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Record W2041157699 · doi:10.1021/ac061391o

Molecular Formula Analysis by an MS/MS/MS Technique To Expedite Dereplication of Natural Products

2006· article· en· W2041157699 on OpenAlex
Yasuo Konishi, Taira Kiyota, Cristina Draghici, Jin‐Ming Gao, Faustinus K. Yeboah, Stéphane Acoca, Suwatchai Jarussophon, Enrico O. Purisima

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnalytical Chemistry · 2006
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsBiotechnology Research Institute
FundersMcGill University
KeywordsChemistryFragmentation (computing)Natural productMoleculeMass spectrometryMassMolecular massIonCharacterization (materials science)Polyatomic ionCombinatorial chemistryChromatographyComputational chemistryMass spectrumStereochemistryNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

A facile and sensitive mass spectrometric method has been developed for the dereplication of natural products. The method provides information about the molecular formula and substructure of a precursor molecule and its fragments, which are invaluable aids in dereplication of natural products at their early stages of purification and characterization. Collision-induced MS/MS technique is used to fragment a precursor ion into several product ions, and individual product ions are selected and subjected to collision-induced MS/MS/MS analysis. This method enables the identification of the fragmentation pathway of a precursor molecule from its first-generation fragments (MS/MS), through to the nth generation product ions (MSn). It also allows for the identification of the corresponding neutral products released (neutral losses). Elements used in the molecular formula analysis include C, H, N, O, and S, as most natural products are constituted by these five elements. High-resolution mass separation and accurate mass measurements afforded the unique identification of molecular formula of small neutral products. Through sequential add-up of the molecular formulas of the small neutral products, the molecular formula of the precursor ion and its productions were uniquely determined. The molecular formula of the precursor molecule was then reversely used to identify or confirm the molecular formula of the neutral products and that of the productions. The molecular formula of the neutral fragments allowed for the identification of substructures, leading to a rapid and efficient characterization of precursor natural product. The method was applied to paclitaxel (C47H51NO14; 853 amu) to identify its molecular formula and its substructures, and to characterize its potential fragmentation pathways. The method was further validated by correctly identifying the molecular formula of minocycline (C23H27N3O7; 457 amu) and piperacillin (C23H27N5O7S; 517 amu).

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.110
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.266
Teacher spread0.260 · 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