Uncovering distinct protein conformations using coevolutionary information and AlphaFold
Bibliographic record
Abstract
Protein structure prediction has been transformed by AlphaFold, yet a key challenge remains: characterizing the multiple conformations adopted by proteins that can switch between different folds, without knowledge of their potential binding partners. Existing methods rely on sampling the multiple sequence alignment (MSA), either through random sampling or clustering, but these methods are statistically inefficient and do not explicitly utilize coevolutionary information during MSA sampling. We introduce an iterative sampling framework that systematically explores the MSA space using residue-specific frequencies and coevolutionary patterns inferred via Markov random fields. We further develop tools to identify a protein’s variable region and extract representative structures, yielding a compact, high-quality ensemble with good coverage of distinct conformations. On a benchmark set of fold-switching proteins, our method outperforms existing ones by substantially improving the diversity of the sampled structures. Overall, this work significantly advances our ability to characterize the conformational landscape of proteins.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".