DeepLigand: accurate prediction of MHC class I ligands using peptide embedding
Name
btz330.pdf
Description
Published version
Size
691.92 KB
Format
Adobe PDF
Checksum (MD5)
405cd81cf53da3cba82c96e687641e0a
Author(s)
Zeng, Haoyang
Gifford, David K
Date Issued
July 2019
Journal
Bioinformatics
Publisher
Oxford University Press (OUP)
Citation
Zeng, Haoyang and David K. Gifford. “DeepLigand: accurate prediction of MHC class I ligands using peptide embedding.” Bioinformatics, 35, 14 (July 2019): i278–i283 © 2019 The Author(s)
Version
Final published version
Abstract
Motivation: The computational modeling of peptide display by class I major histocompatibility complexes (MHCs) is essential for peptide-based therapeutics design. Existing computational methods for peptide-display focus on modeling the peptide-MHC-binding affinity. However, such models are not able to characterize the sequence features for the other cellular processes in the peptide display pathway that determines MHC ligand selection. Results: We introduce a semi-supervised model, DeepLigand that outperforms the state-of-the-art models in MHC Class I ligand prediction. DeepLigand combines a peptide language model and peptide binding affinity prediction to score MHC class I peptide presentation. The peptide language model characterizes sequence features that correspond to secondary factors in MHC ligand selection other than binding affinity. The peptide embedding is learned by pre-training on natural ligands, and can discriminate between ligands and non-ligands in the absence of binding affinity prediction. Although conventional affinity-based models fail to classify peptides with moderate affinities, DeepLigand discriminates ligands from non-ligands with consistently high accuracy.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/BIOINFORMATICS/BTZ330
