Structure–function analyses generate novel specificities to assemble the components of multienzyme bacterial cellulosome complexes
Bibliographic record
Abstract
The cellulosome is a remarkably intricate multienzyme nanomachine produced by anaerobic bacteria to degrade plant cell wall polysaccharides. Cellulosome assembly is mediated through binding of enzyme-borne dockerin modules to cohesin modules of the primary scaffoldin subunit. The anaerobic bacterium Acetivibrio cellulolyticus produces a highly intricate cellulosome comprising an adaptor scaffoldin, ScaB, whose cohesins interact with the dockerin of the primary scaffoldin (ScaA) that integrates the cellulosomal enzymes. The ScaB dockerin selectively binds to cohesin modules in ScaC that anchors the cellulosome onto the cell surface. Correct cellulosome assembly requires distinct specificities displayed by structurally related type-I cohesin–dockerin pairs that mediate ScaC–ScaB and ScaA–enzyme assemblies. To explore the mechanism by which these two critical protein interactions display their required specificities, we determined the crystal structure of the dockerin of a cellulosomal enzyme in complex with a ScaA cohesin. The data revealed that the enzyme-borne dockerin binds to the ScaA cohesin in two orientations, indicating two identical cohesin-binding sites. Combined mutagenesis experiments served to identify amino acid residues that modulate type-I cohesin–dockerin specificity in A. cellulolyticus . Rational design was used to test the hypothesis that the ligand-binding surfaces of ScaA- and ScaB-associated dockerins mediate cohesin recognition, independent of the structural scaffold. Novel specificities could thus be engineered into one, but not both, of the ligand-binding sites of ScaB, whereas attempts at manipulating the specificity of the enzyme-associated dockerin were unsuccessful. These data indicate that dockerin specificity requires critical interplay between the ligand-binding surface and the structural scaffold of these modules.
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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 | 0.001 |
| 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".