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TCRmodel: high resolution modeling of T cell receptors from sequence.
Gowthaman, Ragul; Pierce, Brian G.
Afiliação
  • Gowthaman R; University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA.
  • Pierce BG; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA.
Nucleic Acids Res ; 46(W1): W396-W401, 2018 07 02.
Article em En | MEDLINE | ID: mdl-29790966
T cell receptors (TCRs), along with antibodies, are responsible for specific antigen recognition in the adaptive immune response, and millions of unique TCRs are estimated to be present in each individual. Understanding the structural basis of TCR targeting has implications in vaccine design, autoimmunity, as well as T cell therapies for cancer. Given advances in deep sequencing leading to immune repertoire-level TCR sequence data, fast and accurate modeling methods are needed to elucidate shared and unique 3D structural features of these molecules which lead to their antigen targeting and cross-reactivity. We developed a new algorithm in the program Rosetta to model TCRs from sequence, and implemented this functionality in a web server, TCRmodel. This web server provides an easy to use interface, and models are generated quickly that users can investigate in the browser and download. Benchmarking of this method using a set of nonredundant recently released TCR crystal structures shows that models are accurate and compare favorably to models from another available modeling method. This server enables the community to obtain insights into TCRs of interest, and can be combined with methods to model and design TCR recognition of antigens. The TCRmodel server is available at: http://tcrmodel.ibbr.umd.edu/.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Software / Receptores de Antígenos de Linfócitos T / Modelos Moleculares / Homologia Estrutural de Proteína / Antígenos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Nucleic Acids Res Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Software / Receptores de Antígenos de Linfócitos T / Modelos Moleculares / Homologia Estrutural de Proteína / Antígenos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Nucleic Acids Res Ano de publicação: 2018 Tipo de documento: Article