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Published on: July 14, 2015
Benchmarking AI Protein Structure Predictors Reveals a Persistent Bias in Multi-State Proteins
Muhui Ye1, Yu-Hong Wang2, Maximilian Brogi1
1School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Biorxiv : the Preprint Server for Biology
|July 17, 2026
Summary
Current protein structure prediction models often default to a single dominant state, struggling with conformational ensembles. Ligands have minimal impact, while protein partners can induce state changes, limiting applications in drug discovery.
Area of Science:
- Computational Biology
- Structural Biology
- Biophysics
Background:
- Protein structure prediction tools excel at single-state accuracy.
- Their ability to model dynamic conformational ensembles and the influence of ligands or binding partners remains largely unassessed.
- Understanding these capabilities is crucial for advancing structure-based drug discovery and understanding protein function.
Purpose of the Study:
- To benchmark leading protein structure prediction models (AlphaFold3, Boltz-2, Chai-1, BioEmu) on multi-state proteins.
- To quantify state bias and sampling breadth against experimental data.
- To evaluate the impact of ligands and protein partners on conformational predictions.
Main Methods:
- Benchmarking of AlphaFold3, Boltz-2, Chai-1, and BioEmu.
- Use of four canonical multi-state proteins (PfMATE, LAO, SecA, β2AR).
- Quantification of state bias and sampling breadth against experimental reference structures.
Main Results:
- Models exhibit a strong bias towards the dominant state found in the Protein Data Bank (PDB).
- Small-molecule ligands show weak or inconsistent effects on predicted conformations.
- Large protein partners effectively drive conformational switching between states.
- Multiple sequence alignment (MSA)-based methods also display similar biases, indicating a general limitation.
Conclusions:
- Current protein structure prediction models have limitations in accurately capturing functionally relevant conformational ensembles.
- The influence of protein binding partners is more significant than small-molecule ligands in driving conformational changes.
- These findings highlight challenges for structure-guided ligand discovery and understanding protein dynamics.
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