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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".