PIPLOM: prediction of exogenous peptide loading on major histocompatibility complex class I molecules.

ABSTRACT:

Summary

The exogenous, i.e. in vitro, loading of peptides onto major histocompatibility complex (MHC) class I molecules is a key step in many immunology-related experimental workflows. Here, we provide a machine learning solution, PIPLOM, which is specifically tailored to predict whether peptides can be loaded exogenously onto an MHC class I molecule. Benchmarking on 38 unseen epitopes with in-house ELISA (enzyme-linked immunosorbent assay) experiments showed that PIPLOM is outperforming well-established methods such as NETMHCpan-4.0 or MHCflurry, which are commonly used for the related task of predicting epitope HLA (human leukocyte antigen) haplotype specificity.

Availability and implementation

Source code and data are available as Zenodo package 10.5281/zenodo.1377

SUBMITTER: Schmidt F 

PROVIDER: S-EPMC11904885 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature