Loosenin-like proteins from <i>Phanerochaete carnosa</i> impact both cellulose and chitin fiber networks
Bibliographic record
Abstract
Abstract Microbial expansin-related proteins are ubiquitous across bacterial and fungal organisms, and reportedly play a role in the modification and deconstruction of cell wall polysaccharides including lignocellulose. So far, very few microbial expansin related proteins, including loosenins and loosenin-like (LOOL) proteins, have been functionally characterized. Herein, four LOOLs encoded by Phanerochaete carnosa and belonging to different subfamilies (i.e., PcaLOOL7 and PcaLOOL9 from subfamily A; PcaLOOL2 and PcaLOOL12 from subfamily B) were recombinantly produced and the purified proteins were characterized using diverse cellulose and chitin substrates. Whereas all of the purified PcaLOOLs weakened cellulose filter paper and cellulose nanofibril networks (CNF), none significantly boosted cellulase activity on the selected cellulose substrates (Avicel and Whatman paper). Binding of PcaLOOLs to alpha-chitin was higher than to cellulose (Avicel), and highest at pH 5.0. Notably, whereas PcaLOOL9 reduced the yield strain of chitin nanofibrils (ChNF) in a protein-dose dependent manner, the reverse pattern was observed for PcaLOOL7 despite belonging to the same LOOL subfamily. The current study reveals the potential of microbial expansin-related proteins to impact both cellulose and chitin networks, and provides further evidence pointing to a non-lytic mode of action.
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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) | 0.000 | 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) | 0.001 | 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 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".