BromoCatch: a self-labelling tag platform for protein analysis and live cell imaging
Bibliographic record
Abstract
Abstract Visualizing and manipulating proteins in live cells is crucial for studying complex biological processes. Self-labelling protein (SLP) tags such as HaloTag and SNAP-tag can be fused to genes of interest to allow protein labelling in cells. Limitations including size of the tag and suboptimal fitness of reactivity motivate development of improved tools to enable rapid, specific and stable protein labelling. We present BromoCatch, a novel SLP platform based on a small ∼13 kDa bromodomain (BD) engineered with a nucleophilic cysteine for covalent ligand engagement. A structure-based designed library of 16 “bumped” binders bearing diverse electrophilic warheads was screened against two different cysteine mutants using differential scanning fluorimetry and intact protein mass spectrometry to monitor covalent complex formation. The para-acrylamide bumped derivative MR116 and the Brd4-BD2 double mutant L387A,E438C formed the most potent and stable adduct, and its binding mode through covalent modification was confirmed by an X-ray cocrystal structure solved to 1.3 Å of resolution. BromoCatch exhibited potent and irreversible target engagement in cells through nanoBRET and residence time assays. Practicality and scope are further demonstrated through the design and proof-of-concept application of a biotinylated conjugate, PROTAC tag degraders, and fluorescent probes of both full-on and switch-on types for ex-cellulo and live-cell imaging. Together, we qualify BromoCatch as a novel, versatile and efficient protein labelling tool and technology platform. Its advantageous design features and kinetic fitness, and its modular design enabling diverse functionalities, are anticipated to usher a range of future applications and witness broad utility.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".