Bibliographic record
Abstract
We hypothesize that we can exploit what Nature has already evolved by manipulating the alpha-helix molecular recognition scaffold. Therefore, minimalist proteins capable of sequence-specific, high-affinity binding of DNA were generated to probe how proteins are used and can be used to recognize DNA. The already minimal basic region/leucine zipper motif (bZIP) of GCN4 was reduced to an even more simplified structure by substitution with alanine residues-hence, a generic, Ala-based, helical scaffold. The proteins generated, wt bZIP, 4A, 11A, and 18A, contain 0, 4, 11, and 18 alanine mutations in their DNA-binding basic regions, respectively. All alanine mutants still retain alpha-helical structure and DNA-binding function, despite loss of virtually all Coulombic protein-DNA interactions. Mass spectrometry allowed characterization of proteins and post-translational modifications. Fluorescence anisotropy and DNase I footprinting were used to measure in situ binding of these mutant proteins to DNA duplexes containing target sites AP-1 (5'-TGACTCA-3'), ATF/CREB (5'-TGACGTCA-3'), or nonspecific DNA. The roles of van der Waals and Coulombic interactions toward binding specificity and affinity are being investigated. Thus, both DNA-binding specificity and affinity are maintained in all our bZIP derivatives. This Ala-rich scaffold may be useful in design and synthesis of small, alpha-helical proteins with desired DNA-recognition properties.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".