Repetitive proteins that undergo large conformational changes evade structural prediction algorithms
Marina P Chang1, Tianyi Jin2,3, Alana P Gudinas4
1Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, USA.
The Journal of Chemical Physics
|December 11, 2025
Summary
Protein structure prediction tools like AlphaFold struggle with dynamic proteins. New RTX protein variants show diverse structures, highlighting the need for better predictive models validated by experiments.
Area of Science:
- Structural biology
- Computational biology
- Biochemistry
Background:
- Protein structure prediction algorithms like AlphaFold have advanced protein design.
- These tools face limitations with conformationally dynamic, intrinsically disordered, and stimuli-responsive proteins.
Purpose of the Study:
- To evaluate sequence-to-structure predictions for challenging intrinsically disordered proteins.
- To explore the structural behavior of repeats-in-toxin (RTX) protein variants.
Main Methods:
- Designed RTX sequence variants with modified repeats.
- Utilized AlphaFold2 and AlphaFold3 for initial structure prediction.
- Employed molecular dynamics simulations, circular dichroism spectroscopy, small-angle X-ray scattering, and X-ray crystallography for experimental validation.
Main Results:
- AlphaFold predicted β-roll structures for all RTX variants.
- Experimental methods revealed diverse, sequence-dependent structures for RTX variants in different calcium conditions.
- Predicted structures did not fully capture the experimental conformational dynamics.
Conclusions:
- Current protein structure prediction tools need improvement for intrinsically disordered proteins.
- Multi-modal, multi-scale experimental validation is crucial for accurate protein design.
- Understanding sequence-structure relationships in dynamic proteins is key for biotechnology and sustainability.
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