PACER: a novel 3D plant cell wall model for the analysis of non-catalytic and enzymatic responses
Bibliographic record
Abstract
Abstract Background Substrate accessibility remains a key limitation to the efficient enzymatic deconstruction of lignocellulosic biomass. Limited substrate accessibility is often addressed by increasing enzyme loading, which increases process and product costs. Alternatively, considerable efforts are underway world-wide to identify amorphogenesis-inducing proteins and protein domains that increase the accessibility of carbohydrate-active enzymes to targeted lignocellulose components. Results We established a three-dimensional assay, PACER (plant cell wall model for the analysis of non-catalytic and enzymatic responses), that enables analysis of enzyme migration through defined lignocellulose composites. A cellulose/azo-xylan composite was made to demonstrate the PACER concept and then used to test the migration and activity of multiple xylanolytic enzymes. In addition to non-catalytic domains of xylanases, the potential of loosenin-like proteins to boost xylanase migration through cellulose/azo-xylan composites was observed. Conclusions The PACER assay is inexpensive and parallelizable, suitable for screening proteins for ability to increase enzyme accessibility to lignocellulose substrates. Using the PACER assay, we visualized the impact of xylan-binding modules and loosenin-like proteins on xylanase mobility and access to targeted substrates. Given the flexibility to use different composite materials, the PACER assay presents a versatile platform to study impacts of lignocellulose components on enzyme access to targeted substrates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".