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Assessing the validity of leucine zipper constructs predicted by AlphaFold
Isobel Mitic1,2, Keiran Rowell3, Thomas Litfin3
1Department of Molecular Medicine, School of Biomedical Sciences, University of New South Wales, Sydney, New South Wales, Australia.
Protein Science : a Publication of the Protein Society
|December 23, 2025
Summary
Artificial intelligence (AI) accurately predicts protein structures, including leucine zipper dimers, but may overestimate confidence for improbable interactions. This highlights AI
Area of Science:
- Structural biology
- Computational biology
- Genetics
Background:
- AP-1 transcription factors regulate cellular pathways through dimerization via leucine zipper domains.
- Dimerization specificity is determined by transcription factor affinity and concentration, controlling gene expression.
- AI-driven protein structure prediction offers new avenues for studying protein interactions.
Purpose of the Study:
- To investigate AlphaFold's ability to model leucine zipper domains and predict dimer interfaces.
- To assess AlphaFold's accuracy in differentiating between probable and improbable protein-protein interactions.
- To evaluate the capabilities and limitations of AI in structural biology.
Main Methods:
- Utilized AlphaFold2 and AlphaFold3 for AI-based protein structure prediction.
- Tested AlphaFold's performance on the Fos-Jun AP-1 dimer model.
- Analyzed over 2000 experimentally validated human leucine zipper sequences.
Main Results:
- AlphaFold successfully predicted highly confident leucine zipper dimers.
- The AI model predicted the formation of the FosB homodimer, despite known electrostatic repulsion in vivo.
- Demonstrated AlphaFold's potential for high-confidence but potentially inaccurate structure predictions.
Conclusions:
- AlphaFold can model leucine zipper domains with high confidence.
- AI predictions may not always align with experimental biological constraints, such as electrostatics.
- Understanding AI limitations is crucial for accurate structural biology applications.
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