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Related Experiment Video

Updated: Jan 8, 2026

Enrich and Expand Rare Antigen-specific T Cells with Magnetic Nanoparticles
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Do Pseudosequences Matter in Neoantigen Prediction?

Adele Valeria, Rachel Karchin

    Biorxiv : the Preprint Server for Biology
    |December 22, 2025
    PubMed
    Summary

    Biologically informed pseudosequences are crucial for accurate neoantigen prediction in personalized cancer vaccines. Curated pseudosequences, especially those of 30-35 residues, remain superior to alternative encodings for predicting T cell responses.

    Area of Science:

    • Immunoinformatics
    • Computational biology
    • Cancer immunology

    Background:

    • Personalized cancer vaccines rely on predicting neoantigens that trigger T cell responses.
    • Current methods often use MHC class I pseudosequences, but their optimal definition and encoding are unclear.

    Purpose of the Study:

    • To systematically evaluate different Major Histocompatibility Complex (MHC) class I representations for neoantigen prediction.
    • To compare pseudosequence strategies, including structure-based, evolutionary, random, and varying lengths, alongside embeddings from protein language models and graph annotations.

    Main Methods:

    • Utilized the BigMHC EL framework to assess various MHC allele representations.
    • Compared performance of biologically informed pseudosequences (structure, evolutionary diversity) against random baselines and varying lengths.

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  • Evaluated embeddings from ESM-2 protein language model and graph-based annotations.

    • Main Results:

    • Biologically informed pseudosequences significantly outperformed random baselines.
    • Pseudosequences of 30-35 residues yielded optimal predictive performance.
    • Structure and evolutionary diversity pseudosequences performed similarly, suggesting overlapping residue importance.
    • ESM-2 and annotation embeddings showed improvement over random but not over curated pseudosequences.

    Conclusions:

    • Curated pseudosequences are currently the most effective MHC representations for neoantigen prediction models.
    • While alternative encodings show promise, they do not yet replace the predictive power of residue-level sequence information.