Diversification of molecular pattern recognition in bacterial NLR-like proteins
Bibliographic record
Abstract
Antiviral STANDs (Avs) are bacterial anti-phage proteins evolutionarily related to immune pattern recognition receptors of the NLR family. Type 2 Avs proteins (Avs2) were suggested to recognize the phage large terminase subunit as a signature of phage infection. Here, we show that Avs2 from Klebsiella pneumoniae (KpAvs2) can recognize several different phage proteins as signature for infection. While KpAvs2 recognizes the large terminase subunit of Seuratvirus phages, we find that to protect against Dhillonvirus phages, KpAvs2 recognizes a different phage protein named KpAvs2-stimulating protein 1 (Ksap1). KpAvs2 directly binds Ksap1 to become activated, and phages mutated in Ksap1 escape KpAvs2 defense despite encoding an intact terminase. We further show that KpAvs2 protects against a third group of phages by recognizing another protein, Ksap2. Our results exemplify the evolutionary diversification of molecular pattern recognition in bacterial Avs2, and show that a single pattern recognition receptor evolved to recognize different phage-encoded proteins. Avs proteins are bacterial anti-phage pattern recognition receptors evolutionarily related to eukaryotic NLRs. Here, Béchon et al show that a single bacterial Avs can recognize different phage proteins as a signature for infection, explaining the broad defensive range of Avs proteins.
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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) | 0.000 | 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 | 0.000 |
| 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 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".