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Automated protein-protein structure prediction of the T cell receptor-peptide major histocompatibility complex

Citation

Rollins, Zachary; Curtis, Matthew; George, Steven; Faller, Roland (2022), Automated protein-protein structure prediction of the T cell receptor-peptide major histocompatibility complex, Dryad, Dataset, https://doi.org/10.25338/B83S70

Abstract

The T Cell Receptor (TCR) recognition of a peptide-major histocompatibility complex (pMHC) is a crucial component of the adaptive immune response. The identification of TCR-pMHC pairs is a significant bottleneck in the implementation of TCR immunotherapies and may be augmented by computational methodologies that accelerate the rate of TCR discovery.  The ability to computationally design TCRs to a target pMHC will require an automated integration of next-generation sequencing, homology modeling, molecular dynamics (MD), and TCR ranking. We present a generic pipeline to evaluate patient-specific, sequence-based TCRs to a target pMHC. The most expressed TCRs from 16 colorectal cancer patients are homology modeled to target the CEA peptide using Modeller and ColabFold. Then, these TCR-pMHC structures are compared by performing an automated molecular dynamics equilibration. We find that Colabfold generates starting configurations that require, on average, an ~2.5X reduction in simulation time to equilibrate TCR-pMHC structures compared to Modeller. In addition, there are differences between equilibrated structures generated by Modeller and ColabFold.  Moreover, we identify TCR ranking criteria that may be used to prioritize TCRs for evaluation of in vitro immunogenicity.

Usage Notes

The ".gro" files are molecular structure formatted files and can be viewed with any standard molecular viewing software (e.g., Pymol, VMD, Chimera, etc.). Information on the ".gro" file format can be found at the following link: https://manual.gromacs.org/archive/5.0.3/online/gro.html#:~:text=Files%20with%20the%20gro%20file,as%20in%20the%20sample%20below