Evidence map›Paper›PMID 37292061›Full record

ArticleRSC chemical biology2023

Study and design of amino acid-based radical enzymes using unnatural amino acids.

Feiyan Yuan, Binbin Su, Yang Yu, Jiangyun Wang

Abstract read
In one paragraph

Article in RSC chemical biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Review
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

4 authors.

Feiyan YuanInstitute of Biochemical Engineering, Key Laboratory of Medical Molecule Science and Pharmaceutical Engineering, Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology Beijing 102488 China yang_yu@outlook.com.
Binbin SuInstitute of Biochemical Engineering, Key Laboratory of Medical Molecule Science and Pharmaceutical Engineering, Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology Beijing 102488 China yang_yu@outlook.com.
Yang YuInstitute of Biochemical Engineering, Key Laboratory of Medical Molecule Science and Pharmaceutical Engineering, Ministry of Industry and Information Technology, School of Chemistry and Chemical Engineering, Beijing Institute of Technology Beijing 102488 China yang_yu@outlook.com.ORCID https://orcid.org/0000-0002-0784-9715
Jiangyun WangLaboratory of RNA Biology, Institute of Biophysics, Chinese Academy of Sciences Beijing 100101 China jwang@ibp.ac.cn.ORCID https://orcid.org/0000-0002-5974-1562

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radical enzymes harness the power of reactive radical species by placing them in a protein scaffold, and they are capable of catalysing many important reactions. New native radical enzymes, especially those with amino acid-based radicals, in the category of non-heme iron enzymes (including ribonucleotide reductases), heme enzymes, copper enzymes, and FAD-radical enzymes have been discovered and characterized. We discussed recent research efforts to discover new native amino acid-based radical enzymes, and to study the roles of radicals in processes such as enzyme catalysis and electron transfer. Furthermore, design of radical enzymes in a small and simple scaffold not only allows us to study the radical in a well-controlled system and test our understanding of the native enzymes, but also allows us to create powerful enzymes. In the study and design of amino acid-based radical enzymes, the use of unnatural amino acids allows precise control of p

Identifiers

PMID37292061
PMCPMC10246556

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Registered trials

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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.