PIPLOM: prediction of exogenous peptide loading on major histocompatibility complex class I molecules
Year: 2024
Type: article
Abstract: Summary: , 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.13771214. PIPLOM is available as a web service at https://piplom.immunoscape.com/.
Source: Bioinformatics Advances
Authors: Florian Schmidt, Kanxing Wu, Lorenz Gerber, Florence Chioh Wen Jing, Daniel Carbajo, Glenn Wong Choon Lim, Melissa Wirawan, Christine Eng, Katja Fink, Daniel T MacLeod, Michael Fehlings, Andreas Wilm
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,917
OpenAlex ID: W4408140545