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TCR3d: The T cell receptor structural repertoire database.
Gowthaman, Ragul; Pierce, Brian G.
Afiliação
  • Gowthaman R; University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA.
  • Pierce BG; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA.
Bioinformatics ; 35(24): 5323-5325, 2019 12 15.
Article em En | MEDLINE | ID: mdl-31240309
SUMMARY: T cell receptors (TCRs) are critical molecules of the adaptive immune system, capable of recognizing diverse antigens, including peptides, lipids and small molecules, and represent a rapidly growing class of therapeutics. Determining the structural and mechanistic basis of TCR targeting of antigens is a major challenge, as each individual has a vast and diverse repertoire of TCRs. Despite shared general recognition modes, diversity in TCR sequence and recognition represents a challenge to predictive modeling and computational techniques being developed to predict antigen specificity and mechanistic basis of TCR targeting. To this end, we have developed the TCR3d database, a resource containing all known TCR structures, with a particular focus on antigen recognition. TCR3d provides key information on antigen binding mode, interface features, loop sequences and germline gene usage. Users can interactively view TCR complex structures, search sequences of interest against known structures and sequences, and download curated datasets of structurally characterized TCR complexes. This database is updated on a weekly basis, and can serve the community as a centralized resource for those studying T cell receptors and their recognition. AVAILABILITY AND IMPLEMENTATION: The TCR3d database is available at https://tcr3d.ibbr.umd.edu/.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Bases de Dados Factuais Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Bases de Dados Factuais Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article