High‐throughput prediction of disordered moonlighting regions in protein sequences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intrinsically disordered regions lack stable structure in their native conformation but are nevertheless functional and highly abundant, particularly in Eukaryotes. Disordered moonlighting regions (DMRs) are intrinsically disordered regions that carry out multiple functions. DMRs are different from moonlighting proteins that could be structured and that are annotated at the whole-protein level. DMRs cannot be identified by current predictors of functions of disorder that focus on specific functions rather than multifunctional regions. We conceptualized, designed and empirically assessed first-of-its-kind sequence-based predictor of DMRs, DMRpred. This computational tool outputs propensity for being in a DMR for each residue in an input protein sequence. We developed novel amino acid indices that quantify propensities for functions relevant to DMRs and used evolutionary conservation, putative solvent accessibility and intrinsic disorder derived from the input sequence to build a rich profile that is suitable to accurately predict DMRs. We processed this profile to derive innovative features that we input into a Random Forest model to generate the predictions. Empirical assessment shows that DMRpred generates accurate predictions with area under receiver operating characteristic curve = 0.86 and accuracy = 82%. These results are significantly better than the closest alternative approaches that rely on sequence alignment, evolutionary conservation and putative disorder and disorder functions. Analysis of abundance of putative DMRs in the human proteome reveals that as many as 25% of proteins may have long >30 residues) DMRs. A webserver implementation of DMRpred is available at http://biomine.cs.vcu.edu/servers/DMRpred/.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it