Predicting multiple conformations via sequence clustering and AlphaFold2

By Hannah K. Wayment-Steele, Adedolapo M. Ojoawo, Renee Otten, Julia M Apitz, W. Pitsawong, Marc Hoemberger, Sergey Ovchinnikov, Lucy J. Colwell, Dorothee Kern. 2023

466 citations recorded by the source corpus.

AlphaFold2 (ref. 1) has revolutionized structural biology by accurately predicting single structures of proteins. However, a protein’s biological function often depends on multiple conformational substates2, and disease-causing point mutations often cause population changes within these substates3,4. We demonstrate that clustering a multiple-sequence alignment by sequence similarity enables AlphaFold2 to sample alternative states of known metamorphic proteins with high confidence. Using this method, named AF-Cluster, we investigated the evolutionary distribution of predicted structures for the metamorphic protein KaiB5 and found that predictions of both conformations were distributed in clusters across the KaiB family. We used nuclear magnetic resonance spectroscopy to confirm an AF-Cluster prediction: a cyanobacteria KaiB variant is stabilized in the opposite state compared with the more widely studied variant. To test AF-Cluster’s sensitivity to point mutations, we designed and experimentally verified a set of three mutations predicted to flip KaiB from Rhodobacter sphaeroides from the ground to the fold-switched state. Finally, screening for alternative states in protein families without known fold switching identified a putative alternative state for the oxidoreductase Mpt53 in Mycobacterium tuberculosis. Further development of such bioinformatic methods in tandem with experiments will probably have a considerable impact on predicting protein energy landscapes, essential for illuminating biological function. An analysis of the evolutionary distribution of predicted structures for the metamorphic protein KaiB using AF-Cluster reveals that both conformations of KaiB were distributed in clusters across the KaiB family.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Pushmeet Kohli
2. Alexander Pritzel
3. Eirini Arvaniti
4. Michael Figurnov
5. Ellen D. Zhong

In Computational Biology & Bioinformatics

1. Pushmeet Kohli
2. Kathryn Tunyasuvunakool
3. Eirini Arvaniti
4. Ellen D. Zhong

Citations in the corpus

• Accurate structure prediction of biomolecular interactions with AlphaFold 3 (2024, methodology): “Many methods have been developed, particularly around MSA resampling, that assist in generating diversity from previous AlphaFold models 43–45 and may also assist in multistate prediction with AF3.”
• Scalable emulation of protein equilibrium ensembles with generative deep learning (2025, unknown): “We chose AFCluster [50] and AlphaFlow [49] as baseline methods for multiple conformation generation.”