Machine learning optimization of peptides for presentation by class II MHCs

Zheng Dai; Brooke D Huisman; Haoyang Zeng; Brandon Carter; Siddhartha Jain; Michael E Birnbaum; David K Gifford

doi:10.1093/bioinformatics/btab131

Machine learning optimization of peptides for presentation by class II MHCs

Bioinformatics. 2021 Oct 11;37(19):3160-3167. doi: 10.1093/bioinformatics/btab131.

Authors

Zheng Dai^{1

2}, Brooke D Huisman³, Haoyang Zeng^{1

2}, Brandon Carter^{1

2}, Siddhartha Jain^{1

2}, Michael E Birnbaum³, David K Gifford^{1

2

3}

Affiliations

¹ Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
² Department of Computer Science and Electrical Engineering, MIT, Cambridge, MA, USA.
³ Department of Biological Engineering, MIT, Cambridge, MA, USA.

Abstract

Summary: T cells play a critical role in cellular immune responses to pathogens and cancer and can be activated and expanded by Major Histocompatibility Complex (MHC)-presented antigens contained in peptide vaccines. We present a machine learning method to optimize the presentation of peptides by class II MHCs by modifying their anchor residues. Our method first learns a model of peptide affinity for a class II MHC using an ensemble of deep residual networks, and then uses the model to propose anchor residue changes to improve peptide affinity. We use a high throughput yeast display assay to show that anchor residue optimization improves peptide binding.

Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract

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