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A sequence-based method for predicting extant fold switchers that undergo α-helix ↔ β-strand transitions
Soumya Mishra1,2, Loren L Looger2, Lauren L Porter1,3
1National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.
Biopolymers
|September 9, 2021
Summary
We developed a new sequence-based method to identify fold-switching proteins, which change structure and function. This approach uses secondary structure prediction discrepancies to find these important, yet understudied, shapeshifting proteins.
Area of Science:
- Protein biochemistry
- Structural biology
- Bioinformatics
Background:
- Extant fold-switching proteins dynamically alter their secondary structures and functions in response to cellular signals, playing crucial roles in biological regulation and human health.
- Despite their significance, these proteins are understudied, necessitating efficient discovery and characterization methods.
- Current predictive methods often rely on solved structures or computationally intensive simulations, limiting their broad applicability.
Purpose of the Study:
- To develop a high-throughput, sequence-based computational method for predicting extant fold-switching proteins.
- To identify proteins that transition between alpha-helix and beta-strand secondary structures.
- To overcome the limitations of existing structure-dependent prediction techniques.
Main Methods:
- Utilized JPred4 to predict secondary structure discrepancies (alpha-helix vs. beta-strand) in protein sequences.
- Leveraged the observation that fold-switching regions (FSRs) exhibit different secondary structure propensities in isolation versus within the full protein context.
- Developed a classifier combining predicted secondary structure discrepancies and overall secondary structure content to identify fold switchers.
Main Results:
- A strong correlation was observed between predicted and experimentally validated alpha-helix/beta-strand discrepancies in known fold-switching proteins.
- Single-fold proteins (non-switchers) showed significantly fewer predicted discrepancies, confirming the method's specificity.
- The developed classifier achieved a Matthews correlation coefficient of 0.71, with a low false-positive rate (2/136) but a notable false-negative rate (7/17).
Conclusions:
- The sequence-based method effectively predicts a subset of extant fold-switching proteins with high precision.
- This approach offers a scalable tool for discovering novel fold-switching proteins from large genomic datasets.
- Further refinement may improve the detection of fold-switching proteins with higher sensitivity.
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