Distribution and Evolution of Yersinia Leucine-Rich Repeat Proteins
Bibliographic record
Abstract
Leucine-rich repeat (LRR) proteins are widely distributed in bacteria, playing important roles in various protein-protein interaction processes. In Yersinia, the well-characterized type III secreted effector YopM also belongs to the LRR protein family and is encoded by virulence plasmids. However, little has been known about other LRR members encoded by Yersinia genomes or their evolution. In this study, the Yersinia LRR proteins were comprehensively screened, categorized, and compared. The LRR proteins encoded by chromosomes (LRR1 proteins) appeared to be more similar to each other and different from those encoded by plasmids (LRR2 proteins) with regard to repeat-unit length, amino acid composition profile, and gene expression regulation circuits. LRR1 proteins were also different from LRR2 proteins in that the LRR1 proteins contained an E3 ligase domain (NEL domain) in the C-terminal region or an NEL domain-encoding nucleotide relic in flanking genomic sequences. The LRR1 protein-encoding genes (LRR1 genes) varied dramatically and were categorized into 4 subgroups (a to d), with the LRR1a to -c genes evolving from the same ancestor and LRR1d genes evolving from another ancestor. The consensus and ancestor repeat-unit sequences were inferred for different LRR1 protein subgroups by use of a maximum parsimony modeling strategy. Structural modeling disclosed very similar repeat-unit structures between LRR1 and LRR2 proteins despite the different unit lengths and amino acid compositions. Structural constraints may serve as the driving force to explain the observed mutations in the LRR regions. This study suggests that there may be functional variation and lays the foundation for future experiments investigating the functions of the chromosomally encoded LRR proteins of Yersinia.
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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 | 0.002 | 0.001 |
| 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".