<i>Legionella</i> effector LegA15/AnkH contains an unrecognized cysteine protease-like domain and displays structural similarity to LegA3/AnkD, but differs in host cell localization
Bibliographic record
Abstract
Legionella pneumophila is a human pathogen that causes Legionnaires' disease, a severe form of pneumonia. It can be found in various aquatic environments ranging from cooling towers to ponds. In addition to causing disease in humans, it can also infect free-living amoebae commonly found in various aquatic environments. Once inside a human lung macrophage, it creates a niche called the Legionella-containing vacuole where it can evade phagolysosomal degradation and replicate. During infection, normal cellular functions are hijacked by proteins that are secreted by the pathogen, called bacterial effectors. Here, the structural characterization of the effector LegA15/AnkD is reported. The protein contains an ankyrin-repeat domain followed by a cysteine protease-like (CPL) domain with a putative catalytic triad consisting of His268-Asn290-Cys361. The CPL domain shows similarity to the CE clan in the MEROPS database, which contains ubiquitin-like hydrolases. The C-terminal segment of LegA15, including the CPL domain, shows structural similarity to another effector, LegA3/AnkH, while they share only 12% sequence identity. When expressed in mammalian cells, LegA15 is localized within the cytoplasm, in contrast to LegA3, which localizes to the nucleus.
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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 | 0.001 |
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".