Antibody-Free Reading of the Histone Code Using a Simple Chemical Sensor Array
Bibliographic record
Abstract
The histone code refers to the complex network of histone post-translational modifications that control gene expression and are of high interest as drivers of a large number of human diseases. We report here on a mix-and-match toolkit of readily available dyes and calixarene host molecules that can be combined to form dye-displacement sensors that respond to a wide variety of cationic peptides. Using the data from only two or three such simple supramolecular sensors as a chemical sensor array produces fingerprints of data that discriminate robustly among many kinds of histone code elements. "Reads" that are accomplished include the discrimination of unmethylated, mono-, di-, and trimethylated lysines on a single histone tail sequence, identification of different modifications and combinations of modifications on a single histone tail sequence, identification of a single modification type in several different sequence contexts, and identification of isomeric dimethylarginine modifications. Reads that are sometimes troublesome for antibodies are achieved. We also report on the ability of the sensor array to report simultaneously on the concentrations and identities of histone modifications. This sensor array discriminates between post-translationally modified analytes without being limited to partners that contain a single, programmed binding interaction.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".