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Record W3215055113 · doi:10.1101/2021.11.25.470051

Genetic perturbation enhances functional heterogeneity in alkaline phosphatase

2021· preprint· en· W3215055113 on OpenAlexaff
Morito Sakuma, Shingo Honda, Hiroshi Ueno, Kentaro Miyazaki, Nobuhiko Tokuriki, Hiroyuki Noji

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2021
Typepreprint
Languageen
FieldMedicine
TopicAlkaline Phosphatase Research Studies
Canadian institutionsCanada's Michael Smith Genome Sciences CentreUniversity of British Columbia
FundersCore Research for Evolutional Science and TechnologyJapan Science and Technology AgencyJapan Society for the Promotion of Science
KeywordsGenetic heterogeneityAlkaline phosphataseEnzymeMutantBiologyPhosphatasePoint mutationGeneticsSpatial heterogeneityFunctional analysisChemistryBiochemistryGenePhenotypeEcology

Abstract

fetched live from OpenAlex

Abstract Enzymes inherently exhibit molecule-to-molecule heterogeneity in catalytic activity or function, which underlies the acquisition of new functions in evolutionary processes. However, correlations between the functional heterogeneity of an enzyme and its multi-functionality or promiscuity remain elusive. In addition, the modulation of functional heterogeneity upon genetic perturbation is currently unexplored. Here, we quantitatively analyzed functional heterogeneity in the wild-type and 69 single-point mutants of Escherichia coli alkaline phosphatase (AP) by employing single-molecule assay with a femtoliter reactor array device. Most mutant enzymes exhibited higher functional heterogeneity than the wild-type enzyme, irrespective of catalytic activity. These results indicated that the wild-type AP minimizes functional heterogeneity, and single-point mutations can significantly expand the span of functional heterogeneity in AP. Moreover, we identified a clear correlation between functional heterogeneity and promiscuous activities. These findings suggest that enzymes can acquire greater functional heterogeneity following marginal genetic perturbations that concomitantly promote catalytic promiscuity.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.272
Teacher spread0.246 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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