Adhesin domains responsible for binding bacteria to surfaces they colonize project outwards from companion split domains
Bibliographic record
Abstract
Bacterial adhesins attach their hosts to surfaces that the bacteria will colonize. This surface adhesion occurs through specific ligand-binding domains located towards the distal end of the long adhesin molecules. However, recognizing which of the many adhesin domains are structural and which are ligand binding has been difficult up to now. Here we have used the protein structure modeling program AlphaFold2 to predict structures for these giant 0.2- to 1.5-megadalton proteins. Crystal structures previously solved for several adhesin regions are in good agreement with the models. Whereas most adhesin domains are linked in a linear fashion through their N- and C-terminal ends, ligand-binding domains can be recognized by budding out from a companion core domain so that their ligand-binding sites are projected away from the axis of the adhesin for maximal exposure to their targets. These companion domains are "split" in their continuity by projecting the ligand-binding domain outwards. The "split domains" are mostly β-sandwich extender modules, but other domains like a β-solenoid can serve the same function. Bioinformatic analyses of Gram-negative bacterial sequences revealed wide variety ligand-binding domains are used in their Repeats-in-Toxin adhesins. The ligands for many of these domains have yet to be identified but known ligands include various cell-surface glycans, proteins, and even ice. Recognizing the ligands to which the adhesins bind could lead to ways of blocking colonization by bacterial pathogens. Engineering different ligand-binding domains into an adhesin has the potential to change the surfaces to which bacteria bind.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".