Bioinformatic mapping of a more precise Aspergillus niger degradome
Bibliographic record
Abstract
Aspergillus niger has the ability to produce a large variety of proteases, which are of particular importance for protein digestion, intracellular protein turnover, cell signaling, flavour development, extracellular matrix remodeling and microbial defense. However, the A. niger degradome (the full repertoire of peptidases encoded by the A. niger genome) available is not accurate and comprehensive. Herein, we have utilized annotations of A. niger proteases in AspGD, JGI, and version 12.2 MEROPS database to compile an index of at least 232 putative proteases that are distributed into the 71 families/subfamilies and 26 clans of the 6 known catalytic classes, which represents ~ 1.64% of the 14,165 putative A. niger protein content. The composition of the A. niger degradome comprises ~ 7.3% aspartic, ~ 2.2% glutamic, ~ 6.0% threonine, ~ 17.7% cysteine, ~ 31.0% serine, and ~ 35.8% metallopeptidases. One hundred and two proteases have been reassigned into the above six classes, while the active sites and/or metal-binding residues of 110 proteases were recharacterized. The probable physiological functions and active site architectures of these peptidases were also investigated. This work provides a more precise overview of the complete degradome of A. niger, which will no doubt constitute a valuable resource and starting point for further experimental studies on the biochemical characterization and physiological roles of these proteases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".