Structural biology of the invasion arsenal of Gram‐negative bacterial pathogens
Bibliographic record
Abstract
In the last several years, there has been a tremendous progress in the understanding of host–pathogen interactions and the mechanisms by which bacterial pathogens modulate behavior of the host cell. Pathogens use secretion systems to inject a set of proteins, called effectors, into the cytosol of the host cell. These effectors are secreted in a highly regulated, temporal manner and interact with host proteins to modify a multitude of cellular processes. The number of effectors varies between pathogens from ~ 30 to as many as ~ 350. The functional redundancy of effectors encoded by each pathogen makes it difficult to determine the cellular effects or function of individual effectors, since their individual knockouts frequently produce no easily detectable phenotypes. Structural biology of effector proteins and their interactions with host proteins, in conjunction with cell biology approaches, has provided invaluable information about the cellular function of effectors and underlying molecular mechanisms of their modes of action. Many bacterial effectors are functionally equivalent to host proteins while being structurally divergent from them. Other effector proteins display new, previously unobserved functionalities. Here, we summarize the contribution of the structural characterization of effectors and effector–host protein complexes to our understanding of host subversion mechanisms used by the most commonly investigated Gram‐negative bacterial pathogens. We describe in some detail the enzymatic activities discovered among effector proteins and how they affect various cellular processes.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".