Training bias and sequence alignments shape protein-peptide docking by AlphaFold and related methods.

ABSTRACT: Protein-peptide interactions mediate many biological processes. Accurate structural models of protein-peptide complexes, determined by experiment or computational prediction, are essential for understanding function and designing interaction inhibitors. AlphaFold2-Multimer (AF2-Multimer), AlphaFold3 (AF3), and related models such as Boltz-1 and Chai-1 can predict protein-peptide binding geometry, often with high accuracy. Using a dataset of experimentally resolved structures, we analyzed the performance of these four structure prediction models to understand how they work. We found evidence of bias for previously seen structures, indicating that models struggle to generalize to novel proteins or binding sites. We tested how models use the protein and peptide multiple sequence alignments (M

SUBMITTER: Guan L 

PROVIDER: S-EPMC12518507 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature