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Predicting multiple conformations via sequence clustering and AlphaFold2
Open accessAbstract
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 substates 2 , and disease-causing point mutations often cause population changes within these substates 3,4 . 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 KaiB 5 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.
Overview
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Written from the paper’s full text.
TL;DR
AF-Cluster clusters a protein multiple-sequence alignment by sequence similarity and feeds each cluster to AlphaFold2, enabling sampling of alternative conformational states for metamorphic proteins. The authors experimentally validate predicted KaiB states and designed point mutations, and identify a putative alternative state for Mpt53.
Key findings
- AF-Cluster predicted both ground and fold-switched states for KaiB, RfaH and MAD2; for RfaH, top-five-model plDDT was 84.2 for the active state and 73.9 for the autoinhibited state, versus 68.6 for the autoinhibited state from the complete MSA.
- NMR confirmed the AF-Cluster prediction for KaiBTV-4: CS-Rosetta models were within 1.8 ± 0.3 Å r.m.s.d. to the AF-Cluster model, and the NMR structure matched with 1.89 ± 0.13 Å r.m.s.d. and average pairwise r.m.s.d. of 0.7 Å over backbone atoms.
- Three mutations (I68R, V83D, N84R) switched KaiBRS from the ground to the fold-switched state; NMR showed the mutant occupied the FS state at 86% versus 11% in wild type, and the ground state at 14% versus 89% in wild type.
- Screening 628 protein families identified a putative alternative state for M. tuberculosis Mpt53; in the GA/GB system, AF-Cluster correctly predicted the most stable fold for 10 out of 12 point-mutation sets, whereas default AF2 predicted 8 out of 12.
Methods
- MSAs were generated with ColabFold/MMseqs2, sequences with >25% gaps removed, then clustered by edit distance using DBSCAN; each cluster was used as input to AlphaFold2, with MSA Transformer used to probe evolutionary couplings.
- Experimental validation used NMR spectroscopy, CS-Rosetta, NOESY-based structure calculation, SEC–MALS, and a curated KaiB phylogenetic tree of 487 variants.
- The fold-switch screen used 628 proteins of 48–150 residues with >1,000 MSA sequences from a database of MSAs associated with crystal structures; Mpt53 homologues were analyzed with DALI and phylogenetic analysis.
Limitations
- The paper states that pLDDT cannot be used as a measure of free energy; the thermodynamically disfavoured KaiB FS state still had higher pLDDT than the ground state, and the number of models per state reflects construct abundance rather than Boltzmann weight.
- The paper states that AF-Cluster could not predict the oligomeric states of selecase, lymphotactin or CLIC1, and that conformational substates absent from the evolutionary signal may require alternative methods.
- The paper notes that novel computationally identified folds may be difficult to validate experimentally if the triggering stimulus is unknown; the Mpt53 alternative state is a prediction without experimental validation in this work.
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60 Own result · 32 Not checked yet · 10 Own interpretation · 3 Own method · 3 No basis given
In the paper
“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.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Abstract
What the sources say
- Not checked yetcites [5] Chang, Y. G. et al. Circadian rhythms. A protein fold switch joins the circadian oscillator to clock output in cyanobacteria. Science 349, 324–328 (2015). source
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In the paper
“We demonstrate that clustering a multiple-sequence alignment by sequence similarity enables AlphaFold2 to sample alternative states of known metamorphic proteins with high confidence.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Abstract
boosteddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“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.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Abstract
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In the paper
“Finally, screening for alternative states in protein families without known fold switching identified a putative alternative state for the oxidoreductase Mpt53 in Mycobacterium tuberculosis.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Abstract
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In the paper
“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.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Abstract
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In the paper
“By contrast, AF-Cluster predicted both the autoinhibited and the active state (Fig. 4b).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Testing AF-Cluster on other proteins
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In the paper
“We found that AF-Cluster again had the ability to predict models for both of MAD2’s conformational states (Fig. 4d).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Testing AF-Cluster on other proteins
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In the paper
“As a control, AF-Cluster models of ubiquitin, a protein that is well characterized to have no alternative states, returns only models with high confidence and low r.m.s.d. to the crystal structure PDB 1UBQ.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > AF-Cluster predicts novel states
What the sources say
- Not checked yetcites 1UBQ
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In the paper
“For most of the protein families screened, an increase in r.m.s.d. corresponded to a decrease in plDDT (Fig. 5b).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > AF-Cluster predicts novel states
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In the paper
“We confirmed that, for variants in the tree that had been experimentally characterized, the prediction from AF-Cluster corresponded to the structure expected to be thermodynamically favoured (Fig. 2b).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Experimental test of KaiB predictions
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In the paper
“Notably, the average plDDT for the top five models for each state (84.2 for the active state, 73.9 for the autoinhibited) was higher than the plDDT of the autoinhibited state by the complete MSA (plDDT of 68.6), suggesting that clustering resulted in deconvolving conflicting sets of couplings.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Testing AF-Cluster on other proteins
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In the paper
“We used the same set of clusters to make predictions using the unsupervised deep learning model MSA Transformer36 and found that these clusters contained evolutionary couplings for both states, and the score based on contact maps correlated with the root mean squared deviation (r.m.s.d.) in AF2 (Methods and Extended Data Fig. 3).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > AF-Cluster predicts both KaiB states
What the sources say
- Not checked yetcites [36] Rao, R. M. et al. MSA Transformer. In Proc. International Conference on Machine Learning 8844–8856 (PMLR, 2021). source
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In the paper
“Notably, we found that the AF2 predictions from our MSA clusters comprised a distribution of structures, with the highest-scored regions of the distribution corresponding to the ground and FS state.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > AF-Cluster predicts both KaiB states
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In the paper
“Two prominent states are observed that correspond to the largest-sized MSA clusters (Extended Data Fig. 7a), and both of which have plDDT values that are statistically significantly higher than the rest of the set (Extended Data Fig. 7b).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > AF-Cluster predicts novel states
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In the paper
“For example, clusters predicting the FS state were enriched for arginine at position 68, whereas clusters predicting the ground state at position 68 were enriched for leucine, a switch between a charged and a hydrophobic residue.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Mutations to flip the KaiB equilibrium
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In the paper
“We found that, for uniformly subsampled MSAs of size 10, 1 out of 500 samples was within 3 Å of the ground state, with lower confidence than the MSA cluster samples (Extended Data Fig. 2e). Uniformly subsampled MSAs of size 100 did not sample the ground state at all.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > AF-Cluster predicts both KaiB states
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In the paper
“For example, variants from R. sphaeroides32, T. elongatus5 and Synechococcus elongatus31 all were predicted in the ground state, confirming their characterized circadian-rhythm function.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Experimental test of KaiB predictions
What the sources say
- Not checked yetcites [32] Pitsawong, W. et al. From primordial clocks to circadian oscillators. Nature 616, 183–189 (2023). source
- Not checked yetcites [5] Chang, Y. G. et al. Circadian rhythms. A protein fold switch joins the circadian oscillator to clock output in cyanobacteria. Science 349, 324–328 (2015). source
- Not checked yetcites [31] Ishiura, M. et al. Expression of a gene cluster kaiABC as a circadian feedback process in cyanobacteria. Science 281, 1519–1523 (1998). source
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In the paper
“Comparison of the average peak intensity ratios of the assignable minor (ground state) peaks to those of the major state (FS) peaks show that the mutant occupies the FS state with a population of 86% (versus 11% in the WT), and the ground state with a population of 14% (versus 89% in the WT) (Fig. 3f).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Mutations to flip the KaiB equilibrium
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In the paper
“One state corresponds to the known thioredoxin-like conformation of Mpt53 (ref. 42), whereas the other state corresponds to a conformation with a different secondary structure layout (Fig. 5d,e).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > AF-Cluster predicts novel states
What the sources say
- Not checked yetcites [42] Goulding, C. W. et al. Gram-positive DsbE proteins function differently from Gram-negative DsbE homologs. A structure to function analysis of DsbE from Mycobacterium tuberculosis. J. Biol. Chem. 279, 3516–3524 (2004). source
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In the paper
“The closest structure that we found (PDB: 3EMX) adopted a similar secondary structure to the Mpt53 alternative structure. This structure is of an unspecified thioredoxin from the archaea Aeropyrum pernix with no associated publication.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > AF-Cluster predicts novel states
What the sources say
- Not checked yetcites 3EMX
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In the paper
“Notably, predicting the 3D structure of KaiB using just the 50 MSA sequences that are closest by number of mutations (hereafter, edit distance) to KaiBTE resulted in a prediction of the ground state (Fig. 1c (right)); however, predicting the 3D structure of KaiBTE using the closest 100 sequences returned to predicting the FS state.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > AF-Cluster predicts both KaiB states
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Notably, we noticed that KaiB-4 variants were evolutionarily close to the better-studied KaiB-1 variants involved in the circadian clock, yet the KaiB-4 variants were predicted by AF-Cluster to primarily occupy the FS state (Fig. 2c).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Experimental test of KaiB predictions
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In the paper
“In the second state, strand β1 replaces β5 within the β-sheet. The α-helix α4 is displaced to the opposite side of the β-sheet, and α5 is rotated.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > AF-Cluster predicts novel states
plainSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“Importantly, we show that, using our method, AF-Cluster, both states are sampled and scored with high confidence by AF2’s learned predicted local distance difference test (plDDT) measure.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Main
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In the paper
“The 1H-15N heteronuclear single quantum coherence (HSQC) spectra in both the wild-type (WT) and KaiBRS-3m indicate the presence of major and minor state peaks, with the populations appearing to be flipped (Fig. 3d).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Mutations to flip the KaiB equilibrium
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In the paper
“KaiBTV-4 was found to be stably folded at 35 °C and, after backbone assignments, we found peak duplication for many peaks corresponding to a major stable and minor unfolded state (Extended Data Fig. 4).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Experimental test of KaiB predictions
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In the paper
“For epsilon > epsmax, more sequences are clustered, so the number of clusters is decreasing because clusters are merged.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > Clustering
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
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In the paper
“A total of 479 of these were hits for both the known and alternative state, with 368 exclusively for the known and 208 exclusively for the alternative state.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > Screening for Mpt53 structure homologues
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In the paper
“The resulting tree is shown in Extended Data Fig. 9, and demonstrates that, while the closest structure homologues to the known state are clustered, the closest homologues to the alternative state are dispersed across the tree.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > Screening for Mpt53 structure homologues
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In the paper
“We found that clusters from AF-Cluster scored higher to the ground state (Extended Data Fig. 3b)”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > Investigating evolutionary couplings from clustering using MSA Transformer
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In the paper
“Seven out of the depicted nine proteins had a helix in an analogous spot to the α-4 helix location in the Mpt53 alternative structure.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > Screening for Mpt53 structure homologues
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found that the highest-pLDDT model from AF-Cluster correctly predicted the most stable folds for 10 out of 12, whereas default AF2 correctly predicted 8 out of 12.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Discussion
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In the paper
“For both states, we found that the AUC scores correlated with the r.m.s.d. to the FS state from AF2 (ground state: Spearman R = −0.32, P = 2 × 10−9; FS state: Spearman R = −0.34, P = 4 × 10−10)”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > Investigating evolutionary couplings from clustering using MSA Transformer
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In the paper
“We took the union of these two sets and applied CD-HIT65 with default parameters to filter for highly similar sequences. This resulted in 1,055 sequences remaining.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > Screening for Mpt53 structure homologues
What the sources say
- Not checked yetcites [65] Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012). source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“A few structures had higher weighted r.m.s.d. for the alternative Mpt53 state than for the known Mpt53 state (Extended Data Fig. 7e (orange points) and 7f (structures)).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > Screening for Mpt53 structure homologues
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In the paper
“This resulted in 1,822 matches for the Mpt53 known state and 1,245 matches for the Mpt53 alternative state (Extended Data Fig. 7d).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > Screening for Mpt53 structure homologues
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We identified a KaiB variant in T. elongatus vestitus that is phylogenetically close to the known fold-switching KaiB for which the ground state is thermodynamically favoured, yet was predicted and experimentally corroborated to be stabilized in the FS state.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Discussion
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The premise of methods to infer structure based on evolutionary couplings is that, because amino acids exist and evolve in the context of 3D structure, they are not free to evolve independently, but instead co-evolve in patterns reflective of the underlying structure.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Main
What the sources say
- Not checked yetcites [7] Altschuh, D., Lesk, A. M., Bloomer, A. C. & Klug, A. Correlation of co-ordinated amino acid substitutions with function in viruses related to tobacco mosaic virus. J. Mol. Biol. 193, 693–707 (1987). source
- Not checked yetcites [8] Lockless, S. W. & Ranganathan, R. Evolutionarily conserved pathways of energetic connectivity in protein families. Science 286, 295–299 (1999). source
- Not checked yetcites [9] Marks, D. S. et al. Protein 3D structure computed from evolutionary sequence variation. PLoS ONE 6, e28766 (2011). source
- Not checked yetcites [10] Morcos, F. et al. Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proc. Natl Acad. Sci. USA 108, E1293–E1301 (2011). source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Understanding the mechanistic basis of any protein’s functions requires understanding the complete set of conformational substates that it can adopt2.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Main
What the sources say
- Not checked yetcites [2] Henzler-Wildman, K. & Kern, D. Dynamic personalities of proteins. Nature 450, 964–972 (2007). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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Probably not a substantial claim. It may be too vague to check against evidence.
None of the sources cited for this claim have been checked yet.
In the paper
“Indeed, methods for inferring evolutionary couplings have already demonstrated that contacts corresponding to multiple states can be observed at the level of entire MSAs for membrane proteins15, ligand-induced conformational changes16 and multimerization-induced conformational changes17.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Main
What the sources say
- Not checked yetcites [15] Hopf, T. A. et al. Three-dimensional structures of membrane proteins from genomic sequencing. Cell 149, 1607–1621 (2012). source
- Not checked yetcites [16] Morcos, F., Jana, B., Hwa, T. & Onuchic, J. N. Coevolutionary signals across protein lineages help capture multiple protein conformations. Proc. Natl Acad. Sci. USA 110, 20533–20538 (2013). source
- Not checked yetcites [17] Uguzzoni, G. et al. Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis. Proc. Natl Acad. Sci. USA 114, E2662–E2671 (2017). source
boostedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“For example, although the metamorphic protein KaiB contains only 108 residues, it undergoes a conformational change that affects the secondary structure of around 40 residues in its C-terminal part, switching between a canonical thioredoxin-like structure and a unique alternative conformation5”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Main
What the sources say
- Not checked yetcites [5] Chang, Y. G. et al. Circadian rhythms. A protein fold switch joins the circadian oscillator to clock output in cyanobacteria. Science 349, 324–328 (2015). source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“A computational analysis of the Protein Data Bank (PDB) that identified changes in secondary structure between protein models sharing the same sequence suggested that between 0.5% and 4% of all proteins are fold switching29.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Main
What the sources say
- Not checked yetcites [29] Porter, L. L. & Looger, L. L. Extant fold-switching proteins are widespread. Proc. Natl Acad. Sci. USA 115, 5968–5973 (2018). source
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Mpt53 is an extracellular single-domain enzyme that is suggested to ensure correct folding of several cell-wall and extracellular protein substrates in M. tuberculosis by catalysing disulfide oxidation42.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > AF-Cluster predicts novel states
What the sources say
- Not checked yetcites [42] Goulding, C. W. et al. Gram-positive DsbE proteins function differently from Gram-negative DsbE homologs. A structure to function analysis of DsbE from Mycobacterium tuberculosis. J. Biol. Chem. 279, 3516–3524 (2004). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Fewer than ten metamorphic protein families have been thoroughly experimentally characterized21, spanning a diverse range of functions.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Main
What the sources say
- Not checked yetcites [21] Dishman, A. F. & Volkman, B. F. Design and discovery of metamorphic proteins. Curr. Opin. Struct. Biol. 74, 102380 (2022). source
plainn = 10- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“In RfaH, the C-terminal domain (CTD) interconverts between an α-helix bundle and a β-barrel through binding to functional partners23.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Testing AF-Cluster on other proteins
What the sources say
- Not checked yetcites [23] Zuber, P. K., Schweimer, K., Rosch, P., Artsimovitch, I. & Knauer, S. H. Reversible fold-switching controls the functional cycle of the antitermination factor RfaH. Nat. Commun. 10, 702 (2019). source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our results demonstrate that, in the oncoming age of AF2-enabled structural biology, related sequences for any given protein target might contain a signal for more than one biologically relevant structure, and that deep-learning methods can be used to detect and analyse these multiple conformational states.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Main
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“KaiB is a circadian-rhythm protein found in cyanobacteria5,31 and proteobacteria32 that adopts two conformations with distinct secondary structures as part of its function”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > AF-Cluster predicts both KaiB states
What the sources say
- Not checked yetcites [5] Chang, Y. G. et al. Circadian rhythms. A protein fold switch joins the circadian oscillator to clock output in cyanobacteria. Science 349, 324–328 (2015). source
- Not checked yetcites [31] Ishiura, M. et al. Expression of a gene cluster kaiABC as a circadian feedback process in cyanobacteria. Science 281, 1519–1523 (1998). source
- Not checked yetcites [32] Pitsawong, W. et al. From primordial clocks to circadian oscillators. Nature 616, 183–189 (2023). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The thermodynamically favoured state for KaiB from T. elongatus (KaiBTE) is the ground state; the FS structure was first solved in a complex with KaiC33, and could be solved for the isolated KaiB only by introducing stabilizing mutations to this variant33.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > AF-Cluster predicts both KaiB states
What the sources say
- Not checked yetcites [33] Tseng, R. et al. Structural basis of the day-night transition in a bacterial circadian clock. Science 355, 1174–1180 (2017). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“As previous studies have identified evolutionary couplings corresponding to multiple states of domain-based conformational changes15,16,20”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Discussion
What the sources say
- Not checked yetcites [15] Hopf, T. A. et al. Three-dimensional structures of membrane proteins from genomic sequencing. Cell 149, 1607–1621 (2012). source
- Not checked yetcites [16] Morcos, F., Jana, B., Hwa, T. & Onuchic, J. N. Coevolutionary signals across protein lineages help capture multiple protein conformations. Proc. Natl Acad. Sci. USA 110, 20533–20538 (2013). source
- Not checked yetcites [20] Malinverni, D. & Barducci, A. Coevolutionary analysis of protein subfamilies by sequence reweighting. Entropy 21, 1127 (2020). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The assignments from I-PINE were verified and some were adjusted manually in POKY.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > NMR data analysis and structure calculation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“MSA transformer is an unsupervised learning method, which signifies that its contact predictions purely reflect evolutionary couplings learned in sequences, rather than being supervised on structure as is the case AF2.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > Investigating evolutionary couplings from clustering using MSA Transformer
What the sources say
- Not checked yetcites [36] Rao, R. M. et al. MSA Transformer. In Proc. International Conference on Machine Learning 8844–8856 (PMLR, 2021). source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Mpt53 oxidizes the human kinase TAK1, which was shown to trigger an immune response52.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Discussion
What the sources say
- Not checked yetcites [52] Wang, L. et al. Oxidization of TGFbeta-activated kinase by MPT53 is required for immunity to Mycobacterium tuberculosis. Nat. Microbiol. 4, 1378–1388 (2019). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The final structures were validated using the wwPDB validation tool79,80 (https://validate-rcsb-east.wwpdb.org/validservice/) and the Protein Structure Validation Suite (PSVS)81.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > NMR data analysis and structure calculation
What the sources say
- Not checked yetcites [79] Xu, W. et al. Announcing the launch of Protein Data Bank China as an associate member of the Worldwide Protein Data Bank Partnership. Acta Crystallogr. D 79, 792–795 (2023). source
- Not checked yetcites [80] ww, P. D. B. c. Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res. 47, D520–D528 (2019). source
- Not checked yetcites [81] Bhattacharya, A., Tejero, R. & Montelione, G. T. Evaluating protein structures determined by structural genomics consortia. Proteins 66, 778–795 (2007). source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given that known metamorphic proteins often switch folds through cellular stimuli, it may in general be difficult to experimentally validate novel folds identified through computational methods if the stimulus—whether pH, redox reaction or a binding partner—is unknown.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Discussion
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, conformational substates not present in the evolutionary signal may require alternative methods.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Discussion
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The triple mutation was confirmed by DNA sequencing using GeneWiz primers.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > Protein expression and purification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“If the previous estimate29 that 0.5–4% of all proteins contain fold-switching domains is accurate, this would correspond to approximately 1–8 million fold-switching proteins with possible alternative states that would not be predicted by the default AF2 method.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Discussion
What the sources say
- Not checked yetcites [29] Porter, L. L. & Looger, L. L. Extant fold-switching proteins are widespread. Proc. Natl Acad. Sci. USA 115, 5968–5973 (2018). source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, the number of models returned for each state from AF-Cluster will reflect the abundance of constructs reflecting different states across the protein family, which cannot be interpreted as that state’s Boltzmann weight.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Discussion
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All of the samples were purified to homogeneity with a single band at ~10 kDa on a Bis-Tris 4–12% gradient SDS–PAGE gel (GenScript).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > Protein expression and purification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Disease-causing point mutations are often due to population changes of protein substates3,4 and there is therefore great interest for methods to predict the effects of point mutations on structural ensembles and free energy.”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Discussion
What the sources say
- Not checked yetcites [3] Wang, Z. & Moult, J. SNPs, protein structure, and disease. Hum. Mutat. 17, 263–270 (2001). source
- Not checked yetcites [4] Stein, A., Fowler, D. M., Hartmann-Petersen, R. & Lindorff-Larsen, K. Biophysical and mechanistic models for disease-causing protein variants. Trends Biochem. Sci. 44, 575–588 (2019). source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper relies on this without support.
Proteins must evolve in the context of the multiple conformational states that they adopt.
“However, proteins must evolve in the context of the multiple conformational states that they adopt.”
The paper relies on this without support.
There is a need for methods that deconvolve signal from multiple states if they are not already both present at the level of the entire MSA.
“However, there is a need for methods that deconvolve signal from multiple states if they are not already both present at the level of the entire MSA.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Wayment-Steele et al found that clustering MSAs by sequence similarity enables AF2 to sample known alternative states of KaiB, RfaH, and Mad247”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [47]
In the citing paper
“Initial efforts to enhance sampling of different conformations have involved altering the number of sequences used to generate shallower MSA representations, masking coevolutionary information provided by MSAs, and splitting conflicting coevolutionary signals by clustering MSAs46,47,105.”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [47]
In the citing paper
“we curated a dataset of 74 complexes released after 2024 and with residues less than 800 for efficiency, ensuring no overlap with Boltz-2's training data [22].”
From Reshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1 (2026) · cites this paper as [22]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Mutations to flip the KaiB equilibrium
“Indeed, we found that three mutations—I68R, V83D and N84R—were sufficient to switch a prediction of KaiBRS from the ground state to a prediction of the FS state (Fig. 3b).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > Protein expression and purification
“The triple mutant (I68R/V83D/N84A) of KaiBRS used in this study was generated according to the Q5 Site Directed Mutagenesis protocol using WT KaiBRS as a template.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > AF-Cluster predicts novel states
“As a starting point, we selected 628 proteins 48–150 amino acids in length from a database of MSAs associated with crystal structures30 (Methods).”
§ Predicting multiple conformations via sequence clustering and AlphaFold2 > Methods > Data selection for fold-switch screening
“we selected only proteins with a sequence length of between 52 and 150 residues and with more than 1,000 sequences in the alignment, totalling 628 proteins.”
Citations
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Highly accurate protein structure prediction with AlphaFold
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Cited by
Sorted by citations
Accurate structure prediction of biomolecular interactions with AlphaFold 3
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“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.”
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“Optimizing MSA subsampling to predict kinase core conformational ensembles In recent years, multiple groups have observed that AF2 with different parameters and MSA depths is capable of predicting conformational changes based on sequence data alone 27,28 .”
InfluentialEasy and accurate protein structure prediction using ColabFold
Kim, G., Lee, S., Karin, E. L., Kim, H., Moriwaki, Y., Ovchinnikov, S., Steinegger, M., & Mirdita, M. (2024) · Nature Protocols · doi:10.1038/s41596-024-01060-5 · Cited by 170
“Using AF2 to accurately sample the conformational space is under ongoing research, leading to the development of new methods 32,33 , as well as to the characterization of current limitations 34,35 .”
Self-citationAlphaFold predictions of fold-switched conformations are driven by structure memorization
Chakravarty, D., Schafer, J. W., Chen, E. A., Thole, J. F., Ronish, L. A., Lee, M., & Porter, L. L. (2024) · Nature Communications · doi:10.1038/s41467-024-51801-z · Cited by 159
“Since then, several recently developed enhanced sampling techniques have challenged our hypothesis, proposing instead that AF2 couples coevolution with a learned energy function to predict alternative conformations 10,15,17 .”
InfluentialThe power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins
Agarwal, V., & McShan, A. C. (2024) · Nature Chemical Biology · doi:10.1038/s41589-024-01638-w · Cited by 155
“Initial efforts to enhance sampling of different conformations have involved altering the number of sequences used to generate shallower MSA representations, masking coevolution-ary information provided by MSAs and splitting conflicting coevolutionary signals by clustering MSAs 46,47,105 .”
BackgroundNavigating the landscape of enzyme design: from molecular simulations to machine learning
Zhou, J., & Huang, M. (2024) · Chemical Society Reviews · doi:10.1039/d4cs00196f · Cited by 168
“49 AF-cluster 43 also demonstrated to be able to predict the conformational transition caused by point mutations in the case of KaiB from Rhodobacter sphaeroides .”
Exploring structural diversity across the protein universe with The Encyclopedia of Domains
Lau, A. M., Bordin, N., Kandathil, S. M., Sillitoe, I., Waman, V. P., Wells, J., Orengo, C. A., & Jones, D. T. (2024) · Science · doi:10.1126/science.adq4946 · Cited by 140
“…of divergent models for a given sequence must be that the models were generated using significantly different MSA information, and we note that explicit pre-sampling of the MSAs given to AF2 has been explored recently as a way to persuade the network to generate alternate conformations (39, 40).”
Multistate and functional protein design using RoseTTAFold sequence space diffusion
Lisanza, S. L., Gershon, J. M., Tipps, S. W. K., Sims, J. N., Arnoldt, L., Hendel, S. J., Simma, M. K., Liu, G., Yase, M., Wu, H., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02395-w · Cited by 189
Sparks of function by de novo protein design
Chu, A. E., Lu, T., & Huang, P. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02133-2 · Cited by 131
“Recent methods for conformational sampling such as EigenFold, Distributional Gra-phormer, PepFlow and MSA subsampling and clustering with AlphaFold can build toward methods in this direction 140–144 .”
MethodStructure prediction of alternative protein conformations
Bryant, P., & Noé, F. (2024) · Nature Communications · doi:10.1038/s41467-024-51507-2 · Cited by 105
Omidi, A., Møller, M. H., Malhis, N., Bui, J. M., & Gsponer, J. A. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2406407121 · Cited by 109
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“Moreover, successes in sequence-based drug design and protein structure prediction have highlighted the value of sequence information in protein representation 14,15,61–63 .”
AFsample2 predicts multiple conformations and ensembles with AlphaFold2
Kalakoti, Y., & Wallner, B. (2025) · Communications Biology · doi:10.1038/s42003-025-07791-9 · Cited by 126
Masters, M. R., Mahmoud, A. H., & Lill, M. A. (2025) · Nature Communications · doi:10.1038/s41467-025-63947-5 · Cited by 124
“…of these models has been profound, spawning hundreds of subsequent papers including high-throughput structure prediction 3 – 5 , ensemble sampling 6 – 8 , molecular docking 9 – 12 , stability prediction 13 – 16 , further method development 17 – 20 , and a host of other applied studies 21 – 24 .”
Ligand discovery by activity-based protein profiling
Niphakis, M. J., & Cravatt, B. F. (2024) · Cell chemical biology · doi:10.1016/j.chembiol.2024.08.006 · Cited by 93
Koh, H. Y., Zheng, Y., Yang, M., Arora, R., Webb, G. I., Pan, S., Li, L., & Church, G. M. (2025) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00349-8 · Cited by 117
“Directed evolution is an outcome-driven method that iteratively screens mutated protein variants to achieve the objective, whereas rational design is a knowledge-driven approach that relies on an understanding of sequence–structure–function relationships to guide the design.”
AlphaFold two years on: Validation and impact
Kovalevskiy, O. V., Mateos-Garcia, J. C., & Tunyasuvunakool, K. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2315002121 · Cited by 90
Proteins with alternative folds reveal blind spots in AlphaFold-based protein structure prediction
Chakravarty, D., Lee, M., & Porter, L. L. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102973 · Cited by 76
Diffusion models in protein structure and docking
Yim, J., Stärk, H., Corso, G., Jing, B., Barzilay, R., & Jaakkola, T. (2024) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1711 · Cited by 87
“Faster (non-generative) deep learning methods based on evolutionary clustering 74 have been developed to realize multiple possible conformations.”
BackgroundChemical Synthesis of Human Proteoforms and Application in Biomedicine
Ai, H., Pan, M., & Liu, L. (2024) · ACS Central Science · doi:10.1021/acscentsci.4c00642 · Cited by 71
Deep learning for protein structure prediction and design—progress and applications
Jänes, J., & Beltrão, P. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00016-x · Cited by 76
Beyond static structures: protein dynamic conformations modeling in the post-AlphaFold era
Cui, X., Ge, L., Chen, X., Lv, Z., Wang, S., Zhou, X., & Zhang, G. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf340 · Cited by 70
“Moreover, several research [18–22] built on the artificial intelligence (AI) protein structure prediction methods [11–13, 23], such as AlphaFold2 [ In the post-AlphaFold era, driven by breakthrough advancements in static protein structures, the paradigm of protein research is gradually shifting…”
Predicting protein conformational motions using energetic frustration analysis and AlphaFold2
Guan, X., Tang, Q.-Y., Ren, W., Chen, M., Wang, W., Wolynes, P. G., & Li, W. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2410662121 · Cited by 62
Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development
Nam, K., Shao, Y., Major, D. T., & Wolf‐Watz, M. (2024) · ACS Omega · doi:10.1021/acsomega.3c09084 · Cited by 57
Wu, X., Lin, H., Bai, R., & Duan, H. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.116262 · Cited by 52
Physics-based modeling in the new era of enzyme engineering
Jurich, C. P., Shao, Q., Ran, X., & Yang, Z. J. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00788-8 · Cited by 47
Qian, R., Xue, J., Xu, Y. C., & Huang, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01024 · Cited by 47
Hydrogen/Deuterium Exchange Mass Spectrometry: Fundamentals, Limitations, and Opportunities
Konermann, L., & Scrosati, P. M. (2024) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2024.100853 · Cited by 47
Computational advances in discovering cryptic pockets for drug discovery
Bemelmans, M. P., Cournia, Z., Damm‐Ganamet, K. L., Gervasio, F. L., & Pande, V. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102975 · Cited by 53
Recent Progress of Protein Tertiary Structure Prediction
Wuyun, Q., Chen, Y., Shen, Y., Cao, Y., Hu, G., Cui, W., Gao, J., & Zheng, W. H. (2024) · Molecules · doi:10.3390/molecules29040832 · Cited by 48
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Modeling protein–ligand interactions for drug discovery in the era of deep learning
Wang, Y. X., Li, Y., Chen, J., & Lai, L. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00415b · Cited by 53
Protein representations: Encoding biological information for machine learning in biocatalysis
Harding-Larsen, D., Funk, J., Madsen, N. G., Gharabli, H., Acevedo-Rocha, C. G., Mazurenko, S., & Welner, D. H. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108459 · Cited by 38
Lazou, M., Khan, O., Nguyen, T., Padhorny, D., Kozakov, D., Joseph‐McCarthy, D., & Vajda, S. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2412719121 · Cited by 37
The type V effectors for CRISPR/Cas-mediated genome engineering in plants
Zhang, R., Chai, N., Liu, T., Zheng, Z., Lin, Q., Xie, X., Wen, J., Yang, Z. F., Liu, Y., & Zhu, Q. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108382 · Cited by 36
Aranganathan, A., Gu, X., Wang, D., Vani, B. P., & Tiwary, P. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103000 · Cited by 39
“…that reduces the information entering the AF2 by randomly sampling sequences from MSA, AF cluster that clusters the MSA sequence based on sequence similarity [14], SPEACH_AF [15] that perturbs the MSA with Alanines, and, more recently, local frustration-based MSA clustering/perturbing method [16].”
Lee, M., Schafer, J. W., Prabakaran, J., Chakravarty, D., Clore, M. F., & Porter, L. L. (2025) · Nature Communications · doi:10.1038/s41467-025-60759-5 · Cited by 38
“(Remarks on code availability) Version 2: Reviewer comments: Reviewer #1 (Remarks to the Author) The revisions have strengthened the manuscript.”
Pajkos, M., Clerc, I., Zanon, C., Bernadó, P., & Cortés, J. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169003 · Cited by 39
P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matching
Jin, Y., Huang, Q., Song, Z., Zheng, M., Teng, D., & Shi, Q. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01620 · Cited by 33
Zhu, J., Li, Z., Zhang, B., Zheng, Z., Zhong, B., Bai, J., Hong, X., Wang, T., Wei, T., Yang, J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.05.592611 · Cited by 32
“For other IDP systems, the number of conformations produced by AF-cluster was also limited by the availability of MSA data, making AF-cluster less versatile for IDP applications 30 .”
MethodRaisinghani, N., Alshahrani, M., Gupta, G., & Verkhivker, G. M. (2024) · Viruses · doi:10.3390/v16091458 · Cited by 33
“…the number of sequences subsampled from the MSA ( max_seqs sets the number of sequences passed to the row/column attention track and extra_seqs the number of sequences additionally processed by the main evoformer stack). and KP. variants using AF2 methodology [72,73] with varied MSA depth [74–76].”
BackgroundMethodInfluentialArtificial intelligence for medicine 2025: Navigating the endless frontier
Dai, J., Xu, H., Chen, T., Huang, T., Liang, W., Zhang, R., Xu, G.-X., Zhang, Z., Xue, L., Gao, Y. Q., et al. (2025) · The Innovation Medicine · doi:10.59717/j.xinn-med.2025.100120 · Cited by 35
Deep learning methods for protein structure prediction
Qin, Y., Chen, Z., Ye, P., Xiao, Y., Zhong, T., & Yu, X. (2024) · MedComm – Future Medicine · doi:10.1002/mef2.96 · Cited by 33
Yao, M., Miller, G. W., Vardarajan, B. N., Baccarelli, A., Guo, Z., & Liu, Z. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100700 · Cited by 35
Structural Biology of RNA and Protein‐RNA Complexes after AlphaFold3
Hennig, J. (2025) · ChemBioChem · doi:10.1002/cbic.202401047 · Cited by 33
Sequence clustering confounds AlphaFold2
Schafer, J. W., Lee, M., Chakravarty, D., Thole, J. F., Chen, E. A., & Porter, L. L. (2025) · Nature · doi:10.1038/s41586-024-08267-2 · Cited by 38
Metamorphic proteins and how to find them
Porter, L. L., Artsimovitch, I., & Ramírez‐Sarmiento, C. A. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102807 · Cited by 29
Wu, T., Stein, R. A., Kao, T., Brown, B. P., & Mchaourab, H. S. (2025) · Nature Communications · doi:10.1038/s41467-025-62582-4 · Cited by 34
Neural Clustering Based Visual Representation Learning
Chen, G., Li, X., Yang, Y., & Wang, W. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00546 · Cited by 29
AlphaFold2 has more to learn about protein energy landscapes
Chakravarty, D., Schafer, J. W., Chen, E. A., Thole, J. R., & Porter, L. L. (2023) · doi:10.1101/2023.12.12.571380 · Cited by 159
“This may not be the case, however, because AF2 predicts the folds of ground state oligomeric structures, such as KaiB, with the monomer model (17).”
BackgroundMethodInfluentialArtificial intelligence methods for protein folding and design
Zhang, Z., Ou, C., Cho, Y., Akiyama, Y., & Ovchinnikov, S. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103066 · Cited by 29
Self-citationAdvances in the Simulations of Enzyme Reactivity in the Dawn of the Artificial Intelligence Age
Świderek, K., Bertrán, J., Zinovjev, K., Tuñón, I., & Moliner, V. (2025) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70003 · Cited by 29
“These ML methods can be seen as a shortcut for the exploration of the protein FEL, allowing better identification of those states or substates participating in protein function and the possible communication pathways existing at each conformational state [45].”
Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Transporters?
Xie, T., & Huang, J. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01936 · Cited by 23
AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease
Zhang, H., Lan, J., Wang, H., Lu, R., Zhang, N., He, X., Yang, J., & Chen, L. (2024) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2024.1414916 · Cited by 24
Advances in structure-based allosteric drug design
Li, R., He, X., Wu, C., Li, M., & Zhang, J. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102974 · Cited by 22
AlphaFold and what is next: bridging functional, systems and structural biology
Szczepski, K., & Jaremko, Ł. (2025) · Expert Review of Proteomics · doi:10.1080/14789450.2025.2456046 · Cited by 25
“…different protein structure prediction models (with two additional method for generating conformational ensembles developed by Del Alamo et al. [90] and Wayment-Steele et al. [91]), against a set of 16 membrane transporters in both inward-facing and outward-facing states (32 structures in total).”
van der Weg, K. J., Merdivan, E., Piraud, M., & Gohlke, H. (2025) · Nature Communications · doi:10.1038/s41467-025-57324-5 · Cited by 26
“Recent methods such as AlphaFlow 75 or MSA tuning 76,77 would allow for the creation of databases with multiple conformations for an enzyme basedonvariousstatesofproteins.”
Generative AI for computational chemistry: A roadmap to predicting emergent phenomena
Tiwary, P., Herron, L., John, R., Lee, S., Sanwal, D., & Wang, R. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2415655121 · Cited by 36
Applicability of AlphaFold2 in the modeling of dimeric, trimeric, and tetrameric coiled‐coil domains
Madaj, R., Martinez‐Goikoetxea, M., Kaminski, K., Ludwiczak, J., & Dunin‐Horkawicz, S. (2025) · Protein Science · doi:10.1002/pro.5244 · Cited by 23
Olanders, G., Testa, G., Tibo, A., Nittinger, E., & Tyrchan, C. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01475 · Cited by 26
A General Substitution Matrix for Structural Phylogenetics
Garg, S. G., & Hochberg, G. K. A. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf124 · Cited by 29
Liu, Z., Guo, X., Xu, Y., & Wu, J. (2025) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.4c08630 · Cited by 23
Emerging frontiers in protein structure prediction following the AlphaFold revolution
Rennie, M. L., & Oliver, M. R. (2025) · Journal of The Royal Society Interface · doi:10.1098/rsif.2024.0886 · Cited by 34
Yang, S., & Song, C. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01222 · Cited by 18
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Raisinghani, N., Alshahrani, M., Gupta, G., Tian, H., Xiao, S., Tao, P., & Verkhivker, G. M. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00222 · Cited by 17
Multimeric protein interaction and complex prediction: Structure, dynamics and function
Lu, D., Yu, S., Huang, Y., & Gong, X. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.05.009 · Cited by 21
“These conformational changes not only affect the stability and function of proteins, but may also play a key role in the development of diseases [127].”
AI meets physics in computational structure-based drug discovery for GPCRs
Michino, M., Vendôme, J., & Kufareva, I. (2025) · npj Drug Discovery. · doi:10.1038/s44386-025-00019-0 · Cited by 25
Porter, L. L., Chakravarty, D., Schafer, J. W., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.21.567977 · Cited by 18
Predicting the conformational flexibility of antibody and T-cell receptor CDRs
Spoendlin, F. C., Fernández-Quintero, M. L., Raghavan, S. S. R., Turner, H. L., Gharpure, A., Loeffler, J. R., Wong, W. K., Bujotzek, A., Georges, G., Ward, A. B., et al. (2025) · doi:10.1101/2025.03.19.644119 · Cited by 20
ExEnDiff: An Experiment-Guided Diffusion Model for Protein Conformational Ensemble Generation
Liu, Y., Yu, Z., Lindsay, R. J., Lin, G., Chen, M., Sahoo, A., & Hanson, S. M. (2025) · PRX Life · doi:10.1103/prxlife.3.023013 · Cited by 28
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Computational design of conformation-biasing mutations to alter protein functions
Cavanagh, P. E., Xue, A. G., Dai, S. A., Qiang, A., Matsui, T., & Ting, A. Y. (2026) · Science · doi:10.1126/science.adv7953 · Cited by 34
Duran, C., Casadevall, G., & Osuna, S. (2024) · Faraday Discussions · doi:10.1039/d3fd00156c · Cited by 20
Herrington, N. B., Li, Y. C., Li, Y. C., Pandey, G. S., & Schlessinger, A. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012302 · Cited by 27
Explaining Conformational Diversity in Protein Families through Molecular Motions
Lombard, V., Grudinin, S., & Laine, É. (2024) · Scientific Data · doi:10.1038/s41597-024-03524-5 · Cited by 21
“These approaches involve forced sampling 10 , modulation of input multiple sequence alignment content and depth 11,12 , or guidance with state-annotated templates 13,14 .”
ESM ‐scan—A tool to guide amino acid substitutions
Totaro, M. G., Vide, U., Zausinger, R., Winkler, A., & Oberdorfer, G. (2024) · Protein Science · doi:10.1002/pro.5221 · Cited by 19
Zhang, Y., Zhang, Z., Kagaya, Y., Terashi, G., Zhao, B., Xiong, Y., & Kihara, D. (2025) · Communications Biology · doi:10.1038/s42003-025-08783-5 · Cited by 19
“Approaches include manipulation on multiple sequence alignments (MSAs) via subsampling 27 – 29 , clustering 30 , or amino acid replacements 31 , and deep generative models 32 – 34 .”
Assessing AF2’s ability to predict structural ensembles of proteins
Riccabona, J. R., Spoendlin, F. C., Fischer, A.-L. M., Loeffler, J. R., Quoika, P. K., Jenkins, T. P., Ferguson, J. A., Smorodina, E., Laustsen, A. H., Greiff, V., et al. (2024) · Structure · doi:10.1016/j.str.2024.09.001 · Cited by 17
The conformational landscape of fold-switcher KaiB is tuned to the circadian rhythm timescale
Wayment-Steele, H. K., Otten, R., Pitsawong, W., Ojoawo, A. M., Glaser, A., Calderone, L. A., & Kern, D. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2412293121 · Cited by 19
“Could it be possible that the other structure predicted by AF - Cluster is the Enigma state?”
InfluentialSelf-citationRaisinghani, N., Alshahrani, M., Gupta, G., Xiao, S., Tao, P., & Verkhivker, G. M. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c01341 · Cited by 14
An outlook on structural biology after A lpha F old: tools, limits and perspectives
Rosignoli, S., Pacelli, M., Manganiello, F., & Paiardini, A. (2024) · FEBS Open Bio · doi:10.1002/2211-5463.13902 · Cited by 21
Recent progress and future challenges in structure-based protein-protein interaction prediction
Yuan, R., Zhang, J., Zhou, J., & Cong, Q. (2025) · Molecular Therapy · doi:10.1016/j.ymthe.2025.04.003 · Cited by 25
“Methods to diversify the predicted structures include subsampling the input MSAs, 241 introducing dropouts in the AF2 network during inference (AFsample by Wallner et al.)”
Generation of protein dynamics by machine learning
Janson, G., & Feig, M. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103115 · Cited by 18
Spoendlin, F. C., Fernández-Quintero, M. L., Raghavan, S. S. R., Turner, H. L., Gharpure, A., Loeffler, J. R., Wong, W. K., Bujotzek, A., Georges, G., Ward, A. B., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01131-6 · Cited by 20
“22,23,26–30).”
Alignment-based Protein Mutational Landscape Prediction: Doing More with Less
Abakarova, M., Marquet, C., Rera, M., Rost, B., & Laine, E. (2023) · Genome Biology and Evolution · doi:10.1093/gbe/evad201 · Cited by 14
They all rock: A systematic comparison of conformational movements in LeuT-fold transporters
Licht, J. A., Berry, S. P., Gutierrez, M. A., & Gaudet, R. (2024) · Structure · doi:10.1016/j.str.2024.06.015 · Cited by 19
Decoding the functional impact of the cancer genome through protein–protein interactions
Fu, H., Mo, X., & Ivanov, A. A. (2025) · Nature reviews. Cancer · doi:10.1038/s41568-024-00784-6 · Cited by 22
“For example, AF3 predicts E3 ubiquitin ligases exclusively in their closed form, corresponding to their ligand-bound state 153,160 .”
Exploring structural diversity across the protein universe with The Encyclopedia of Domains
Lau, A. M., Bordin, N., Kandathil, S. M., Sillitoe, I., Waman, V. P., Wells, J., Orengo, C. A., & Jones, D. T. (2024) · doi:10.1101/2024.03.18.585509 · Cited by 140
Advancing protein structure prediction beyond AlphaFold2
Park, S., Myung, S., & Baek, M. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.102985 · Cited by 18
Frustration in physiology and molecular medicine
Parra, R. G., Komives, E. A., Wolynes, P. G., & Ferreiro, D. U. (2025) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2025.101362 · Cited by 18
Xie, P., Xu, Y., Tang, J., Wu, S., & Gao, H. (2024) · Communications Biology · doi:10.1038/s42003-024-06193-7 · Cited by 16
“To furtherverify this, we generated a structural similarity dendrogram with the Ec PhoB PDB structures and predicted structures as AlphaFold2 can generate various conformations that exist naturally, even with identical input protein sequences, which are equivalent to multiple same sequences 63 .”
Guo, W., Ren, K., Zhao, L., et al. (2024) · Food Chemistry X · doi:10.1016/j.fochx.2024.101940 · Cited by 16
Do Deep Learning Models for Co-Folding Learn the Physics of Protein-Ligand Interactions?Preprint
Masters, M., Mahmoud, A., & Lill, M. A. (2024) · bioRxiv · doi:10.1101/2024.06.03.597219 · Cited by 11
“…these models has been profound, spawning hundreds of subsequent papers including high-throughput structure prediction [3, 4, 5], ensemble sampling [6, 7, 8], molecular docking [9, 10, 11, 12], stability prediction [13, 14, 15, 16], further method development [17, 18, 19, 20], and a host of other…”
BackgroundImproving AlphaFold 3 structural modeling by incorporating explicit crosslinksPreprint
Gilep, K., Obarska-Kosińska, A., & Kosiński, J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.03.626671 · Cited by 19
From machine learning to multimodal models: The AI revolution in enzyme engineering
Shi, Z., Xu, S., Xue, S., Chen, K., Lu, Y., Wang, F., Long, S., Tian, Y., Zhang, P., Wang, J., et al. (2025) · BioDesign Research · doi:10.1016/j.bidere.2025.100044 · Cited by 18
“In the field of molecular interaction prediction between proteins and nucleic acids , models such as AlphaFold 2 [148], AlphaFold 3 [42], RoseTTAFold All-Atom [143], and Chai-1 [38] have demonstrated the capability of modeling multi-molecule coupled systems(Fig.”
Ohnuki, J., & Okazaki, K.-I. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c02726 · Cited by 16
Metadata
- Authors
- Wayment-Steele, H. K., Ojoawo, A., Otten, R., Apitz, J. M., Pitsawong, W., Hömberger, M., Ovchinnikov, S., Colwell, L., & Kern, D.
- Affiliations
- Howard Hughes Medical Institute (US)
- Harvard University (US)
- Google (United States) (US)
- Corresponding author
- Kern, Dorothee
- ORCID
- Venue
- Nature
- Published
- 2023-11-13
- Volume
- 625
- Issue
- 7996
- Pages
- 832-839
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC10808063
- OpenAlex
- W4388627063
- Semantic Scholar
- 6d464c44cad5eb182ae580adfff8624562e457b8
- Cited by
- 545
- References
- 90
- Citations by year
- 2026: 175 · 2025: 202 · 2024: 160 · 2023: 8
- FWCI
- 48.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Funders
- National Institute of General Medical Sciences (P41GM111135, T32GM135126, R24GM141526)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Keywords
- protein conformational switching, sequence clustering, AlphaFold2, KaiB, fold switching, point mutations, protein energy landscape, oxidative enzymes, protein family evolution
- MeSH terms
- Machine Learning, Antigens, Bacterial, Bacterial Proteins, Mutation, Protein Conformation, Proteins, Rhodobacter sphaeroides, Cluster Analysis, Sequence Alignment, Protein Folding
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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