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Record W4403332606 · doi:10.1021/jacs.4c10348

Discovery of a New Class of Aminoacyl Radical Enzymes Expands Nature’s Known Radical Chemistry

2024· article· en· W4403332606 on OpenAlexaff
Beverly Fu, Hao Yang, Duncan J. Kountz, Maike N. Lundahl, Harry R. Beller, William E. Broderick, Joan Broderick, Brian M. Hoffman, Emily P. Balskus

Bibliographic record

VenueJournal of the American Chemical Society · 2024
Typearticle
Languageen
FieldEnergy
TopicMetalloenzymes and iron-sulfur proteins
Canadian institutionsUniversity of Toronto
FundersNational Institute of General Medical SciencesHoward Hughes Medical InstituteNational Institutes of HealthNational Science Foundation
KeywordsChemistryRadicalOrganic chemistryStereochemistry

Abstract

fetched live from OpenAlex

Radical enzymes, including the evolutionarily ancient glycyl radical enzyme (GRE) family, catalyze chemically challenging reactions that are involved in a myriad of important biological processes. All GREs possess an essential, conserved backbone glycine that forms a stable, catalytically essential α-carbon radical. Through close examination of the GRE family, we unexpectedly identified hundreds of noncanonical GRE homologs that encode either an alanine, serine, or threonine in place of the catalytic glycine residue. Contrary to a long-standing belief, we experimentally demonstrate that these aminoacyl radical enzymes (AAREs) form stable α-carbon radicals on the three cognate residues when activated by partner activating enzymes. The previously unrecognized AAREs are widespread in microbial genomes, highlighting their biological importance and potential for exhibiting new reactivity. Collectively, these studies expand the known radical chemistry of living systems while raising questions about the evolutionary emergence of the AAREs.

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 score0.002
Threshold uncertainty score0.006

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 studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.248
Teacher spread0.240 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Chemical SocietySame topicMetalloenzymes and iron-sulfur proteinsFrench-language works237,207