之间的几乎最佳的对齐方式可以显示区域使用了更高的保守或不多的保守仅包含两个序列的信息。最保守的区域对应于最高度结构化的区域，最大的区域高度可变的区域对应于循环和线圈以及其他高变量区域。我们建议使用近距离的一致性显示蛋白质和高变量的保守二级结构地区。我们将使用二级结构特异性氨基酸取代矩阵提供特异性。该建议的目标是1）建立贝叶斯网络数据库代表蛋白质基序，2）测试这些网络的能力使用测试集和交叉验证方法检测基序，3）比较这些网络具有其他用于搜索蛋白质数据库的方法，4）建立一组集成的贝叶斯网络以预测蛋白质次要结构，5）将蛋白质二级结构的预测与现有方法6）构建一个近乎最佳的序列一致性工作台，然后 7）预测蛋白质中蛋白质的结构化和非结构化区域最佳对齐。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Genomics and computational molecular biology.

基因组学和计算分子生物学。

DOI：
10.1016/s1369-5274(98)80039-8
发表时间：
1998
期刊：
Current opinion in microbiology.
影响因子：
0
作者：
Brutlag,DL
通讯作者：
Brutlag,DL

A motion planning approach to flexible ligand binding.

灵活配体结合的运动规划方法。

DOI：
发表时间：
1999
期刊：
Proceedings. International Conference on Intelligent Systems for Molecular Biology
影响因子：
0
作者：
Singh,AP;Latombe,JC;Brutlag,DL
通讯作者：
Brutlag,DL

Discovering empirically conserved amino acid substitution groups in databases of protein families.

在蛋白质家族数据库中发现经验保守的氨基酸取代基。