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Predicting Peptide-MHC Binding Affinities With Imputed Training Data
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  • Alex Rubinsteyn,
  • Timothy O'Donnell,
  • Nandita Damaraju,
  • Jeff Hammerbacher
Alex Rubinsteyn
Icahn School of Medicine at Mount Sinai
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Timothy O'Donnell
Icahn School of Medicine at Mount Sinai
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Nandita Damaraju
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Jeff Hammerbacher
Icahn School of Medicine at Mount Sinai
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Abstract

Predicting the binding affinity between MHC proteins and their peptide ligands is a key problem in computational immunology. State of the art performance is currently achieved by the allele-specific predictor NetMHC and the pan-allele predictor NetMHCpan, both of which are ensembles of shallow neural networks. We explore an intermediate between allele-specific and pan-allele prediction: training allele-specific predictors with synthetic samples generated by imputation of the peptide-MHC affinity matrix. We find that the imputation strategy is useful on alleles with very little training data. We have implemented our predictor as an open-source software package called MHCflurry and show that MHCflurry achieves competitive performance to NetMHC and NetMHCpan.