A Multiplexing Activity-Based Protein-Profiling Platform for Dissection of a Native Bacterial Xyloglucan-Degrading System
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Bacteria and yeasts grow on biomass polysaccharides by expressing and excreting a complex array of glycoside hydrolase (GH) enzymes. Identification and annotation of such GH pools, which are valuable commodities for sustainable energy and chemistries, by conventional means (genomics, proteomics) are complicated, as primary sequence or secondary structure alignment with known active enzymes is not always predictive for new ones. Here we report a “low-tech”, easy-to-use, and sensitive multiplexing activity-based protein-profiling platform to characterize the xyloglucan-degrading GH system excreted by the soil saprophyte, Cellvibrio japonicus, when grown on xyloglucan. A suite of activity-based probes bearing orthogonal fluorophores allows for the visualization of accessory exo -acting glycosidases, which are then identified using biotin-bearing probes. Substrate specificity of xyloglucanases is directly revealed by imbuing xyloglucan structural elements into bespoke activity-based probes. Our ABPP platform provides a highly useful tool to dissect xyloglucan-degrading systems from various sources and to rapidly select potentially useful ones. The observed specificity of the probes moreover bodes well for the study of other biomass polysaccharide-degrading systems, by modeling probe structures to those of desired substrates.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".