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Cited by 381

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Computational Design of ACE2-Based Peptide Inhibitors of SARS-CoV-2

Open access
Han, Yanxiao, Král, Petr·2020·ACS Nano

Abstract

Peptide inhibitors against the SARS-CoV-2 coronavirus, currently causing a worldwide pandemic, are designed and simulated. The inhibitors are mostly formed by two sequential self-supporting α-helices (bundle) extracted from the protease domain (PD) of angiotensin-converting enzyme 2 (ACE2), which bind to the SARS-CoV-2 receptor binding domains. Molecular dynamics simulations revealed that the α-helical peptides maintain their secondary structure and provide a highly specific and stable binding (blocking) to SARS-CoV-2. To provide a multivalent binding to the SARS-CoV-2 receptors, many such peptides could be attached to the surfaces of nanoparticle carriers. The proposed peptide inhibitors could provide simple and efficient therapeutics against the COVID-19 disease.

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Crossref · 10.1021/acsnano.0c02857

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Metadata

Authors
Han, Y., & Král, P.
Venue
ACS Nano
Published
2020-04-14
Volume
14
Issue
4
Pages
5143-5147
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W3015313862
Cited by
381
References
7
FWCI
9.96
Type
journal article
Language
en
Open access
bronze

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.