Electronically tuned N-terminal glycine selective bioconjugation enables in vitro and in vivo species-specific Staphylococcus aureus targeting
Bibliographic record
Abstract
Precisely targeting a biomolecule within living systems could create unique opportunities at the biology-medicine interface. Such methods have been non-trivial due to the complex selectivity attributes. Here, we introduce an electronically tunable chemical technology that targets N-terminal glycine, a unique low-frequency molecular signature. Initially, we established the method using peptides and proteins, along with DFT-led mechanistic insights. The reactivity, selectivity, and bioconjugate stability remain unaffected by diverse bioadditives. These unique attributes enabled the selective targeting of the Gly5-based molecular signature in Staphylococcus aureus, a leading contributor to bacterial infection-associated mortality. Notably, the method facilitates species-specific detection of S. aureus and its resistant strains, MRSA and VRSA. It enables late-stage bond-engineering to install multiple biorthogonal handles, downstream chemistry, and chemically orthogonal reversal. The specificity extends to in vivo labeling of S. aureus within Drosophila melanogaster, as well as skin, tissue, and blood infections in mice. The N-Gly modification inhibits drug-susceptible and drug-resistant clinical strains of S. aureus, including biofilm formation and preformed biofilms. Finally, the in vivo efficiency was established using a wound-healing assay in mice. The exclusive N-Gly residue-specific targeting across the molecular complexity spectrum, both in vitro and in vivo, establishes peptidoglycan as a potential diagnostic and therapeutic target.
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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) | 0.000 | 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) | 0.001 | 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 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".