Emerging enzyme surface display systems for waste resource recovery
Bibliographic record
Abstract
The current century marks an inflection point for human progress, as the developed world increasingly comes to recognize that the ecological and socioeconomic impacts of resource extraction must be balanced with more sustainable modes of growth that are less reliant on non-renewable sources of energy and materials. This has opened a window of opportunity for cross-sector development of biotechnologies that harness the metabolic problem-solving power of microbial communities. In this context, recovery has emerged as an organizing principal to create value from industrial and municipal waste streams, and the search is on for new enzymes and platforms that can be used for waste resource recovery at scale. Enzyme surface display on cells or functionalized materials has emerged as a promising platform for waste valorization. Typically, surface display involves the use of substrate binding or catalytic domains of interest translationally fused with extracellular membrane proteins in a microbial chassis. Novel display systems with improved performance features include S-layer display with increased protein density, spore display with increased resistance to harsh conditions, and intracellular inclusions including DNA-free cells or nanoparticles with improved social licence for in situ applications. Combining these display systems with advances in bioprinting, electrospinning and high-throughput functional screening have potential to transform outmoded extractive paradigms into 'trans-metabolic" processes for remediation and waste resource recovery within an emerging circular bioeconomy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".