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The atomic-level physiochemical determinants of T cell receptor dissociation kinetics

Citation

Rollins, Zachary et al. (2021), The atomic-level physiochemical determinants of T cell receptor dissociation kinetics, Dryad, Dataset, https://doi.org/10.25338/B8R33G

Abstract

The force-dependent bond lifetime of a T Cell Receptors (TCR) to 17 peptide-Major Histocompatibility Complexes (pMHCs) was simulated using Steered Molecular Dynamics. Physiochemical descriptors of the TCR-pMHC interaction during simulation were then filtered and selected to understand the best feature sets that predict bond lifetime. This methodology sets precedence on the rational design of TCRs via force-dependent bond lifetime enhancement.

Methods

Data was collected and processed on high performance computers (hpc1/hpc2) at the University of California, Davis College of Engineering.

Usage Notes

Datasets for quaternary (data_quaternary_fs.csv) and secondary (data_secondary_fs.csv) features for all TCR-pMHC structures. Root mean square deviations (RMSD) for the equilibration of all TCR-pMHC structures in the format (rmsd_ mutant.png).  Triplicate starting configuration of all TCR-pMHC structures before steered molecular dynamics simulation in the format (mutant_1.gro, mutant_2.gro, and mutant_3.gro). Pearson (pearson_quaternary.csv, pearson_secondary.csv) and Spearman (spearman_quaternary.csv, spearman_secondary.csv) correlation coefficients between all physiochemical features. Results from hyperparameter search and cross-validation include: the elastic net ranked top ten features (top_qfeatures_ranked_hp_gridsearch.csv, top_qfeatures_cv.csv, top_sfeatures_ranked_hp_gridsearch.csv, and top_sfeatures_cv.csv), the elastic net exhaustive search feature set selections (top3_qfeatures_hp_gridsearch_cv.csv, top3_qfeatures_exhaustivesearch_cv.csv,  top3_qfeatures.csv, top5_qfeatures_hp_gridsearch_cv.csv, top5_qfeatures_exhaustivesearch_cv.csv, top5_qfeatures.csv, top7_qfeatures_hp_gridsearch_cv.csv, top7_qfeatures_exhaustivesearch_cv.csv, top7_qfeatures.csv, top3_sfeatures_hp_gridsearch_cv.csv, top3_sfeatures_exhaustivesearch_cv.csv, top3_sfeatures.csv, top5_sfeatures_hp_gridsearch_cv.csv, top5_sfeatures_exhaustivesearch_cv.csv, top5_sfeatures.csv, top7_sfeatures_hp_gridsearch_cv.csv, top7_sfeatures_exhaustivesearch_cv.csv, and top7_sfeatures.csv), and the machine learning algorithms (top1_qfeature_best_MLmodel_cv.csv, top1_qfeature_ML_hp_gridsearch_cv.csv, top3_qfeatures_best_MLmodel_cv.csv, top3_qfeatures_ML_hp_gridsearch_cv.csv, top5_qfeatures_best_MLmodel_cv.csv, top5_qfeatures_ML_hp_gridsearch_cv.csv, top7_qfeatures_best_MLmodel_cv.csv, top7_qfeatures_ML_hp_gridsearch_cv.csv, top1_sfeature_best_MLmodel_cv.csv, top1_sfeature_ML_hp_gridsearch_cv.csv, top3_sfeatures_best_MLmodel_cv.csv, top3_sfeatures_ML_hp_gridsearch_cv.csv, top5_sfeatures_best_MLmodel_cv.csv, top5_sfeatures_ML_hp_gridsearch_cv.csv, top7_sfeatures_best_MLmodel_cv.csv, and top7_sfeatures_ML_hp_gridsearch_cv.csv). The README file gives links data to figures in manuscript (README.txt).