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Brian G. Pierce

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Open access Jul 2026

Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition

It is shown that increased sampling and AlphaFold3 generally improve performance relative to default sampling and AlphaFold2, however predictive accuracy and improvement levels varied considerably among interface classes, with antibody-peptide complexes representing a challenge despite their small antigen size.

Rui Yin, S. Saravanakumar, S. Shi et al. · 1 citation
Open access Aug 2026

Benchmarking Deep Learning Predictions of Mutation-Induced Fold Switching

A systematic NMR-characterized dataset of mutants of the GA/GB model fold-switching system is presented and it is found that this benchmark revealed variable and position-dependent performance across methods, with certain AlphaFold2-based algorithms able to predict mutant effects at individual sites, indicating some understanding of physical effects of residue substitutions.

Nathaniel R. Felbinger, K. Carillo, Yihong Chen et al. · 0 citations