AlphaFold can predict a protein’s structure, but a 2024 study found it rarely recovered both known structures of proteins that switch between distinct folds. Across seven fold-switching proteins whose structures were confirmed after the models’ training, the combined AlphaFold approaches captured both folds for just one. The result is a focused limitation—not evidence that AlphaFold is generally useless—and raises a question about whether some apparent successes reflect memorized structures rather than a learned account of how proteins fold.
What does it mean for a protein to switch folds?
A fold-switching protein can adopt two distinct, experimentally observed structures. This is more than a small movement or a change in orientation: the protein’s overall fold changes. In some cases, a biological context or cellular event is associated with which form it takes.
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That makes fold-switchers a useful but deliberately demanding test for structure prediction. Producing one plausible structure does not show that a model captures the protein’s alternative conformation, or the conditions under which the protein adopts it. Chakravarty and colleagues treated the two experimentally observed folds as a minimalist approximation of a protein’s broader folded-state energy landscape; they did not claim that these two structures describe every possible state.
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In a 2024 study in Nature Communications, Chakravarty and colleagues combined more than 280,000 models from several AlphaFold2 and AlphaFold3 implementations to examine fold-switching proteins likely represented in training. Their success measure was strict: a protein counted only if the methods recovered both experimentally determined conformations. By that measure, they succeeded for 32 of 92 proteins, or 35%.
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The researchers also tested seven fold-switchers whose structures had been experimentally confirmed after the models’ training. AlphaFold captured both known folds for one of the seven. The paper’s abstract describes approximately 280,000 additional predictions for this test; its discussion summarizes the overall effort as more than 500,000 structures across 99 fold-switchers. These are different descriptions of the sampling effort, not competing estimates of the same subset.
The counts should not be read as a general accuracy score for AlphaFold. They measure recovery of both known conformations in a particular set of proteins, with the study’s distinction between likely training-set exposure and structures confirmed after training.
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Why does training-set exposure matter?
Fold-switching proteins make it possible to ask whether a model can represent more than a structure it may have encountered in its training data. The contrast in this study was between proteins likely represented in training and a small group whose folds were confirmed afterward. AlphaFold recovered both forms more often in the former group, but even there the outcome was 32 of 92 under the study’s two-fold criterion.
The authors interpret evidence from some predictions as consistent with AlphaFold2 relying on memorized structures rather than deriving the alternative fold from sequence coevolution signals. This is an interpretation of results from the tested systems, not a demonstration that every AlphaFold prediction is memorized or that memorization explains every success.
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Can confidence scores identify the alternative fold?
Not reliably in this test, the authors report. AlphaFold2 confidence scores tended to select against experimentally observed alternative folds and did not distinguish low- from high-energy conformations. A confident prediction of one structure therefore should not, on this evidence, be treated as proof that the model has identified the protein’s full set of conformations or their relative energetic favorability.
The study also discusses a specific AlphaFold3 XCL1 prediction in which evolutionary restraints were misassigned. That case illustrates a possible failure mode in this analysis; it does not establish how often such errors occur across other proteins or predictions.
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What the study does—and does not—show
What it supports
- For the fold-switchers examined, recovering one known structure was not equivalent to recovering both experimentally observed folds.
- In the study’s comparison, the models recovered both folds for a minority of proteins likely represented in training, and for one of seven proteins confirmed after training.
- Confidence metrics and some prediction behavior did not provide reliable evidence, in this test, that the alternative conformation or its energetic status had been captured.
What it does not establish
- It does not show that AlphaFold is broadly ineffective for proteins with a single relevant structure or for other structure-prediction tasks.
- It does not prove that all successful predictions come from memorization, or that the authors’ proposed explanation applies to every model output.
- It does not map the complete energy landscape of the tested proteins: two experimentally observed conformations provide a simplified test, not an exhaustive catalogue of states.
The practical takeaway is to interpret a single predicted structure in light of the biological question. If a protein is known to adopt distinct folds, the relevant test is whether a method can account for both forms—not merely whether one output looks plausible.
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The primary article is Chakravarty et al., “AlphaFold predictions of fold-switched conformations are driven by structure memorization,” Nature Communications 15, article 7296, published August 24, 2024. The authors’ supporting analysis is available through the linked Zenodo record and GitHub repository. For background on why fold switching matters biologically, see Andy Extance’s Chemistry World account published September 30, 2024.
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