MétaCan
Menu
Back to cohort
Record W2338693143 · doi:10.1021/acschembio.5b01076

Cell-specific Profiling of Nascent Proteomes Using Orthogonal Enzyme-mediated Puromycin Incorporation

2016· article· en· W2338693143 on OpenAlexfundno aff
Ruth M. Barrett, Hui-wen Liu, Haihong Jin, Richard H. Goodman, Michael S. Cohen

Bibliographic record

VenueACS Chemical Biology · 2016
Typearticle
Languageen
FieldChemistry
TopicClick Chemistry and Applications
Canadian institutionsnot available
FundersNational Institute of Mental HealthPublic Health Agency of CanadaNational Institutes of HealthTakeda Pharmaceutical CompanyOregon Health and Science University
KeywordsPuromycinProteomeBiologyTranslation (biology)Computational biologyCellCell biologyProtein biosynthesisGene expression profilingGene expressionGeneBiochemistryMessenger RNA

Abstract

fetched live from OpenAlex

Translation regulation is a fundamental component of gene expression, allowing cells to respond rapidly to a variety of stimuli in the absence of new transcription. The lack of methods for profiling nascent proteomes in distinct cell populations in heterogeneous tissues has precluded an understanding of translational regulation in physiologically relevant contexts. Here, we describe a chemical genetic method that involves orthogonal enzyme-mediated incorporation of a clickable puromycin analogue into nascent polypeptides. Using this method, we show that we can label newly synthesized proteins in a cell-specific manner in cells grown in culture and in heterogeneous tissues. We also show that we can identify the nascent proteome in genetically targeted cell populations using affinity enrichment and tandem mass spectrometry. Our method has the potential to provide unprecedented insights into cell-specific translational regulation in heterogeneous tissues.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.260
Teacher spread0.233 · 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 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

Citations49
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueACS Chemical BiologySame topicClick Chemistry and ApplicationsFrench-language works237,207