Evidence map›Paper›PMID 35856922›Full record

ReviewBioscience reports2022

Engineering of enzymes using non-natural amino acids.

Yiwen Li, Paul A Dalby

Open access · goldAbstract readReview
In one paragraph

Review in Bioscience reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.8field-weighted citation impact, top 14% 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

6 citing papers in PubMed, 21 citations in OpenAlex.

  1. Review
  2. Article
  3. Biophysics-based protein language models for protein engineering.bioRxiv : the preprint server for biology · 2025
    Article
  4. Article
  5. Review
  6. 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

2 authors at 1 institution in 1 country.

Yiwen LiDepartment of Biochemical Engineering, University College London, London, Gower Street WC1E 6BT, U.K.
Paul A DalbyDepartment of Biochemical Engineering, University College London, London, Gower Street WC1E 6BT, U.K.ORCID 0000-0002-0980-8167
University College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In enzyme engineering, the main targets for enhancing properties are enzyme activity, stereoselective specificity, stability, substrate range, and the development of unique functions. With the advent of genetic code extension technology, non-natural amino acids (nnAAs) are able to be incorporated into proteins in a site-specific or residue-specific manner, which breaks the limit of 20 natural amino acids for protein engineering. Benefitting from this approach, numerous enzymes have been engineered with nnAAs for improved properties or extended functionality. In the present review, we focus on applications and strategies for using nnAAs in enzyme engineering. Notably, approaches to computational modelling of enzymes with nnAAs are also addressed. Finally, we discuss the bottlenecks that currently need to be addressed in order to realise the broader prospects of this genetic code extension technique.

Indexed as

Amino AcidsProtein EngineeringCloning, MolecularProteinsAmino AcidsProteinsDirected EvolutionEnzyme engineeringnon-canonicalnon-naturalSynthetic biology

Identifiers

PMID35856922
PMCPMC9366748
OpenAlexW4286002007

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.