The Transporter Classification Database (TCDB)

运输者分类数据库 (TCDB)

• 批准号: 7079749
• 负责人: MILTON H. SAIER
• 金额: $ 29.33万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2010-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7079749
• 关键词: biochemical evolution; chemical information system; classification; computer program /software; computer system design /evaluation; transport proteins

DESCRIPTION (provided by applicant): Transport systems provide essential functions for all living cells. Their importance to medicine cannot be overemphasized as (1) they provide a basis for multidrug resistance in pathogenic bacteria, fungi and protozoans as well as in tumor cells, (2) they are defective in numerous human genetic diseases, and (3) they are essential elements of toxin secretion systems by virtually all pathogenic organisms. We have developed a classification system for transport proteins called the Transporter Classification (TC) system. The TC system is a functional/phylogenetic system designed for the classification of all transmembrane transport proteins found in living organisms on Earth. It parallels but differs from the strictly functional EC system, developed decades ago by the Enzyme Commission of the International Union of Biochemistry and Molecular Biology (IUBMB) for the classification of enzymes. The TC system has recently been adopted by the IUBMB as the internationally acclaimed system to the classification of transporters. TCDB is a curated repository for factual information compiled from more than 10,000 references, encompassing approximately 3,000 representative transport proteins and putative transporters, classified into about 400 families. The primary goal of this proposal is to facilitate the updating, expansion and improvement of TCDB for use by the international scientific community. Our primary goals are first, to automate text classification and information extraction software so as to facilitate the continual updating of TCDB as newly published information about transporters becomes available (Specific Aims 1-3), and second, to devise software for the semiautomatic prediction of transport functions for putative transporters for which complete sequence data but little or no experimental data are available (Specific Aims 4-6). Our specific aims are to: (1) Incorporate into TCDB a text classification system for automatically identifying published literature describing newly characterized transport systems or previously characterized systems where new information is provided or old data are corrected. (2) Design information extraction software for incorporation of pertinent information from publications identified in specific aim #1 into TCDB. (3) Automate links to the primary literature from which the information in specific aim #2 was extracted as well as links to protein and operon analysis tools. (4) Incorporate software that will allow identification of motifs, signature sequences, protein domains, phylogenetic relationships and structural features for the proteins in TCDB and their homologues for the purpose of functional prediction. (5) Incorporate software that will facilitate the extraction of operon analysis data that can similarly be used for functional prediction. (6) Develop approaches that will allow integration of the different lines of functional information revealed in specific aims #4 and 5.

描述（由申请人提供）：运输系统为所有活细胞提供基本功能。它们对医学的重要性怎么强调都不为过，因为(1)它们为致病菌、真菌和原生动物以及肿瘤细胞的多药耐药提供了基础，(2)它们在许多人类遗传疾病中存在缺陷，(3)它们是几乎所有致病生物毒素分泌系统的基本要素。我们已经开发了一种转运蛋白的分类系统，称为转运蛋白分类系统。TC系统是一个功能/系统发育系统，用于对地球上活生物体中发现的所有跨膜转运蛋白进行分类。它与几十年前由国际生物化学和分子生物学联合会（IUBMB）酶委员会开发的用于酶分类的严格功能的EC系统相似但不同。转运商分类系统最近已被港管局采纳，成为国际公认的转运商分类系统。TCDB是一个精心策划的事实信息库，从10,000多篇参考文献中汇编而成，包括约3,000个代表性转运蛋白和假定转运蛋白，分为约400个科。本提案的主要目标是促进TCDB的更新、扩展和改进，以供国际科学界使用。我们的主要目标是，首先，自动化文本分类和信息提取软件，以便随着有关转运体的新发布信息的可用性而促进TCDB的不断更新（具体目标1-3）；其次，设计软件，用于半自动预测具有完整序列数据但很少或没有实验数据的假定转运体的转运函数（具体目标4-6）。我们的具体目标是：(1)在TCDB中加入一个文本分类系统，用于自动识别描述新特征运输系统或先前特征系统的已发表文献，其中提供了新信息或旧数据被纠正。(2)设计信息提取软件，将特定目标#1中确定的出版物中的相关信息纳入TCDB。(3)自动链接到主要文献，从这些文献中提取了特定目标#2中的信息，以及链接到蛋白质和操纵子分析工具。(4)结合软件，将允许识别基序，特征序列，蛋白质结构域，系统发育关系和结构特征的蛋白质在TCDB及其同源物的功能预测的目的。(5)结合有助于提取操纵子分析数据的软件，这些数据可以类似地用于功能预测。(6)制定方法，将具体目标#4和5中揭示的不同功能信息线整合起来。