Bioinformatics approaches to characterizing amino acid function.

表征氨基酸功能的生物信息学方法。

基本信息

批准号：
7220054
负责人：
SEAN DAVID MOONEY
金额：
$ 15.51万
依托单位：
INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-04-24 至 2009-04-23
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7220054
关键词：
Address Ally Amino Acids Biochemistry Bioinformatics Biology Classification Collaborations Computational Biology Computers Data Data Set Databases Descriptor Disease Elements Evolution Funding Gene Proteins Genes Genome Genomics Goals Hand Health Homologous Gene Indium Informatics Information Systems Knowledge Laboratory Research Language Machine Learning Malignant Neoplasms Methods Modeling Molecular Molecular Biology Mutation Nucleic Acids Nucleotides Outcome Pharmacogenetics Positioning Attribute Productivity Proteins Range Research Research Personnel Resolution Resources Scientist Sequence Homologs Site Structural Biologist Structural Models Structural Protein Structure Structure-Activity Relationship Tertiary Protein Structure Testing Transcript Variant Work base comparative computer science drug discovery experience genome sequencing innovation knowledge base multidisciplinary novel novel strategies paralogous gene programs protein function protein protein interaction protein structure research study tool web interface

项目摘要

DESCRIPTION (provided by applicant): Identifying residues of importance in the protein products of genes is a challenging and important problem for informatics, genome annotation, molecular biology, biochemistry and drug discovery. Functional annotation of genes is inherently hierarchical; genes can be annotated at the level of genome sequence, transcript variant, protein product, protein domain, nucleotide or amino acid. Only a few resources annotate protein function at the level of the amino acid and language relating residue function and gene product sequence, structure and expression is challenging. To address this, I am investigating how sequence, evolutionary and structural descriptors can be used to quantify function. I am applying this knowledge to develop methods that can associate residues with known functional annotations, perform annotation transfer onto an experimentally determined or modeled protein structure, and determine the likely molecular effects of mutation, thus creating a framework for residue annotation. One of the greatest challenges for the computational biologist is identifying features (or attributes) that are useful for classification of genomic data. With this effort, we will continue our work describing novel features for classification of functional sites and we will test them using supervised machine learning tools. We will do this by, 1) testing the power of several diverse functional features for classification of catalytic residues in proteins, 2) applying these features to other important residue functional annotation problems, and 3) evaluate features based on homologous sequences. This research is important for understanding the molecular basis of diseases such as cancer and pharmacogenetics data from a molecular perspective. When completed, scientists will have a rich set of data and tools for basic health research.

描述（由申请人提供）：识别基因蛋白质产品中重要性的残留物是信息学，基因组注释，分子生物学，生物化学和药物发现的具有挑战性且重要的问题。基因的功能注释本质上是分层的。可以在基因组序列，转录物变异，蛋白质产物，蛋白质结构域，核苷酸或氨基酸的水平上注释基因。在氨基酸和语言水平上，只有少数资源注释蛋白质功能，与残基功能以及基因产物序列，结构和表达相关。为了解决这个问题，我正在研究如何使用序列，进化和结构描述符来量化功能。我将这些知识应用于开发可以将残基与已知功能注释相关联的方法，将注释转移执行到实验确定或建模的蛋白质结构上，并确定突变的可能分子效应，从而为残基注释创造框架。计算生物学家的最大挑战之一是识别可用于基因组数据分类的特征（或属性）。通过这项努力，我们将继续工作，描述功能站点分类的新颖功能，并将使用监督的机器学习工具对其进行测试。我们将通过1）测试蛋白质中催化残基的几种各种功能特征的功率，2）将这些特征应用于其他重要的残基功能注释问题，3）基于同源序列评估特征。这项研究对于从分子的角度理解癌症和药物遗传学数据的分子基础很重要。完成后，科学家将拥有丰富的数据和工具，用于基本健康研究。