Two-component systems interface discrimination in Actinobacillus pleuropneumoniae
Bibliographic record
Abstract
Abstract Background Pathogenic bacteria grow in different environments and have developed signaling systems known as two-component systems that allow them to thrive in distinct habitats efficiently. Actinobacillus pleuropneumoniae is an obligate pig pathogen that colonizes its host and survives outside it by forming biofilms. The small number of the two-component systems in this pathogen makes it a suitable model to assess the interaction specificity of these systems. Results This was done through multiple sequence alignments, mutual information, heterodimer modeling, structural data, molecular dynamics, and the interface coupling index, which were used to evaluate molecular recognition. For the study, more than two thousand homologue sequences were collected from a diverse range of bacteria. Four different clusters of specificity-determining residues were found for all evaluated systems. The system-wide discrimination capability of twocomponent systems relies on the composition of these clusters composed of 3, 3, 4, and 5 residue pairs, from systems CpxAR, NarQP, PhoRB, and QseCB, respectively. These residue pairs are spatially nearby, the shape and composition of each cluster are systemspecific and have minimal overlap among them. Conclusions The interaction interface composition of the twocomponent systems network in A. pleuropneumoniae was defined and their discriminatory components were described. In summary, molecular recognition depends on specific components from the interaction interface called orthologue interaction specificity clusters. These clusters enable the emergence of specificity, which allows the system to differentiate between cognate and non-cognate components, thereby enabling the system to recognize highly similar components through molecular recognition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".