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Intrinsic protein disorder and conditional folding in AlphaFoldDB
Damiano Piovesan1, Alexander Miguel Monzon1,2, Silvio C E Tosatto1
1Department of Biomedical Sciences, University of Padova, Padova, Italy.
Protein Science : a Publication of the Protein Society
|October 10, 2022
Summary
Intrinsically disordered regions (IDRs) are challenging to study. AlphaFoldDB structure predictions surprisingly show high accuracy for predicting IDRs and conditionally folded regions, highlighting disorder-structure plasticity.
Area of Science:
- Protein bioinformatics
- Computational structural biology
Background:
- Intrinsically disordered regions (IDRs) challenge traditional protein structure-function relationships.
- Analyzing IDRs is difficult due to their lack of stable structures.
- Large-scale accurate protein structure predictions are now available via AlphaFoldDB.
Purpose of the Study:
- To evaluate AlphaFoldDB's capability in predicting intrinsically disordered regions (IDRs).
- To establish baseline performance metrics for IDR prediction using AlphaFoldDB models.
- To assess AlphaFoldDB's performance on conditionally folded binding regions.
Main Methods:
- Utilized AlphaFoldDB predicted structures.
- Employed the recent CAID dataset for evaluation.
- Established three distinct baselines for IDR prediction.
Main Results:
- AlphaFoldDB models demonstrate high competitiveness in predicting IDRs.
- AlphaFoldDB also shows strong performance in predicting conditionally folded binding regions.
- The results underscore the plasticity of the disorder-to-structure continuum.
Conclusions:
- AlphaFoldDB provides a valuable resource for the prediction of intrinsically disordered regions.
- The study validates the utility of large-scale structure prediction databases for analyzing protein disorder.
- AlphaFoldDB's accuracy highlights the dynamic nature of protein structures and their functions.
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