A molecular dynamics investigation of N-glycosylation effects on T-cell receptor kinetics

Abstract

The binding interaction between the T-cell receptor (TCR) and peptide-major histocompatibility complex (pMHC) is modulated by several factors (known and unknown), however, investigations into effects of glycosylation are limited. A fully glycosylated computational model of the TCR bound to the pMHC is developed to investigate the effects of glycosylation on dissociation kinetics from the pMHC. Here, we examine the effects of N-glycosylation on TCR-pMHC bond strength using steered molecular dynamic simulations. N-glycosylation is a post-translational modification that adds sugar moieties to molecules and can modulate the activity of several immune molecules. Using a TCR-pMHC pair found in melanoma as a case study, our study demonstrates that N-glycosylation of the TCR-pMHC alters the proteins’ conformation; increases the bond lifetime; and increases the number of hydrogen bonds and Lennard-Jones Contacts involved in the TCR-pMHC bond. We find that weak glycan-protein or glycan-glycan interactions impact the equilibrated structure of the TCR and pMHC leading to an increase in the overall bond strength of the TCR-pMHC complex including the duration and energetic strength under constant load. These results indicate that N-glycosylation plays an important role in the TCR-pMHC bond and should be considered in future computational and experimental studies.

Communicated by Ramaswamy H. Sarma

Keywords:

• Glycosylation
• MD simulation
• TCR receptor

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The starting configurations of SMD simulations for glycosylated and aglycosylated structures have been available on a Dryad repository: https://doi.org/10.25338/B8Q05W. Moreover, csv files with all interactions as well as high resolution (300 DPI) interaction maps have been added to the Dryad repository. In addition, the trajectories and principal component projected trajectories (PC1-PC3) have been compiled and uploaded to the repository. Custom python scripts relevant to the production of figures are available on Github repository: https://github.com/zrollins/TCRglyco.

Authors contribution

ZAR performed simulations, analyzed and interpreted the data, and wrote the manuscript. BSH performed simulations and edited the manuscript. SCG designed the experiments, analyzed and interpreted the data, and secured the funding. RF designed the experiments, analyzed and interpreted the data, wrote the manuscript, and secured funding and computer time.

Additional information

Funding

Simulations were performed on the hpc1/hpc2 clusters in the UC Davis, College of Engineering. BSH was partially supported by LLNL’s LDRD program, under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. ZAR was partially supported by startup funding to SCG from the Department of Biomedical Engineering.