PepRank

Streamlining Vaccine Antigen Discovery

PepRank is a pioneering model and web service for predicting Major Histocompatibility Complex (MHC) and peptide elution. Its training methodology focuses on peptide rankings predictions, reducing the need for extensive experiments and making them cost-effective. Ideal for scenarios where researchers need to experimentally verify a small, targeted set of peptides.

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Cite: Enhancing Antigenic Peptide Discovery: Improved MHC-I Binding Prediction and Methodology

@article{GIZINSKI20241,
title = {Enhancing antigenic peptide discovery: Improved MHC-I binding prediction and methodology},
journal = {Methods},
volume = {224},
pages = {1-9},
year = {2024},
issn = {1046-2023},
doi = {https://doi.org/10.1016/j.ymeth.2024.01.016},
url = {https://www.sciencedirect.com/science/article/pii/S1046202324000306},
author = {Stanisław Giziński and Grzegorz Preibisch and Piotr Kucharski and Michał Tyrolski and Michał Rembalski and Piotr Grzegorczyk and Anna Gambin},
keywords = {Major histocompatibility complex, BERT, Peptide presentation prediction, Benchmark dataset},
abstract = {The Major Histocompatibility Complex (MHC) is a critical element of the vertebrate cellular immune system, responsible for presenting peptides derived from intracellular proteins. MHC-I presentation is pivotal in the immune response and holds considerable potential in the realms of vaccine development and cancer immunotherapy. This study delves into the limitations of current methods and benchmarks for MHC-I presentation. We introduce a novel benchmark designed to assess generalization properties and the reliability of models on unseen MHC molecules and peptides, with a focus on the Human Leukocyte Antigen (HLA)–a specific subset of MHC genes present in humans. Finally, we introduce HLABERT, a pretrained language model that outperforms previous methods significantly on our benchmark and establishes a new state-of-the-art on existing benchmarks.}
}