Structural Profiling of Bacterial Effectors Reveals Enrichment of Host-Interacting Domains and Motifs
Bibliographic record
Abstract
Effector proteins are bacterial virulence factors secreted directly into host cells and, through extensive interactions with host proteins, rewire host signaling pathways to the advantage of the pathogen. Despite the crucial role of globular domains as mediators of protein-protein interactions (PPIs), previous structural studies of bacterial effectors are primarily focused on individual domains, rather than domain-mediated PPIs, which limits their ability to uncover systems-level molecular recognition principles governing host-bacteria interactions. Here, we took an interaction-centric approach and systematically examined the potential of structural components within bacterial proteins to engage in or target eukaryote-specific domain-domain interactions (DDIs). Our results indicate that: 1) effectors are about six times as likely as non-effectors to contain host-like domains that mediate DDIs exclusively in eukaryotes; 2) the average domain in effectors is about seven times as likely as that in non-effectors to co-occur with DDI partners in eukaryotes rather than in bacteria; and 3) effectors are about nine times as likely as non-effectors to contain bacteria-exclusive domains that target host domains mediating DDIs exclusively in eukaryotes. Moreover, in the absence of host-like domains or among pathogen proteins without domain assignment, effectors harbor a higher variety and density of short linear motifs targeting host domains that mediate DDIs exclusively in eukaryotes. Our study lends novel quantitative insight into the structural basis of effector-induced perturbation of host-endogenous PPIs and may aid in the design of selective inhibitors of host-pathogen interactions.
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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) | 0.000 | 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 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".