Improved predictive models of peptide presentation on MHC I

O'Donnell, T.; Rubinsteyn, A.; Laserson, U.

doi:10.1101/2020.03.28.013714

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bioRxiv · 10.1101/2020.03.28.013714

Improved predictive models of peptide presentation on MHC I

O'Donnell, T.·Rubinsteyn, A.·Laserson, U.

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Abstract

Computational prediction of the peptides presented on MHC class I proteins is an important tool for studying T cell immunity. The data available to develop such predictors has expanded with the use of mass spec to identify naturally-presented MHC ligands. In addition to elucidating binding motifs, the identified ligands also reflect the antigen processing steps that occur prior to MHC binding. Here, we developed an integrated predictor of MHC I presentation that combines new models for MHC I binding and antigen processing. Considering only peptides first predicted by the binding model to bind strongly to MHC, the antigen processing model is trained to discriminate published mass spec-identified MHC I ligands from unobserved peptides. The integrated model outperformed the two individual components as well as NetMHCpan 4.0 and MixMHCpred 2.0.2 on held-out mass spec experiments. Our predictors are implemented in the MHCflurry package, version 1.6.0 (github.com/openvax/mhcflurry).

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O'Donnell, T., Rubinsteyn, A., Laserson, U.. 2020-03-30. Improved predictive models of peptide presentation on MHC I. https://doi.org/10.1101/2020.03.28.013714

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