TL;DR
EpiTune is presented, a b-cell epitope prediction model that fine-tunes the underlying protein language model to deliver best-in-class predictions of linear epitopes and competitive predictions for confirmational epitopes.
Defining the binding epitopes of antibodies is essential for understanding how they bind to their antigens and perform their molecular functions. However, while determining linear epitopes of monoclonal antibodies can be accomplished utilizing well-established empirical procedures, these approaches are generally labor-...
Accurate prediction of peptide binding to human leukocyte antigen class I (HLA-I) molecules is critical for advancing immunological research, particularly in vaccine design and immunotherapy. However, limitations in model performance, interpretability, and dataset quality impede the widespread adoption of existing pred...
The most recent methods substantially outperformed earlier ones, producing medium-or-better top-ranked models for approximately half of post-cutoff Fv complexes without templates or experimental restraints, and performing similarly on antigens with or without a close pre-cutoff homolog.
The results show that current LLMs capture partial epitope-related signals but remain limited in antibody-specific sequence grounding, long-context residue localization, and biologically grounded reasoning, so EpiBench provides a diagnostic testbed for measuring and improving sequence-aware biomedical LLMs toward relia...
This work evaluated ImmuneBuilder, IgFold, AlphaFold3, GRAMM, and dyMEAN on 50 non-redundant humanized antibody–antigen complexes using multiple retained predictions and paired statistical testing, finding all three antibody structure predictors were accurate.
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