Missense mutation knowledge can decrease prediction inaccuracies on protein secondary structure
Bibliographic record
Abstract
Abstract Protein tertiary structure prediction models like AlphaFold2 have revolutionized the field with unprecedented accuracy. Yet predicting structural changes arising from single amino acid mutations remains a challenge. The complexity introduced by these mutations calls for models that can incorporate mutational information into their predictions. We propose a novel refinement strategy for protein secondary structure prediction that leverages missense mutational data. As part of this strategy, we introduce Mut2Dens , a model that not only yields improved consistency of predictions for mutational data, but also maintains robust predictive performance on non-mutational datasets. Mut2Dens takes multiple predicted secondary structures and generates a mutation-aware secondary structure. This awareness comes from our mutational dataset, learning to avoid common mistakes in prediction methods after a missense mutation occurs. In particular, Mut2Dens employs the extremely randomized trees (ExtraTree) algorithm to avoid overfitting and makes effective use of the limited mutational data available from experimentally determined three-dimensional structures. By combining predictions from highly accurate structure prediction models, we create an ensemble that integrates their strengths while enhancing mutational capabilities. This refinement strategy also improves the non-mutational performance of state-of-the-art methods by addressing their most inaccurate and least confident predictions. Moreover, it reduces improbable outcomes in mutated protein structures—such as transforming π -helices into β -sheets—that can still occur in current prediction models. Finally, by using interpretable machine learning algorithms (e.g., ExtraTree), we can reveal the underlying biological knowledge from the refinement model; the insights gained from Mut2Dens can be corroborated with known mutational outcomes, helping users pinpoint discrepancies across structure prediction models and make more informed decisions regarding the predicted structures. The data utilized here is available at https://github.com/ivanpmartell/sam-models .
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".