Prediction of multiple conformational states by combining sequence clustering with AlphaFold2

Abstract

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.

Protein structure prediction has reached the single-structure frontier

“…Predictions of fold-switchers have moderately reduced pLDDT versus single-fold proteins, but the pLDDTs are still substantially higher than for disordered proteins or disordered regions of specific proteins. Even more strikingly, Wayment-Steele and colleagues have shown that by clustering the sequences used by AlphaFold2, the algorithm can actually predict both folds in specific fold-switching systems 20 . This exciting result suggests that predictions of structural distributions may not be far off, as AlphaFold2 can already produce multiple correct structural outputs for the same protein.…”

OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization

“…The model has been made available to the public with DeepMind’s official open-source implementation, which has been used to predict the structures of hundreds of millions of proteins (Tunyasuvunakool et al 2021, Varadi et al 2021, Callaway 2022). This implementation has enabled researchers to optimize AlphaFold2’s prediction procedure and user experience (Mirdita, Schütze, et al 2022) and to employ it as a module within novel algorithms, including ones for protein complex prediction (Baek 2021), peptide-protein interactions (Tsaban et al 2022), structure ranking (Roney and Ovchinnikov 2022), and more ( e.g ., Baltzis et al 2022, Bryant et al 2022, Wayment-Steele et al 2022).…”

Conformational epistasis impairs AlphaFold structural predictions

“…Recently different works showed results of increased capacity of AF2 to reproduce protein conformational diversity using alignment subsampling (Del Alamo et al, 2022; Wayment-Steele et al, 2022). In an attempt to increase the information of evolutionary trajectories in the input used by AF2, we used ancestral reconstruction prediction (Joy et al, 2016).…”