Evidence map›Paper›PMID 38155642›Full record

ReviewJACS Au2023

Designed Local Electric Fields-Promising Tools for Enzyme Engineering.

Shakir Ali Siddiqui, Thijs Stuyver, Sason Shaik, Kshatresh Dutta Dubey

Open access · goldAbstract readReview
In one paragraph

Review in JACS Au, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
6.0field-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

21 citing papers in PubMed, 46 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Oriented Electric Fields─Universal Catalysts.Accounts of chemical research · 2025
    Article
  10. Article
  11. Article
  12. What Can be Learned From the Electrostatic Environments Within Nitrogenase Enzymes?Chemistry (Weinheim an der Bergstrasse, Germany) · 2025
    Article
  13. Preorganized Electric Fields in Voltage-Gated Sodium Channels.Chembiochem : a European journal of chemical biology · 2025
    Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Electric fields imbue enzyme reactivity by aligning active site fragment orbitals.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  19. Review
  20. 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

4 authors at 3 institutions in 3 countries.

Shakir Ali SiddiquiMolecular Simulation Lab, Department of Chemistry, School of Natural Sciences, Shiv Nadar Institution of Eminence, Delhi NCR, India 201314.ORCID https://orcid.org/0000-0002-3518-2823
Thijs StuyverEcole Nationale Supérieure de Chimie de Paris, Université PSL, CNRS, Institute of Chemistry for Life and Health Sciences, 75 005 Paris, France.ORCID https://orcid.org/0000-0002-8322-0572
Sason ShaikInstitute of Chemistry, Edmond J Safra Campus, The Hebrew University of Jerusalem, Givat Ram, Jerusalem, 9190400, Israel.
Kshatresh Dutta DubeyMolecular Simulation Lab, Department of Chemistry, School of Natural Sciences, Shiv Nadar Institution of Eminence, Delhi NCR, India 201314.ORCID https://orcid.org/0000-0001-8865-7602
Shiv Nadar University · INCentre National de la Recherche Scientifique · FRHebrew University of Jerusalem · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Designing efficient catalysts is one of the ultimate goals of chemists. In this Perspective, we discuss how local electric fields (LEFs) can be exploited to improve the catalytic performance of supramolecular catalysts, such as enzymes. More specifically, this Perspective starts by laying out the fundamentals of how local electric fields affect chemical reactivity and review the computational tools available to study electric fields in various settings. Subsequently, the advances made so far in optimizing enzymatic electric fields through targeted mutations are discussed critically and concisely. The Perspective ends with an outlook on some anticipated evolutions of the field in the near future. Among others, we offer some pointers on how the recent data science/machine learning revolution, engulfing all science disciplines, could potentially provide robust and principled tools to facilitate rapid inference of electric field effects, as well as the translation between optimal electrostatic environments and corresponding chemical modifications.

Identifiers

PMID38155642
PMCPMC10752214
OpenAlexW4389221245

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.