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AlphaFold often did not recover both known structures of fold-switching proteins in a 2024 study. It found both experimentally observed conformations for 32 of 92 proteins likely represented in its training data (35%); among seven proteins confirmed after training, it captured both for one. The results point to a specific limitation in this test—not proof that AlphaFold is broadly ineffective or that every prediction is simply memorized.

What it means for a protein to switch folds

A fold-switching protein can adopt two distinct, experimentally observed structures. This is more than a small movement of a flexible loop or a rigid-body shift: the protein changes its overall fold. In some cases, which conformation it adopts is linked to biological context.

Predicting one of those structures does not show that a model can represent the other. Chakravarty and colleagues treated the two known conformations as a simplified test of a protein’s broader landscape of possible folded states; they did not claim that two structures describe every state a protein can occupy. The study, published in Nature Communications on August 24, 2024, focused on whether AlphaFold could recover both known folds.

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How often did AlphaFold recover both known structures?

The study pooled predictions from AlphaFold2 and AlphaFold3 implementations. Its success criterion was strict: a protein counted as a success only if the models recovered both experimentally determined conformations. Finding one structure alone did not qualify.

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Test group Result What the result measures
92 fold-switching proteins likely represented in training 32 of 92 (35%) Both experimentally observed folds recovered
7 fold-switching proteins confirmed after training 1 of 7 (14%) Both experimentally observed folds recovered

For the likely-in-training group, the authors assessed more than 280,000 combined AlphaFold2 and AlphaFold3 models. They generated approximately 280,000 additional predictions for the seven-protein test set. In the paper’s discussion, the authors describe the overall sampling effort as more than 500,000 structures across 99 fold-switchers. These figures refer to different summaries of the study’s analyses, not to a single number of distinct proteins.

Why training-set exposure matters

The contrast between the two groups makes training exposure central to interpreting the findings. The 92 proteins were considered likely to have been represented in training; the seven were experimentally confirmed after training. The models recovered both folds more often in the first group, but the study’s results do not by themselves establish why every individual prediction succeeded or failed.

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The authors interpret some apparent successes as evidence consistent with structure memorization: a model may reproduce structures encountered during training rather than demonstrate that it has learned the physical principles needed to predict a protein’s alternative fold. This is a proposed explanation for results in the tested systems, not a conclusion that all AlphaFold predictions are memorized or that the model has learned nothing about protein structure.

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What confidence scores and the XCL1 case reveal

In this test, AlphaFold2 confidence scores tended to disfavor experimentally observed alternative folds. The authors also report that those scores did not reliably distinguish low- from high-energy conformations in the cases they examined. A high confidence score therefore should not be treated as evidence, by itself, that a fold-switching protein’s alternative state has been ruled out or that the predicted state is the only biologically relevant one.

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The paper also discusses an AlphaFold3 prediction for human lymphotactin (XCL1) in which evolutionary restraints were misassigned. The authors use this case to illustrate how a prediction can be affected by how coevolutionary signals are handled. It is a specific example, not an explanation for every model output.

What readers should—and should not—conclude

  • Supported by this test: recovering both known folds was difficult, especially for the seven proteins whose structures were confirmed after model training.
  • Supported as an interpretation: some results are consistent with models relying on structures encountered during training rather than successfully deriving an alternative fold from sequence signals.
  • Not established: that AlphaFold is useless for ordinary single-structure prediction, that every successful prediction is memorization, or that the tested proteins represent all proteins with multiple conformations.
  • Practical implication: for a suspected fold-switcher, one predicted structure is not enough to establish that the model captures its alternatives. Experimental evidence and biological context remain important when interpreting such predictions.
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Where to inspect the study and its data

The paper’s supporting analysis is linked through its Zenodo record and GitHub repository. For a specialist account of why fold switching matters to biology and disease, see Chemistry World’s September 30, 2024 report.

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