Structural Basis for Substrate Specificity of Mammalian Neuraminidases
Bibliographic record
Abstract
The removal of sialic acid (Sia) residues from glycoconjugates in vertebrates is mediated by a family of neuraminidases (sialidases) consisting of Neu1, Neu2, Neu3 and Neu4 enzymes. The enzymes play distinct physiological roles, but their ability to discriminate between the types of linkages connecting Sia and adjacent residues and between the identity and arrangement of the underlying sugars has never been systematically studied. Here we analyzed the specificity of neuraminidases by studying the kinetics of hydrolysis of BODIPY-labeled substrates containing common mammalian sialylated oligosaccharides: 3'Sia-LacNAc, 3'SiaLac, SiaLex, SiaLea, SiaLec, 6'SiaLac, and 6'SiaLacNAc. We found significant differences in substrate specificity of the enzymes towards the substrates containing α2,6-linked Sia, which were readily cleaved by Neu3 and Neu1 but not by Neu4 and Neu2. The presence of a branching 2-Fuc inhibited Neu2 and Neu4, but had almost no effect on Neu1 or Neu3. The nature of the sugar residue at the reducing end, either glucose (Glc) or N-acetyl-D-glucosamine (GlcNAc) had only a minor effect on all neuraminidases, whereas core structure (1,3 or 1,4 bond between D-galactose (Gal) and GlcNAc) was found to be important for Neu4 strongly preferring β3 (core 1) to β4 (core 2) isomer. Neu3 and Neu4 were in general more active than Neu1 and Neu2, likely due to their preference for hydrophobic substrates. Neu2 and Neu3 were examined by molecular dynamics to identify favorable substrate orientations in the binding sites and interpret the differences in their specificities. Finally, using knockout mouse models, we confirmed that the substrate specificities observed in vitro were recapitulated in enzymes found in mouse brain tissues. Our data for the first time provide evidence for the characteristic substrate preferences of neuraminidases and their ability to discriminate between distinct sialoside targets.
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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) | 0.001 | 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".