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The complexity, challenges and benefits of comparing two transporter classification systems in TCDB and Pfam.
Brief Bioinform ; 16(5): 865-72, 2015 Sep.
Article en En | MEDLINE | ID: mdl-25614388
ABSTRACT
Transport systems comprise roughly 10% of all proteins in a cell, playing critical roles in many processes. Improving and expanding their classification is an important goal that can affect studies ranging from comparative genomics to potential drug target searches. It is not surprising that different classification systems for transport proteins have arisen, be it within a specialized database, focused on this functional class of proteins, or as part of a broader classification system for all proteins. Two such databases are the Transporter Classification Database (TCDB) and the Protein family (Pfam) database. As part of a long-term endeavor to improve consistency between the two classification systems, we have compared transporter annotations in the two databases to understand the rationale for differences and to improve both systems. Differences sometimes reflect the fact that one database has a particular transporter family while the other does not. Differing family definitions and hierarchical organizations were reconciled, resulting in recognition of 69 Pfam 'Domains of Unknown Function', which proved to be transport protein families to be renamed using TCDB annotations. Of over 400 potential new Pfam families identified from TCDB, 10% have already been added to Pfam, and TCDB has created 60 new entries based on Pfam data. This work, for the first time, reveals the benefits of comprehensive database comparisons and explains the differences between Pfam and TCDB.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Proteínas / Bases de Datos de Proteínas Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2015 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Proteínas / Bases de Datos de Proteínas Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2015 Tipo del documento: Article