Fusion Proteins from Artificial and Natural Structural Modules
Bibliographic record
Abstract
The purpose of preparing fusion proteins from designed and natural sequences is mainly twofold; it aims at the stabilization of structure and at the modification of biological activity. Fusion with b-galactosidase, for example, can increase the intracellular stability and DDT-degrading activity of an artificial DDT-binding peptide, and fusions with a leucine zipper produce mono- and bifunctional single-chain variable domain antibody fragments or homodimeric and heterodimeric DNA-binding proteins like an artificial homodimeric HIV-1 enhancer-binding protein with increased binding specificity and repressor activity. Of importance are also short leader sequences that mediate the translocation of proteins across the cytoplasmic and the nuclear membrane. An interesting by-product of the leucine zipper-mediated dimerization of an HIV-1 enhancer-binding protein was the synthesis and the structural as well as functional characterization of a retro-leucine zipper. Keywords: DNA-binding proteins, retro-leucine zipper, topoisomerase analogue, Dimeric and Tetrameric Fv Fragments, Affinity Chromatography, Myoglobin F-Helix, HIV-1 enhancer-containing plasmids, COOH-terminal peptides, heterooligomerization, Tetramerization Module
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.004 |
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".