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Record W4316927300 · doi:10.1093/glycob/cwac072

Cataloging natural sialic acids and other nonulosonic acids (NulOs), and their representation using the Symbol Nomenclature for Glycans

2023· article· en· W4316927300 on OpenAlexaff
Amanda L. Lewis, Filip V. Toukach, Evan Bolton, Xi Chen, Martin Frank, Thomas Lütteke, Yuriy A. Knirel, Ian C. Schoenhofen, Ajit Varki, Evgeny Vinogradov, Robert J. Woods, Natasha E. Zachara, Jian Zhang, Johannis P. Kamerling, Sriram Neelamegham, Alan G. Darvill, Anne Dell, Bernard Henrissat, Carolyn R. Bertozzi, Frédérique Lisacek, Gerald Hart, Hisashi Narimatsu, Hudson H. Freeze, Issaku Yamada, James C. Paulson, Jamey D. Marth, Johannes F.G. Vliegenthart, Kiyoko F. Aoki‐Kinoshita, Marilynn E. Etzler, Markus Aebi, Matthew P. Campbell, Michael Tiemeyer, Minoru Kanehisa, Naoyuki Taniguchi, Nathan Edwards, Nicolle H. Packer, Pamela Stanley, Pauline M. Rudd, Peter H. Seeberger, Raja Mazumder, René Ranzinger, Richard D. Cummings, Roger A. Sayle, Ronald L. Schnaar, Serge Pérez, Stuart Kornfeld, Taroh Kinoshita, William S. York

Bibliographic record

VenueGlycobiology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsNational Research Council Canada
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesNational Heart, Lung, and Blood InstituteNoguchi Memorial Institute for Medical Research, University of GhanaNational Institutes of HealthRussian Science FoundationDanmarks Tekniske UniversitetCentre National de la Recherche ScientifiqueEidgenössische Technische Hochschule ZürichGriffith UniversityUniversity of California, DavisMacquarie UniversityImperial College LondonNational Institute of Diabetes and Digestive and Kidney DiseasesJohns Hopkins UniversityGeorgetown UniversityU.S. National Library of MedicineWashington University in St. LouisGeorge Washington University
KeywordsSialic acidGlycanBiochemistryChemistryAmino acidBiologyComputational biologyGlycoprotein

Abstract

fetched live from OpenAlex

Nonulosonic acids or non-2-ulosonic acids (NulOs) are an ancient family of 2-ketoaldonic acids (α-ketoaldonic acids) with a 9-carbon backbone. In nature, these monosaccharides occur either in a 3-deoxy form (referred to as "sialic acids") or in a 3,9-dideoxy "sialic-acid-like" form. The former sialic acids are most common in the deuterostome lineage, including vertebrates, and mimicked by some of their pathogens. The latter sialic-acid-like molecules are found in bacteria and archaea. NulOs are often prominently positioned at the outermost tips of cell surface glycans, and have many key roles in evolution, biology and disease. The diversity of stereochemistry and structural modifications among the NulOs contributes to more than 90 sialic acid forms and 50 sialic-acid-like variants described thus far in nature. This paper reports the curation of these diverse naturally occurring NulOs at the NCBI sialic acid page (https://www.ncbi.nlm.nih.gov/glycans/sialic.html) as part of the NCBI-Glycans initiative. This includes external links to relevant Carbohydrate Structure Databases. As the amino and hydroxyl groups of these monosaccharides are extensively derivatized by various substituents in nature, the Symbol Nomenclature For Glycans (SNFG) rules have been expanded to represent this natural diversity. These developments help illustrate the natural diversity of sialic acids and related NulOs, and enable their systematic representation in publications and online resources.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0120.018
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.008

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 head0.031
GPT teacher head0.324
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations24
Published2023
Admission routes1
Has abstractyes

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