Building the Environmental Chemical-Protein Interaction Network (eCPIN): An Exposome-Wide Strategy for Bioactive Chemical Contaminant Identification
Bibliographic record
Abstract
Although advancements in nontargeted analysis have made it possible to detect hundreds of chemical contaminants in a single run, the current environmental toxicology approaches lag behind, precluding the transition from analytical chemistry efforts to health risk assessment. We herein highlighted a recently developed "top-down" bioanalytical method, protein Affinity Purification with Nontargeted Analysis (APNA), to screen for bioactive chemical contaminants at the "exposome-wide" level. To achieve this, a tagged functional protein is employed as a "bait" to directly isolate bioactive chemical contaminants from environmental mixtures, which are further identified by nontargeted analysis. Advantages of this protein-guided approach, including the discovery of new bioactive ligands as well as new protein targets for known chemical contaminants, were highlighted by several case studies. Encouraged by these successful applications, we further proposed a framework, i.e., the environmental Chemical-Protein Interaction Network (eCPIN), to construct a complete map of the 7 billion binary interactions between all chemical contaminants (>350,000) and human proteins (∼20,000) via APNA. The eCPIN could be established in three stages through strategically prioritizing the ∼20,000 human proteins, such as focusing on the 48 nuclear receptors (e.g., thyroid hormone receptors) in the first stage. The eCPIN will provide an unprecedented throughput for screening bioactive chemical contaminants at the exposome-wide level and facilitate the identification of molecular initiating events at the proteome-wide level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".