Structural Analysis of Mammalian Sialic Acid Esterase
Bibliographic record
Abstract
• Sialic acid esterase removes acetyl groups from extracellular sialoglycans to regulate B cell receptor signalling. • We report the crystal structure of mammalian sialic acid esterase in complex with acetate. • Molecular dynamics simulations with O -acetylated sialoglycans, reveal that the active site is broadly specific. • Structural mapping of mutations linked to autoimmune diseases, reveals a possible binding interface with regulatory factors. Sialic acid esterase (SIAE) catalyzes the removal of O -acetyl groups from sialic acids found on cell surface glycoproteins to regulate cellular processes such as B cell receptor signalling and apoptosis. Loss-of-function mutations in SIAE are associated with several common autoimmune diseases including Crohn’s, ulcerative colitis, and arthritis. To gain a better understanding of the function and regulation of this protein, we determined crystal structures of SIAE from three mammalian homologs, including an acetate bound structure. The structures reveal that the catalytic domain adopts the fold of the SGNH hydrolase superfamily. The active site is composed of a catalytic dyad, as opposed to the previously reported catalytic triad. Attempts to determine a substrate-bound structure yielded only the hydrolyzed product acetate in the active site. Rigid docking of complete substrates followed by molecular dynamics simulations revealed that the active site does not form specific interactions with substrates, rather it appears to be broadly specific to accept sialoglycans with diverse modifications. Based on the acetate bound structure, a catalytic mechanism is proposed. Structural mapping of disease mutations reveals that most are located on the surface of the enzyme and would only cause minor disruptions to the protein fold, suggesting that these mutations likely affect binding to other factors. These results improve our understanding of SIAE biology and may aid in the development of therapies for autoimmune diseases and cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".