A flexible docking approach for prediction of T cell receptor-peptide-MHC complexes
dc.contributor.author | Pierce, Brian G. | |
dc.contributor.author | Weng, Zhiping | |
dc.date | 2022-08-11T08:07:59.000 | |
dc.date.accessioned | 2022-08-23T15:38:21Z | |
dc.date.available | 2022-08-23T15:38:21Z | |
dc.date.issued | 2013-01-01 | |
dc.date.submitted | 2013-02-22 | |
dc.identifier.citation | Protein Sci. 2013 Jan;22(1):35-46. doi: 10.1002/pro.2181. <a href="http://dx.doi.org/10.1002/pro.2181">Link to article on publisher's site</a> | |
dc.identifier.issn | 0961-8368 (Linking) | |
dc.identifier.doi | 10.1002/pro.2181 | |
dc.identifier.pmid | 23109003 | |
dc.identifier.uri | http://hdl.handle.net/20.500.14038/25919 | |
dc.description.abstract | T cell receptors (TCRs) are immune proteins that specifically bind to antigenic molecules, which are often foreign peptides presented by major histocompatibility complex proteins (pMHCs), playing a key role in the cellular immune response. To advance our understanding and modeling of this dynamic immunological event, we assembled a protein-protein docking benchmark consisting of 20 structures of crystallized TCR/pMHC complexes for which unbound structures exist for both TCR and pMHC. We used our benchmark to compare predictive performance using several flexible and rigid backbone TCR/pMHC docking protocols. Our flexible TCR docking algorithm, TCRFlexDock, improved predictive success over the fixed backbone protocol, leading to near-native predictions for 80% of the TCR/pMHC cases among the top 10 models, and 100% of the cases in the top 30 models. We then applied TCRFlexDock to predict the two distinct docking modes recently described for a single TCR bound to two different antigens, and tested several protein modeling scoring functions for prediction of TCR/pMHC binding affinities. This algorithm and benchmark should enable future efforts to predict, and design of uncharacterized TCR/pMHC complexes. | |
dc.language.iso | en_US | |
dc.relation | <a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=23109003&dopt=Abstract">Link to Article in PubMed</a> | |
dc.relation.url | http://dx.doi.org/10.1002/pro.2181 | |
dc.subject | Receptors, Antigen, T-Cell | |
dc.subject | Genes, MHC Class II | |
dc.subject | Major Histocompatibility Complex | |
dc.subject | Molecular Docking Simulation | |
dc.subject | Biochemistry, Biophysics, and Structural Biology | |
dc.subject | Bioinformatics | |
dc.subject | Computational Biology | |
dc.subject | Immunity | |
dc.subject | Molecular Biology | |
dc.title | A flexible docking approach for prediction of T cell receptor-peptide-MHC complexes | |
dc.type | Journal Article | |
dc.source.journaltitle | Protein science : a publication of the Protein Society | |
dc.source.volume | 22 | |
dc.source.issue | 1 | |
dc.identifier.legacycoverpage | https://escholarship.umassmed.edu/bioinformatics_pubs/6 | |
dc.identifier.contextkey | 3761387 | |
html.description.abstract | <p>T cell receptors (TCRs) are immune proteins that specifically bind to antigenic molecules, which are often foreign peptides presented by major histocompatibility complex proteins (pMHCs), playing a key role in the cellular immune response. To advance our understanding and modeling of this dynamic immunological event, we assembled a protein-protein docking benchmark consisting of 20 structures of crystallized TCR/pMHC complexes for which unbound structures exist for both TCR and pMHC. We used our benchmark to compare predictive performance using several flexible and rigid backbone TCR/pMHC docking protocols. Our flexible TCR docking algorithm, TCRFlexDock, improved predictive success over the fixed backbone protocol, leading to near-native predictions for 80% of the TCR/pMHC cases among the top 10 models, and 100% of the cases in the top 30 models. We then applied TCRFlexDock to predict the two distinct docking modes recently described for a single TCR bound to two different antigens, and tested several protein modeling scoring functions for prediction of TCR/pMHC binding affinities. This algorithm and benchmark should enable future efforts to predict, and design of uncharacterized TCR/pMHC complexes.</p> | |
dc.identifier.submissionpath | bioinformatics_pubs/6 | |
dc.contributor.department | Program in Bioinformatics and Integrative Biology | |
dc.contributor.department | Department of Biochemistry and Molecular Pharmacology | |
dc.source.pages | 35-46 |