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Record W4392915381 · doi:10.26434/chemrxiv-2024-cjs5j

Engineering a Photoenzyme to Use Red Light

2024· preprint· en· W4392915381 on OpenAlexfundno aff
J. M. Carceller, Bhumika Jayee, Claire G. Page, Daniel G. Oblinsky, Nithin Chintala, Gustavo Mondragón‐Solórzano, Jingzhe Cao, Zayed Alassad, Zheyu Zhang, Nathaniel White, Gregory D. Scholes, Sijia S. Dong, Todd K. Hyster

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldChemistry
TopicRadical Photochemical Reactions
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaBasic Energy SciencesNational Energy Research Scientific Computing CenterU.S. Department of EnergyOffice of ScienceNational Science Foundation
KeywordsFlavin groupCyanPhotochemistryChemistrySubstrate (aquarium)CofactorCatalysisPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Photoenzymatic catalysis is an emerging platform for asymmetric synthesis. In most of these reactions, the protein templates a charge transfer complex between the cofactor and substrate, which absorbs in the blue region of the electromagnetic spectrum. Here, we report the engineering of a photoenzymatic ‘ene’-reductase to utilize red light (620 nm) for a radical cyclization reaction. Mechanistic studies indicate that red light ac-tivity is achieved by introducing a broadly absorbing shoulder off the previously identified cyan absorption feature. Molecular dynamics simulations, docking, and excited-state calculations suggest that red light absorption is a 𝜋→ 𝜋* transition from flavin to the substrate, while the cyan feature is the red-shift of the flavin 𝜋→ 𝜋* transition, which occurs upon substrate binding. Differences in the excitation event help to disfavor alkylation of the flavin cofactor, a pathway for catalyst decomposition observed with cyan light but not red.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.139
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0010.002
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 head0.018
GPT teacher head0.245
Teacher spread0.228 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2024
Admission routes1
Has abstractyes

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