Program in Bioinformatics and Integrative Biology
Bioinformatics | Cell Biology | Computational Biology | Integrative Biology
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 alphabeta and gammadelta 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 alpha chain but a different beta chain, highlighting the likely dominance of the conserved alpha 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.
DOI of Published Version
BMC Bioinformatics. 2014 Sep 26;15(1):319. doi: 10.1186/1471-2105-15-319. Link to article on publisher's site
Pierce BG, Vreven T, Weng Z. (2014). Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes. Open Access Articles. https://doi.org/10.1186/1471-2105-15-319. Retrieved from https://escholarship.umassmed.edu/oapubs/2436
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