Strategy for selecting and characterizing linker peptides for CBM9‐tagged fusion proteins expressed in <i>Escherichia coli</i>
Bibliographic record
Abstract
The influence of linker design on fusion protein production and performance was evaluated when a family 9 carbohydrate-binding module (CBM9) serves as the affinity tag for recombinant proteins expressed in Escherichia coli. Two bioinformatic strategies for linker design were applied: the first identifies naturally occurring linkers within the proteome of the host organism, the second involves screening peptidases and their known specificities using the bioinformatics software MEROPS to design an artificial linker resistant to proteolysis within the host. Linkers designed using these strategies were compared against traditional poly-glycine linkers. Although widely used, glycine-rich linkers were found by tandem MS data to be susceptible to hydrolysis by E. coli peptidases. The natural (PT)(x)P and MEROPS-designed S(3)N(10) linkers were significantly more stable, indicating both strategies provide a useful approach to linker design. Factor X(a) processing of the fusion proteins depended strongly on linker chemistry, with poly(G) and S(3)N(10) linkers showing the fastest cleavage rates. Luminescence resonance energy transfer studies, used to measure average distance of separation between GFP and Tb(III) bound to a strong calcium-binding site of CBM9, revealed that, for a given linker chemistry, the separation distance increases with increasing linker length. This increase was particularly large for poly(G) linkers, suggesting that this linker chemistry adopts a hydrated, extended configuration that makes it particularly susceptible to proteolysis. Differential scanning calorimetry studies on the PT linker series showed that fusion of CBM9 to GFP did not alter the T(m) of GFP but did result in a destabilization, as seen by both a decrease in T(m) and DeltaH(cal), of CBM9. The degree of destabilization increased with decreasing length of the (PT)(x)P linker such that DeltaT(m) = -8.4 degrees C for the single P linker.
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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".