PreprintCited by 2Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

Evolutionary selection of proteins with two folds

Open access
Schafer, Joseph W., Porter, Lauren L.·2023·bioRxiv (Cold Spring Harbor Laboratory)

Preprint — 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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Crossref · 10.1101/2023.01.18.524637

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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
OpenAlex
W4317603817
Cited by
2
References
9
Citations by year
2024: 1 · 2023: 1
Type
posted content
Language
en
Open access
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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