Prediction of multiple conformational states by combining sequence clustering with AlphaFold2
By Hannah K. Wayment-Steele, Sergey Ovchinnikov, Lucy J. Colwell, Dorothee Kern. 2022
58 citations recorded by the source corpus.
AlphaFold2 (AF2) has revolutionized structural biology by accurately predicting single structures of proteins and protein-protein complexes. However, biological function is rooted in a protein’s ability to sample different conformational substates, and disease-causing point mutations are often due to population changes of these substates. This has sparked immense interest in expanding AF2’s capability to predict conformational substates. We demonstrate that clustering an input multiple sequence alignment (MSA) by sequence similarity enables AF2 to sample alternate states of known metamorphic proteins, including the circadian rhythm protein KaiB, the transcription factor RfaH, and the spindle checkpoint protein Mad2, and score these states with high confidence. Moreover, we use AF2 to identify a minimal set of two point mutations predicted to switch KaiB between its two states. Finally, 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 future experimental testing. Further development of such bioinformatic methods in tandem with experiments will likely have profound impact on predicting protein energy landscapes, essential for shedding light into biological function.
Subfields: Molecular Biology
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In Molecular Biology
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Citations in the corpus
- AFsample: improving multimer prediction with AlphaFold using massive sampling (2023, background): “Another option is to randomly perturb or alter the input multiple sequence alignment (MSA), which has been shown to enable better sampling of the conformational landscape and prediction of multiple conformational states (Wayment-Steele et al. 2022).”
- OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization (2023, unknown): “16–18).”
- Biasing AlphaFold2 to predict GPCRs and kinases with user-defined functional or structural properties (2022, methodology): “To date, a number of AF2 customizations that adopted different concepts are available (Del Alamo et al., 2022; Heo and Feig, 2022; Stein and Mchaourab, 2022; Wayment-Steele et al., 2022).”
- Improved multimer prediction using massive sampling with AlphaFold in CASP15 (2023, background): “11 Alternatively, enabling the dropout layers in the neural network can also enhance diversity among generated models.”