Evidence map›Paper›PMID 32712315›Full record

ArticleInfection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases2020

Decoding the proteome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for cell-penetrating peptides involved in pathogenesis or applicable as drug delivery vectors.

Shiva Hemmati, Yasaman Behzadipour, Mahdi Haddad

Open access · bronzeAbstract read
In one paragraph

Article in Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

15 citing papers in PubMed, 27 citations in OpenAlex.

  1. Review
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  6. Pharmaceuticals (Basel, Switzerland) · 2023
    Review
  7. Article
  8. Article
  9. Designed Multifunctional Peptides for Intracellular Targets.Antibiotics (Basel, Switzerland) · 2022
    Review
  10. Review
  11. Structural biology of SARS-CoV-2: open the door for novel therapies.Signal transduction and targeted therapy · 2022
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  12. Review
  13. Article
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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

3 authors at 1 institution in 1 country.

Shiva HemmatiDepartment of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran; Biotechnology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran; Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran. Electronic address: hemmatish@sums.ac.ir.
Yasaman BehzadipourDepartment of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran.
Mahdi HaddadDepartment of Pharmaceutical Biotechnology, School of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran.
Shiraz University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Synthetic or natural derived cell-penetrating peptides (CPPs) are vastly investigated as tools for the intracellular delivery of membrane-impermeable molecules. As viruses are intracellular obligate parasites, viral originated CPPs have been considered as suitable intracellular shuttling vectors for cargo transportation. A total of 310 CPPs were identified in the proteome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Screening the proteome of the cause of COVID-19 reveals that SARS-CoV-2 CPPs (SCV2-CPPs) span the regions involved in replication, protein-nucleotide and protein-protein interaction, protein-metal ion interaction, and stabilization of homo/hetero-oligomers. However, to find the most appropriate peptides as drug delivery vectors, one might face several hurdles. Computational analyses showed that 94.3% of the identified SCV2-CPPs are non-toxins, and 38% are neither antigenic nor allergenic. Interestingly, 36.70% of SCV2-CPPs were resistant to all four groups of protease families. Nearly 1/3 of SCV2-CPPs had sufficient inherent or induced helix and sheet conformation leading to increased uptake efficiency. Heliquest lipid-binding discrimination factor revealed that 44.30% of the helical SCV2-CPPs are lipid-binding helices. Although Cys-rich derived CPPs of helicase (NSP13) can potentially fold into a cyclic conformation in endosomes with a higher rate of endosomal release, the most optimal SCV2-CPP candidates as vectors for drug delivery were SCV2-CPP118, SCV2-CPP119, SCV2-CPP122, and SCV2-CPP129 of NSP12 (RdRp). Ten experimentally validated viral-derived CPPs were also used as the positive control to check the scalability and reliability of our protocol in SCV2-CPP retrieval. Some peptides with a cell-penetration ability known as bioactive peptides are adopted as biotherapeutics themselves. Therefore, 59.60%, 29.63%, and 32.32% of SCV2-CPPs were identified as potential antibacterial, antiviral, and antifungals, respectively. While 63.64% of SCV2-CPPs had immuno-modulatory properties, 21.89% were recognized as anti-cancers. Conclusively, the workflow of this study provides a platform for profound screening of viral proteomes as a rich source of biotherapeutics or drug delivery carriers.

Indexed as

Antiviral AgentsCell-Penetrating PeptidesComputational BiologyDrug Evaluation, PreclinicalHumansProtein FoldingProtein Structure, SecondaryProteomeSARS-CoV-2Support Vector MachineViral ProteinsVirus ReplicationAntiviral AgentsCell-Penetrating PeptidesProteomeViral ProteinsBioactive peptideCell-penetrating peptideCoronavirusCOVID-19Drug deliverySARS-CoV-2

Identifiers

PMID32712315
PMCPMC7378008
OpenAlexW3044377469

What OpenQuestion holds

Textmetadata
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.