PREDICTING MOLECULAR FUNCTION FOR PROTEINS USING GO AND STRUCTURE INFORMATION

使用 GO 和结构信息预测蛋白质的分子功能

• 批准号: 7602214
• 负责人: DAVID BAKER
• 金额: $ 6.35万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7602214
• 关键词: Automation; Biological; Computer Retrieval of Information on Scientific Projects Database; Databases; Funding; Genes; Goals; Grant; Hour; Institution; Knowledge; Maps; Molecular; Ontology; Open Reading Frames; Procedures; Process; Production; Protein Databases; Proteins; Publications; Research; Research Personnel; Resources; Set protein; Source; Structure; United States National Institutes of Health; Work; Yeasts; man; protein function

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. In the past, when attempting to determine molecular function for a large set of proteins from known and predicted protein strctures, has been a very labor-intensive enterprise. Not only must the work be done to determine the structure, which is particularly challenging and labor-intensive on the part of ab intio protein stricture predictions, but those structures must be mapped to protein molecular function and correlated with what is known biologically about the protein to pick the most likely correct answers. In our recent publication covering the uncharacterized ORFs in yeast, this assignment of function was attempted for roughly 100 open reading frames in S. cerevesiae. This process involved literally hundreds of man hours. Our goal is complete automation of this procedure--resulting in the production of molecular function predictions from the Gene Ontology database for proteins of unknown function. This will be accomplished using the Gene Ontology database's annotation of gene products as representing our biological knowledge for a protein.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 过去，试图从已知和预测的蛋白质strctures中确定大量蛋白质的分子功能时，一直是劳动密集型的企业。 不仅必须完成工作来确定结构，这在AB Intio蛋白狭窄的预测中特别具有挑战性和劳动密集型，而且这些结构必须映射到蛋白质分子功能上，并与对蛋白质上的知名度相关，以选择最可能正确的答案。 在我们最近涵盖酵母中未表征的ORF的出版物中，该功能的分配尝试在S. cerevesiae进行大约100个开放阅读框架。 这个过程涉及数百个小时。 我们的目标是完全自动化此过程 - 从基因本体学数据库产生分子功能预测的反应，以实现未知功能的蛋白质。 这将使用基因本体数据库对基因产物的注释来实现，以代表我们对蛋白质的生物学知识。