Cloning and Expression of <i>afpA</i> , a Gene Encoding an Antifreeze Protein from the Arctic Plant Growth-Promoting Rhizobacterium <i>Pseudomonas putida</i> GR12-2
Bibliographic record
Abstract
The Arctic plant growth-promoting rhizobacterium Pseudomonas putida GR12-2 secretes an antifreeze protein (AFP) that promotes survival at subzero temperatures. The AFP is unusual in that it also exhibits a low level of ice nucleation activity. A DNA fragment with an open reading frame encoding 473 amino acids was cloned by PCR and inverse PCR using primers designed from partial amino acid sequences of the isolated AFP. The predicted gene product, AfpA, had a molecular mass of 47.3 kDa, a pI of 3.51, and no previously known function. Although AfpA is a secreted protein, it lacked an N-terminal signal peptide and was shown by sequence analysis to have two possible secretion systems: a hemolysin-like, calcium-binding secretion domain and a type V autotransporter domain found in gram-negative bacteria. Expression of afpA in Escherichia coli yielded an intracellular 72-kDa protein modified with both sugars and lipids that exhibited lower levels of antifreeze and ice nucleation activities than the native protein. The 164-kDa AFP previously purified from P. putida GR12-2 was a lipoglycoprotein, and the carbohydrate was required for ice nucleation activity. Therefore, the recombinant protein may not have been properly posttranslationally modified. The AfpA sequence was most similar to cell wall-associated proteins and less similar to ice nucleation proteins (INPs). Hydropathy plots revealed that the amino acid sequence of AfpA was more hydrophobic than those of the INPs in the domain that forms the ice template, thus suggesting that AFPs and INPs interact differently with ice. To our knowledge, this is the first gene encoding a protein with both antifreeze and ice nucleation activities to be isolated and characterized.
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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) | 0.001 | 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) | 0.000 | 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".