HinZip: Combining Hin Recombinase and FosW to Mimic HD-Zip Plant Proteins
Bibliographic record
Abstract
Small customized proteins that bind specific DNA sequences in a genome could serve as powerful tools for synthetic biology and therapeutic applications. These proteins could regulate gene circuits or act as precision-targeted inhibitors in disease networks. Here, we designed HinZip, a protein engineered to bind a unique 24+ base-pair DNA sequence with high affinity and specificity, thereby minimizing off-target effects. HinZip is inspired by the HD-Zip (homeodomain-leucine zipper) transcription factor family, which exists only in plants. No high-resolution structures exist for HD-Zip: genome-wide analyses indicate that HD-Zips use a homeodomain to bind DNA and a leucine zipper for dimerization. To emulate this functionality, we fused the Hin recombinase DNA-binding domain with the FosW leucine zipper. Electrophoretic mobility shift assays confirmed HinZip’s cooperative binding to a 29 base-pair inverted HixC palindrome ( K d = 17 nM), with no detectable binding to nonspecific DNA at protein concentrations up to 2 μM. Circular dichroism and dynamic light scattering further support dimer formation. Additionally, the bacterial one-hybrid assay demonstrated HinZip’s sequence-specific binding in cellulo. Even in the absence of structural guidance, we successfully designed a functional "frankenprotein" by integrating unrelated protein modules. This work underscores the feasibility of engineering bespoke DNA-binding proteins for targeted genomic interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".