EpiTune: An Accurate Epitope Prediction Model with Mechanistic Insights

Linear antibody epitope prediction using AlphaFold2

A calculation pipeline based on the localColabFold implementation of AlphaFold2 that can predict linear antibody epitopes by predicting the structure of the complex between antibody heavy and light chains and target peptide sequences derived from antigens is developed.

Comprehensive Evaluation and Explainable Interpretation of Peptide-HLA Binding Prediction Tools.

The results reveal substantial performance disparities, with self-attention-based models, including STMHCpan and BigMHC, exhibiting superior accuracy, and the capsule network model CapsNet-MHC_AN demonstrated robust performance.

Benchmarking antibody-antigen co-folding on human monomeric antigens

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.

EpiBench: Can LLMs Understand Epitopes for Antibody Drug Discovery?

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...

Limitations of general protein language models and public TCRpMHC data in training specificity prediction models

Benchmarking antibody modeling tools across structure prediction, docking, and paratope–epitope interface analysis

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.