DeepLigand: accurate prediction of MHC class I ligands using peptide embedding.

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 seco

SUBMITTER: Zeng H 

PROVIDER: S-EPMC6612839 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature