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Record W2508402358 · doi:10.1002/bip.22939

<scp>N</scp>omenclature of homodetic cyclic peptides produced from ribosomal precursors: <scp>A</scp>n IUPAC task group interim report

2016· article· en· W2508402358 on OpenAlexaff
David J. Craik, Youn Young Shim, Ulf Göransson, Gerard P. Moss, Ning‐Hua Tan, Pramodkumar D. Jadhav, Jianheng Shen, Martin J. T. Reaney

Bibliographic record

VenueBiopolymers · 2016
Typearticle
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsGenome PrairieUniversity of Saskatchewan
FundersAustralian Research CouncilInternational Fund for Agricultural DevelopmentInternational Union of Pure and Applied Chemistry
KeywordsChemical nomenclatureInterimNumberingNomenclatureChemistryTask (project management)Task groupGroup (periodic table)AmideCyclic peptideStereochemistryComputer sciencePeptideBiochemistryOrganic chemistryEngineeringProgramming languageBiologyEngineering managementPolitical science

Abstract

fetched live from OpenAlex

In 2015, an International Union of Pure and Applied Chemistry (IUPAC) Task Group was formed to develop nomenclature recommendations for homodetic cyclic peptides produced from ribosomal precursors. Delegates of the 2015 International Conference on Circular Proteins (ICCP) were presented with the strengths and weaknesses of four published approaches to homodetic cyclic peptide nomenclature, and a summary of the ensuing discussion is presented here. This interim report presents a potentially novel suggestion-the use of Cahn-Ingold-Prelog rules to specify amino acid priority in homodetic peptides for consistent numbering. Indeed, this might be the first extension of the Cahn-Ingold-Prelog rules in five decades. The authors invite interested parties to contact the corresponding author with suggestions for the improvement of the proposed nomenclature; these ideas will be discussed and considered for inclusion in the final report.

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)0.007
metaresearch head score (Gemma)0.005
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 score0.008
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.006

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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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