Transcription factors across the <i>Escherichia coli</i> pangenome: a 3D perspective
Bibliographic record
Abstract
Abstract Motivation Identification of complete sets of transcription factors (TFs) is a foundational step in the inference of genetic regulatory networks. With the availability of high-quality predictions of protein three-dimensional structures (3D), it has become possible to use structural comparisons for the inference of homology beyond what is possible from sequence analyses alone. This work explores the potential to use predicted 3D structures for the identification of TFs in the Escherichia coli pangenome. Results Comparisons between predicted structures and their experimentally confirmed counterparts confirmed the high-quality of predicted structures, with most 3D structural alignments showing TM-scores well above established structural similarity thresholds, though the quality seemed slightly lower for TFs than for other proteins. As expected, structural similarity decreased with sequence similarity, though most TM-scores still remained above the structural similarity threshold. This was true regardless of the aligned structures being experimental or predicted. Results at the lowest sequence identity levels revealed potential for 3D structural comparisons to extend homology inferences below the “twilight zone” of sequence-based methods. The body of predicted 3D structures covered 99.7% of available proteins from the E. coli pangenome, missing only two of those matching TF domain sequence profiles. Structural analyses increased the inferred TFs in the E. coli pangenome by 18% above the amount obtained with sequence profiles alone.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".