Direct measurement of the kinetics of CBM9 fusion‐tag bioprocessing using luminescence resonance energy transfer
Bibliographic record
Abstract
The economics of affinity-tagging technologies, particularly at preparative scales, depends in part on the cost and efficiency of the bioprocessing step used to remove the affinity tag and obtain the final purified product (Lowe et al., J Biochem Biophys Methods. 2001;49:561-574). When CBM9, the family 9 cellulose binding module from Thermotoga maritima, serves as the affinity tag, the overall efficiency of tag removal is a function of the choice of processing enzyme and the local structure of the cleavage site, most notably the linker sequence flanking the bioprocessing recognition site on the tag side. A novel spectroscopic method is reported and used to rapidly and accurately measure CBM9 fusion-tag bioprocessing kinetics and their dependence on the choice of linker sequence. The assay monitors energy transfer between a lanthanide-based donor bound to the CBM9 tag and an acceptor fluorophore presented on the target protein or peptide. Enzyme-catalyzed cleavage of the fusion tag terminates this resonance energy transfer, resulting in a change in fluorescence intensity that can be monitored to quantify substrate concentration over time. The assay is simple, fast and accurate, providing k(cat)/K(M) values that contain standard errors of less than 3%. As a result, both substantial and subtle differences in bioprocessing kinetics can be measured and used to guide bioproduct design.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".