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Evolutionary selection of proteins with two folds

Joseph W. Schafer, Lauren L. Porter

2023Nature Communications46 citationsDOIOpen Access PDF

Abstract

Although most globular proteins fold into a single stable structure, an increasing number have been shown to remodel their secondary and tertiary structures in response to cellular stimuli. State-of-the-art algorithms predict that these fold-switching proteins adopt only one stable structure, 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 single-fold variants could be masking these signatures, we developed an approach, called Alternative Contact Enhancement (ACE), to search both highly diverse protein superfamilies-composed of single-fold and fold-switching variants-and protein subfamilies with more fold-switching variants. ACE successfully revealed coevolution of amino acid pairs uniquely corresponding to both conformations of 56/56 fold-switching proteins from distinct families. Then, we used ACE-derived contacts to (1) predict two experimentally consistent conformations of a candidate protein with unsolved structure and (2) develop a blind prediction pipeline for fold-switching proteins. 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.

Topics & Concepts

Fold (higher-order function)CoevolutionComputational biologyProtein foldingProtein structureGlobular proteinBiologyProtein structure predictionProtein engineeringPhylogenetic treeEvolutionary biologyGeneticsComputer scienceGeneCell biologyBiochemistryEnzymeProgramming languageProtein Structure and DynamicsRNA and protein synthesis mechanismsMachine Learning in Bioinformatics