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

## TL;DR

This paper introduces PIPLOM, a machine learning tool that predicts if peptides can be loaded onto MHC class I molecules in vitro, outperforming existing methods.

## Contribution

The novel contribution is a machine learning model specifically designed for exogenous peptide loading prediction on MHC class I molecules.

## Key findings

- PIPLOM outperformed established tools like NETMHCpan-4.0 and MHCflurry in predicting exogenous peptide loading.
- Benchmarking on 38 epitopes showed strong performance of PIPLOM in in-house ELISA experiments.

## Abstract

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.

Source code and data are available as Zenodo package 10.5281/zenodo.13771214. PIPLOM is available as a web service at https://piplom.immunoscape.com/.