Challenging AlphaFold in predicting proteins with large-scale allosteric transitions
Bibliographic record
Abstract
Many proteins function by toggling between distinct conformations, yet most structure predictors have been trained on data that do not capture this conformational diversity. Here, we benchmarked AlphaFold2, AlphaFold3, and recent variants on autoinhibited proteins, a class of allosterically regulated, often multi-domain proteins that exist in equilibrium between active and autoinhibited states. Our analyses show that AlphaFold2 fails to reproduce the experimental structures of many autoinhibited proteins, which is reflected in reduced confidence scores. This contrasts sharply with its high-accuracy, high-confidence predictions of non-autoinhibited multi-domain proteins. When tested for its ability to capture conformational diversity, we found that AlphaFold2 performs better when combined with uniform subsampling of sequence alignments rather than local subsampling. BioEmu and AlphaFold3 improve upon these results, yet still struggle to accurately reproduce details of experimental structures. Together, our study underscores the persistent challenges of predicting protein structures shaped by complex energy landscapes. Although many proteins function by toggling between distinct conformations, most structure predictors remain limited to a single static fold. Here, the authors test the performance of AlphaFold2, AlphaFold3, and recent variants on a dataset of autoinhibited proteins exhibiting at least two functionally distinct conformations, and show that AlphaFold2 fails to reproduce the experimental structures of many autoinhibited proteins, but that it can capture conformational diversity when using uniform multiple sequence alignment subsampling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".