Peptide backbone circularization enhances antifreeze protein thermostability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Antifreeze proteins (AFPs) are a class of ice-binding proteins that promote survival of a variety of cold-adapted organisms by decreasing the freezing temperature of bodily fluids. A growing number of biomedical, agricultural, and commercial products, such as organs, foods, and industrial fluids, have benefited from the ability of AFPs to control ice crystal growth and prevent ice recrystallization at subzero temperatures. One limitation of AFP use in these latter contexts is their tendency to denature and irreversibly lose activity at the elevated temperatures of certain industrial processing or large-scale AFP production. Using the small, thermolabile type III AFP as a model system, we demonstrate that AFP thermostability is dramatically enhanced via split intein-mediated N- and C-terminal end ligation. To engineer this circular protein, computational modeling and molecular dynamics simulations were applied to identify an extein sequence that would fill the 20-Å gap separating the free ends of the AFP, yet impose little impact on the structure and entropic properties of its ice-binding surface. The top candidate was then expressed in bacteria, and the circularized protein was isolated from the intein domains by ice-affinity purification. This circularized AFP induced bipyramidal ice crystals during ice growth in the hysteresis gap and retained 40% of this activity even after incubation at 100°C for 30 min. NMR analysis implicated enhanced thermostability or refolding capacity of this protein compared to the noncyclized wild-type AFP. These studies support protein backbone circularization as a means to expand the thermostability and practical applications of AFPs.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it