A robust deep learning workflow to predict CD8 + T-cell epitopes.

ABSTRACT:

Background

T-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes.

Methods

We developed TRAP (T-cell recognition potential of HLA-I presented pe

SUBMITTER: Lee CH 

PROVIDER: S-EPMC10498576 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature