Benchmarking AI Protein Structure Predictors Reveals a Persistent Bias in Multi-State Proteins

Authors

Ye, M., Wang, Y.-H., Brogi, M., Parks, J. M., Kuo, K. M., Gumbart, J. C.

Abstract

Protein structure predictors achieve high single-state accuracy, but it remains unclear whether they can recover functionally relevant conformational ensembles or account for the presence of ligands and/or binding partners. Here, we benchmark AlphaFold3, Boltz-2, Chai-1, and BioEmu on four canonical multi-state proteins (Pf-MATE, LAO, SecA, and {beta}2AR), quantifying state bias and sampling breadth against experimental reference structures. Models frequently default to a dominant state represented in the PDB; small-molecule ligands have weak or inconsistent effects, while large protein partners drive clear conformational switching between states. Multiple sequence alignment (MSA)-based approaches (AF-Cluster and random subsampling) recapitulate similar biases, indicating that this behavior is not unique to newer architectures. These results underscore current limitations for multi-state protein structure prediction and structure-guided ligand discovery.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 13 Jul 2026.

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