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Record W4377088110 · doi:10.1126/sciadv.adf8169

Structure of the immunoregulatory sialidase NEU1

2023· article· en· W4377088110 on OpenAlexafffund
A. Gorelik, Katalin Illes, Mohammad T. Mazhab‐Jafari, Bhushan Nagar

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health NetworkMcGill University
FundersArgonne National LaboratoryNational Institute of General Medical SciencesCanadian Institutes of Health ResearchOffice of ScienceNational Institutes of HealthU.S. Department of Energy
KeywordsSialidaseNeuraminidaseCathepsin AGlycanEnzymeBiochemistryCell biologyChemistrySialic acidGlycoproteinBiologyCathepsin

Abstract

fetched live from OpenAlex

Sialic acids linked to glycoproteins and glycolipids are important mediators of cell and protein recognition events. These sugar residues are removed by neuraminidases (sialidases). Neuraminidase-1 (sialidase-1 or NEU1) is a ubiquitously expressed mammalian sialidase located in lysosomes and on the cell membrane. Because of its modulation of multiple signaling processes, it is a potential therapeutic target for cancers and immune disorders. Genetic defects in NEU1 or in its protective protein cathepsin A (PPCA, CTSA) cause the lysosomal storage diseases sialidosis and galactosialidosis. To further our understanding of this enzyme's function at the molecular level, we determined the three-dimensional structure of murine NEU1. The enzyme oligomerizes through two self-association interfaces and displays a wide substrate-binding cavity. A catalytic loop adopts an inactive conformation. We propose a mechanism of activation involving a conformational change in this loop upon binding to its protective protein. These findings may facilitate the development of selective inhibitor and agonist therapies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.296
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2023
Admission routes2
Has abstractyes

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