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Workshop: Machine Learning in Structural Biology Workshop

Predicting conformational landscapes of known and putative fold-switching proteins using AlphaFold2

Hannah Wayment-Steele · Sergey Ovchinnikov · Lucy Colwell · Dorothee Kern


Proteins that switch their secondary structures upon response to a stimulus -- commonly known as "metamorphic proteins" -- directly question the paradigm of “one structure per protein”. Despite the potential to more deeply understand protein folding and function through studying metamorphic proteins, their discovery has been largely by chance, with fewer than 10 experimentally validated. AlphaFold2 (AF2) has dramatically increased accuracy in predicting single structures, though it fails to return alternate states for known metamorphic proteins in its default settings. We demonstrate that clustering an input multiple sequence alignment (MSA) by sequence similarity enables AF2 to sample alternate states of known metamorphs. Moreover, AF2 scores these alternate states with high confidence. We used our clustering method, AF-cluster, to screen for alternate states in protein families without known fold-switching, and identified a putative alternate state for the oxidoreductase DsbE. Similarly to KaiB, DsbE is predicted to switch between a thioredoxin-like fold and a novel fold. This prediction is the subject of ongoing experimental testing. Further development of such bioinformatic methods in tandem with experiment will likely aid in accelerating discovery and gaining a more systematic understanding of fold-switching in protein families.

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