Enhanced activity of split trehalase biosensors by interspecies domain combineering
Bibliographic record
Abstract
The split trehalase biosensor has potential as a versatile diagnostic technology. Split enzymes are engineered proteins, divided into inactive fragments, which can reassemble and regain activity when brought together by an analyte. The split TreA biosensor requires no sample processing and produces stable signals (in the form of glucose). Split trehalase reagents can function in blood, but periplasmic trehalase of E. coli requires blood acidification for maximal activity. The objective of this study was to obtain split trehalase with near physiological pH optimum. For this purpose, periplasmic trehalases of Cellvibrio spp. with higher activity at neutral pH, were split in analogy with the E. coli TreA into hood and catalytic domains. However, these split trehalases displayed self-complementation due to spontaneous reassembly. In contrast, when catalytic domains of Cellvibrio trehalases were combined with E. coli hood domains, these hybrids displayed conditional complementation capacity when split trehalase fragments fused to immunoglobulin-binding protein G (STIGA) were used to quantify immunoglobulin concentrations. Other hybrid combinations of Cellvibrio spp. had increased activity compared to the cognate pairs, albeit with strong self-complementation. A mutagenesis analysis of residues responsible for self-complementation led to uncoupling of self-complementation from allostery. The Michaelis-Menten kinetics of Cellvibrio enzymes and fragment pairs confirmed improved activity of a mutated hybrid pair of Cellvibrio hood and catalytic domains at physiological pH. In conclusion, the improvements in pH optimum and activity, demonstrated with STIGA, can now be leveraged to enhance other variations of the split trehalase biosensor platform, broadening its utility for testing clinical samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".