Evidence map›Paper›PMID 36768158›Full record

ArticleInternational journal of molecular sciences2023

Self-Assembling Enzymatic Nanocomplexes with Polypeptides and Low-Weight Organic Compounds: Preparation, Characterization, and Application of New Antibacterials.

Ilya Lyagin, Nikolay Stepanov, Denis Presnov, Artem Trifonov, Elena Efremenko

Abstract read
In one paragraph

Article in International journal of molecular sciences, 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

5 authors.

Ilya LyaginFaculty of Chemistry, Lomonosov Moscow State University, 119991 Moscow, Russia.ORCID 0000-0002-3970-4334
Nikolay StepanovFaculty of Chemistry, Lomonosov Moscow State University, 119991 Moscow, Russia.ORCID 0000-0003-0821-8226
Denis PresnovSkobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, 119991 Moscow, Russia.ORCID 0000-0002-9213-0165
Artem TrifonovFaculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia.
Elena EfremenkoFaculty of Chemistry, Lomonosov Moscow State University, 119991 Moscow, Russia.ORCID 0000-0002-6992-854X

Funding

State Task of Lomonosov Moscow State University 121041500039-8
6 · The paper itself

Abstract

The self-assembling of nanosized materials is a promising field for research and development. Multiple approaches are applied to obtain inorganic, organic and composite nanomaterials with different functionality. In the present work, self-assembling nanocomplexes (NCs) were prepared on the basis of enzymes and polypeptides followed by the investigation of the influence of low-molecular weight biologically active compounds on the properties of the NCs. For that, the initially possible formation of catalytically active self-assembling NCs of four hydrolytic enzymes with nine effectors was screened via molecular modeling. It allowed the selection of two enzymes (hexahistidine-tagged organophosphorus hydrolase and penicillin acylase) and two compounds (emodin and naringenin) having biological activity. Further, such NCs based on surface-modified enzymes were characterized by a batch of physical and biochemical methods. At least three NCs containing emodin and enzyme (His

Indexed as

EmodinPenicillin AmidaseAnti-Bacterial AgentsAryldialkylphosphataseOrganic ChemicalsPeptidesAnti-Bacterial AgentsAryldialkylphosphataseEmodinOrganic ChemicalsPenicillin AmidasePeptidesantibacterialsemodinenzymehexahistidine-tagged organophosphorus hydrolasemolecular modelingnanomaterialpenicillin acylasepolymyxinsquorum quenching

Identifiers

PMID36768158
PMCPMC9915939

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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.