Evidence map›Paper›PMID 38308132›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024

Growth-Coupled Evolutionary Pressure Improving Epimerases for D-Allulose Biosynthesis Using a Biosensor-Assisted In Vivo Selection Platform.

Chao Li, Xin Gao, Huimin Li, Tong Wang, Fuping Lu, Hui-Min Qin

Open access · goldAbstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
6.1field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Chao LiKey Laboratory of Industrial Fermentation Microbiology of the Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, National Engineering Laboratory for Industrial Enzymes, College of Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457, P. R. China.
Xin GaoKey Laboratory of Industrial Fermentation Microbiology of the Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, National Engineering Laboratory for Industrial Enzymes, College of Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457, P. R. China.
Huimin LiKey Laboratory of Industrial Fermentation Microbiology of the Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, National Engineering Laboratory for Industrial Enzymes, College of Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457, P. R. China.
Tong WangKey Laboratory of Industrial Fermentation Microbiology of the Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, National Engineering Laboratory for Industrial Enzymes, College of Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457, P. R. China.
Fuping LuKey Laboratory of Industrial Fermentation Microbiology of the Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, National Engineering Laboratory for Industrial Enzymes, College of Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457, P. R. China.
Hui-Min QinKey Laboratory of Industrial Fermentation Microbiology of the Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, National Engineering Laboratory for Industrial Enzymes, College of Biotechnology, Tianjin University of Science and Technology, Tianjin, 300457, P. R. China.ORCID 0000-0002-2588-3634
Tianjin University of Science and Technology · CN

Funding

National Key Research and Development Program of China 2022YFC2104901National Natural Science Foundation of China 32372279
6 · The paper itself

Abstract

Fast screening strategies that enable high-throughput evaluation and identification of desired variants from diversified enzyme libraries are crucial to tailoring biocatalysts for the synthesis of D-allulose, which is currently limited by the poor catalytic performance of ketose 3-epimerases (KEases). Here, the study designs a minimally equipment-dependent, high-throughput, and growth-coupled in vivo screening platform founded on a redesigned D-allulose-dependent biosensor system. The genetic elements modulating regulator PsiR expression levels undergo systematic optimization to improve the growth-responsive dynamic range of the biosensor, which presents ≈30-fold facilitated growth optical density with a high signal-to-noise ratio (1.52 to 0.05) toward D-allulose concentrations from 0 to 100 mm. Structural analysis and evolutionary conservation analysis of Agrobacterium sp. SUL3 D-allulose 3-epimerase (ADAE) reveal a highly conserved catalytic active site and variable hydrophobic pocket, which together regulate substrate recognition. Structure-guided rational design and directed evolution are implemented using the growth-coupled in vivo screening platform to reprogram ADAE, in which a mutant M42 (P38N/V102A/Y201L/S207N/I251R) is identified with a 6.28-fold enhancement of catalytic activity and significantly improved thermostability with a 2.5-fold increase of the half-life at 60 °C. The research demonstrates that biosensor-assisted growth-coupled evolutionary pressure combined with structure-guided rational design provides a universal route for engineering KEases.

Indexed as

FructoseRacemases and EpimerasesBiological EvolutionFructosepsicoseRacemases and Epimerasesbiosensorscrystal structure analysisD‐allulose 3‐epimerasegrowth‐coupled selection platformprotein engineering

Identifiers

PMID38308132
PMCPMC11005681
OpenAlexW4391513578

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.