Target Identification by Chromatographic Co-elution: Monitoring of Drug-Protein Interactions without Immobilization or Chemical Derivatization
Bibliographic record
Abstract
Bioactive molecules typically mediate their biological effects through direct physical association with one or more cellular proteins. The detection of drug-target interactions is therefore essential for the characterization of compound mechanism of action and off-target effects, but generic label-free approaches for detecting binding events in biological mixtures have remained elusive. Here, we report a method termed target identification by chromatographic co-elution (TICC) for routinely monitoring the interaction of drugs with cellular proteins under nearly physiological conditions in vitro based on simple liquid chromatographic separations of cell-free lysates. Correlative proteomic analysis of drug-bound protein fractions by shotgun sequencing is then performed to identify candidate target(s). The method is highly reproducible, does not require immobilization or derivatization of drug or protein, and is applicable to diverse natural products and synthetic compounds. The capability of TICC to detect known drug-protein target physical interactions (K(d) range: micromolar to nanomolar) is demonstrated both qualitatively and quantitatively. We subsequently used TICC to uncover the sterol biosynthetic enzyme Erg6p as a novel putative anti-fungal target. Furthermore, TICC identified Asc1 and Dak1, a core 40 S ribosomal protein that represses gene expression, and dihydroxyacetone kinase involved in stress adaptation, respectively, as novel yeast targets of a dopamine receptor agonist.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".