DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues
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
Protein-DNA and protein-RNA interactions are part of many diverse and essential cellular functions and yet most of them remain to be discovered and characterized. Recent research shows that sequence-based predictors of DNA-binding residues accurately find these residues but also cross-predict many RNA-binding residues as DNA-binding, and vice versa. Most of these methods are also relatively slow, prohibiting applications on the whole-genome scale. We describe a novel sequence-based method, DRNApred, which accurately and in high-throughput predicts and discriminates between DNA- and RNA-binding residues. DRNApred was designed using a new dataset with both DNA- and RNA-binding proteins, regression that penalizes cross-predictions, and a novel two-layered architecture. DRNApred outperforms state-of-the-art predictors of DNA- or RNA-binding residues on a benchmark test dataset by substantially reducing the cross predictions and predicting arguably higher quality false positives that are located nearby the native binding residues. Moreover, it also more accurately predicts the DNA- and RNA-binding proteins. Application on the human proteome confirms that DRNApred reduces the cross predictions among the native nucleic acid binders. Also, novel putative DNA/RNA-binding proteins that it predicts share similar subcellular locations and residue charge profiles with the known native binding proteins. Webserver of DRNApred is freely available at http://biomine.cs.vcu.edu/servers/DRNApred/.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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