Comprehensive Comparative Assessment of In-Silico Predictors of Disordered Regions
Bibliographic record
Abstract
Intrinsic disorder is relatively common in proteins, plays important roles in numerous cellular activities, and its prevalence was implicated in various human diseases. However, annotations of the disorder lag behind the rapidly increasing number of known protein chains. The last decade observed development of a relatively large number of in-silico methods that predict the disorder using the protein sequence as their input. We perform a first-of-its kind comprehensive empirical evaluation of the disorder predictors which is characterized by three novel aspects, (1) we evaluate the quality of the disorder predictions at the residue, segment, and chain levels; (2) we consider a large number of published and accessible to the end user predictors that are evaluated on a relatively big dataset with close to 500 proteins; and (3) we assess statistical significance of differences between the considered methods. Our study reveals that there is no universally superior predictor and that the top-performing methods are complementary. We show that while recent consensus-based predictors outperform other considered methods for the residue-level predictions, some older methods perform better for the prediction of the disordered segments. Our analysis indicates that certain predictors are biased to under-predict the disorder, while some other solutions tend to over-predict the number of the disordered residues. We also evaluate the utility of the predicted residue-level disorder for prediction of proteins with long disordered segments and prediction of the chainlevel disorder content. Lastly, we provide recommendations concerning development of a new generation of consensusbased methods and specialized methods for improved prediction of the disorder content.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".