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Record W2608223149 · doi:10.1039/c7an00338b

Unravelling the mysteries of sub-second biochemical processes using time-resolved mass spectrometry

2017· article· en· W2608223149 on OpenAlexafffund
Cristina Lento, Derek J. Wilson

Bibliographic record

VenueThe Analyst · 2017
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsYork UniversityToronto Public Health
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMass spectrometryChemistryEnvironmental chemistryChromatography

Abstract

fetched live from OpenAlex

Many important chemical and biochemical phenomena proceed on sub-second time scales before entering equilibrium. In this mini-review, we explore the history and recent advancements of time-resolved mass spectrometry (TRMS) for the characterization of millisecond time-scale chemical reactions and biochemical processes. TRMS allows for the simultaneous tracking of multiple reactants, intermediates and products with no chromophoric species required, high sensitivity and temporal resolution. The method has most recently been used for the characterization of several short-lived reaction intermediates in rapid chemical reactions. Most of the reactions that occur in living organisms are accelerated by enzymes, with pre-steady state kinetics only attainable using time-resolved methods. TRMS has been increasingly used to monitor the conversion of substrates to products and the resulting changes to the enzyme during catalytic turnover. Early events in protein folding systems have also been elucidated, along with the characterization of dynamics and transient secondary structures in intrinsically disordered proteins. In this review, we will highlight representative examples where TRMS has been applied to study these phenomena.

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 categoriesInsufficient 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 score0.037
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.276
Teacher spread0.253 · 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.

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

Citations8
Published2017
Admission routes2
Has abstractyes

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