Reshaping of a Glycoside Hydrolase Active Site through Expression-Compensated Droplet-Based Microfluidic Screening Provides Useful Tools for Glycomics
Bibliographic record
Abstract
The glycosylation of proteins endows them with distinct biophysical properties and allows them to play fundamental roles in cellular communication. Much of our understanding of glycoproteins has derived from the ability to enzymatically manipulate glycan structures. In particular, selective cleavage of glycans from proteins simplifies the analysis of glycoproteins and the determination of structure-activity relationships. However, limited enzymatic tools are available for the study of mucin-type O-glycans. To address this, we carried out the directed evolution of a glycoside hydrolase to increase its ability to cleave the sialyl T-antigen, a ubiquitous O-glycan structure in humans. We employed ultrahigh-throughput droplet-based microfluidics to rapidly screen vast libraries of variants in pL-sized droplets, thus minimizing the quantities of complex substrate required. Furthermore, by use of fluorescent protein-fusion and ratiometric gating during droplet sorting we could account for varying expression levels and identify highly active hits that could have been overlooked due to lower expression levels. Within just two rounds of screening, we uncovered variants with 840-fold enhancements in activity and new specificities compared to those of the WT enzyme. This campaign highlights the versatility of glycoside hydrolases and provides a broadly applicable strategy to engineer enzymatic tools for glycomics through microfluidic screening.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".