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Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes

BACKGROUND: T cell receptors (TCRs) can recognize diverse lipid and metabolite antigens presented by MHC-like molecules CD1 and MR1, and the molecular basis of many of these interactions has not been determined. Here we applied our protein docking algorithm TCRFlexDock, previously developed to perfo...

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Autores principales: Pierce, Brian G, Vreven, Thom, Weng, Zhiping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4261541/
https://www.ncbi.nlm.nih.gov/pubmed/25260513
http://dx.doi.org/10.1186/1471-2105-15-319
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author Pierce, Brian G
Vreven, Thom
Weng, Zhiping
author_facet Pierce, Brian G
Vreven, Thom
Weng, Zhiping
author_sort Pierce, Brian G
collection PubMed
description BACKGROUND: T cell receptors (TCRs) can recognize diverse lipid and metabolite antigens presented by MHC-like molecules CD1 and MR1, and the molecular basis of many of these interactions has not been determined. Here we applied our protein docking algorithm TCRFlexDock, previously developed to perform docking of TCRs to peptide-MHC (pMHC) molecules, to predict the binding of αβ and γδ TCRs to CD1 and MR1, starting with the structures of the unbound molecules. RESULTS: Evaluating against TCR-CD1d complexes with crystal structures, we achieved near-native structures in the top 20 models for two out of four cases, and an acceptable-rated prediction for a third case. We also predicted the structure of an interaction between a MAIT TCR and MR1-antigen that has not been structurally characterized, yielding a top-ranked model that agreed remarkably with a characterized TCR-MR1-antigen structure that has a nearly identical TCR α chain but a different β chain, highlighting the likely dominance of the conserved α chain in MR1-antigen recognition. Docking performance was improved by re-training our scoring function with a set of TCR-pMHC complexes, and for a case with an outlier binding mode, we found that alternative docking start positions improved predictive accuracy. We then performed unbound docking with two mycolyl-lipid specific TCRs that recognize lipid-bound CD1b, which represent a class of interactions that is not structurally characterized. Highly-ranked models of these complexes showed remarkable agreement between their binding topologies, as expected based on their shared germline sequences, while differences in residue-level interactions with their respective antigens point to possible mechanisms underlying their distinct specificities. CONCLUSIONS: Together these results indicate that flexible docking simulations can provide accurate models and atomic-level insights into TCR recognition of MHC-like molecules presenting lipid and other small molecule antigens. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-319) contains supplementary material, which is available to authorized users.
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spelling pubmed-42615412014-12-10 Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes Pierce, Brian G Vreven, Thom Weng, Zhiping BMC Bioinformatics Research Article BACKGROUND: T cell receptors (TCRs) can recognize diverse lipid and metabolite antigens presented by MHC-like molecules CD1 and MR1, and the molecular basis of many of these interactions has not been determined. Here we applied our protein docking algorithm TCRFlexDock, previously developed to perform docking of TCRs to peptide-MHC (pMHC) molecules, to predict the binding of αβ and γδ TCRs to CD1 and MR1, starting with the structures of the unbound molecules. RESULTS: Evaluating against TCR-CD1d complexes with crystal structures, we achieved near-native structures in the top 20 models for two out of four cases, and an acceptable-rated prediction for a third case. We also predicted the structure of an interaction between a MAIT TCR and MR1-antigen that has not been structurally characterized, yielding a top-ranked model that agreed remarkably with a characterized TCR-MR1-antigen structure that has a nearly identical TCR α chain but a different β chain, highlighting the likely dominance of the conserved α chain in MR1-antigen recognition. Docking performance was improved by re-training our scoring function with a set of TCR-pMHC complexes, and for a case with an outlier binding mode, we found that alternative docking start positions improved predictive accuracy. We then performed unbound docking with two mycolyl-lipid specific TCRs that recognize lipid-bound CD1b, which represent a class of interactions that is not structurally characterized. Highly-ranked models of these complexes showed remarkable agreement between their binding topologies, as expected based on their shared germline sequences, while differences in residue-level interactions with their respective antigens point to possible mechanisms underlying their distinct specificities. CONCLUSIONS: Together these results indicate that flexible docking simulations can provide accurate models and atomic-level insights into TCR recognition of MHC-like molecules presenting lipid and other small molecule antigens. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2105-15-319) contains supplementary material, which is available to authorized users. BioMed Central 2014-09-26 /pmc/articles/PMC4261541/ /pubmed/25260513 http://dx.doi.org/10.1186/1471-2105-15-319 Text en © Pierce et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Pierce, Brian G
Vreven, Thom
Weng, Zhiping
Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes
title Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes
title_full Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes
title_fullStr Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes
title_full_unstemmed Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes
title_short Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes
title_sort modeling t cell receptor recognition of cd1-lipid and mr1-metabolite complexes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4261541/
https://www.ncbi.nlm.nih.gov/pubmed/25260513
http://dx.doi.org/10.1186/1471-2105-15-319
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