NetMHCpan-4.2: improved prediction of CD8+ epitopes by use of transfer learning and structural features.

ABSTRACT: Identification of CD8+ T cell epitopes is crucial for advancing vaccine development and immunotherapy strategies. Traditional methods for predicting T cell epitopes primarily focus on MHC presentation, leveraging immunopeptidome data. Recent advancements however suggest significant performance improvements through transfer learning and refinement using epitope data. To further investigate this, we here develop an enhanced MHC class I (MHC-I) antigen presentation predictor by integrating newly curated binding affinity and eluted ligand datasets, expanding MHC allele coverage, and incorporating novel input features related to the structural constraints of the MHC-I peptide-binding cleft. We next apply transfer learning using experimentally validated pathogen- and cancer-derived epitopes from

SUBMITTER: Nilsson JB 

PROVIDER: S-EPMC12367478 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature