Identification of ice-binding peptide sequences from genetically-encoded phage libraries
Why this work is in the frame
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Bibliographic record
Abstract
Ice-binding peptides are small molecules capable of exerting control over ice nucleation. They are of interest in in areas ranging from medically important problems such as cryosurgery on tumours and preservation of transplant organs, to more concrete everyday applications such as snowmaking or de-icing of roads. In this project, we aim to identify glycopeptides capable of inducing ice nucleation. To our knowledge, no ice-nucleating peptides have yet been identified. Taking inspiration from an approach used in the literature for the purification of antifreeze proteins from a mixed solution, we developed a phage-display technique allowing for the selection of ice-binding peptides from a naive library. In this method, a test tube chilled to -25°C is placed into a phage-containing solution. Ice builds around the test tube, integrating phage that display ice-binding peptides as it grows. Through multiple freezing rounds, we are able to narrow the selected population from 10^12 pfu to 10^2 pfu in five rounds. We present preliminary selection results from the SXCX3C library. Through these results, we wish to demonstrate a selection method applicable in a challenging system where the target (ice) must be able to be distinguished from the surrounding liquid. In addition, we have constructed a freezing platform capable of validating ice-nucleating properties, and model the process with gold slides coated with alkanethiol SAMs (self-assembled monolayers). Our next steps will be to synthesize and evaluate the ice-nucleating properties of peptide hits to identify the peptides that exhibit a statistically significant influence on ice nucleation temperature. * Indicates faculty mentor.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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