Evolutionary selection of proteins with two folds
Open accessPreprint — published as 10.1038/s41467-023-41237-2
Abstract
Although most globular proteins fold into a single stable structure 1 , an increasing number have been shown to remodel their secondary and tertiary structures in response to cellular stimuli 2 . State-of-the-art algorithms 3-5 predict that these fold-switching proteins assume only one stable structure 6,7 , missing their functionally critical alternative folds. Why these algorithms predict a single fold is unclear, but all of them infer protein structure from coevolved amino acid pairs. Here, we hypothesize that coevolutionary signatures are being missed. Suspecting that over-represented single-fold sequences may be masking these signatures, we developed an approach to search both highly diverse protein superfamilies–composed of single-fold and fold-switching variants–and protein subfamilies with more fold-switching variants. This approach successfully revealed coevolution of amino acid pairs uniquely corresponding to both conformations of 56/58 fold-switching proteins from distinct families. Then, using a set of coevolved amino acid pairs predicted by our approach, we successfully biased AlphaFold2 5 to predict two experimentally consistent conformations of a candidate protein with unsolved structure. The discovery of widespread dual-fold coevolution indicates that fold-switching sequences have been preserved by natural selection, implying that their functionalities provide evolutionary advantage and paving the way for predictions of diverse protein structures from single sequences.
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Cites
Highly accurate protein structure prediction with AlphaFold
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AlphaFold and Implications for Intrinsically Disordered Proteins
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Sampling alternative conformational states of transporters and receptors with AlphaFold2
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Flagged first, then by citations
- Authors
- Schafer, J. W., & Porter, L. L.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Porter, Lauren L.
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2023-01-20
- PMC
- PMC9928049
- OpenAlex
- W4317603817
- Cited by
- 2
- References
- 9
- Citations by year
- 2024: 1 · 2023: 1
- Type
- posted content
- Language
- en
- Open access
- green
- dx.doi.org
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institutes of Health
- U.S. National Library of Medicine
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein structure prediction, AlphaFold2, protein superfamilies, protein subfamilies, protein conformational diversity, natural selection
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