Computational design of SARS-CoV-2 peptide binders with better predicted binding affinities than human ACE2 receptor

Abstract SARS-CoV-2 is coronavirus causing COVID-19 pandemic. To enter human cells, receptor binding domain of S1 subunit of SARS-CoV-2 (SARS-CoV-2-RBD) binds to peptidase domain (PD) of angiotensin-converting enzyme 2 (ACE2) receptor. Employing peptides to inhibit binding between SARS-CoV-2-RBD and...

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Autores principales:	Thassanai Sitthiyotha, Surasak Chunsrivirot
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d32c88726a6b4ff48b66bc613ee2b969
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spelling	oai:doaj.org-article:d32c88726a6b4ff48b66bc613ee2b9692021-12-02T16:34:05ZComputational design of SARS-CoV-2 peptide binders with better predicted binding affinities than human ACE2 receptor10.1038/s41598-021-94873-32045-2322https://doaj.org/article/d32c88726a6b4ff48b66bc613ee2b9692021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94873-3https://doaj.org/toc/2045-2322Abstract SARS-CoV-2 is coronavirus causing COVID-19 pandemic. To enter human cells, receptor binding domain of S1 subunit of SARS-CoV-2 (SARS-CoV-2-RBD) binds to peptidase domain (PD) of angiotensin-converting enzyme 2 (ACE2) receptor. Employing peptides to inhibit binding between SARS-CoV-2-RBD and ACE2-PD is a therapeutic solution for COVID-19. Previous experimental study found that 23-mer peptide (SBP1) bound to SARS-CoV-2-RBD with lower affinity than ACE2. To increase SBP1 affinity, our previous study used residues 21–45 of α1 helix of ACE2-PD (SPB25) to design peptides with predicted affinity better than SBP1 and SPB25 by increasing interactions of residues that do not form favorable interactions with SARS-CoV-2-RBD. To design SPB25 with better affinity than ACE2, we employed computational protein design to increase interactions of residues reported to form favorable interactions with SARS-CoV-2-RBD and combine newly designed mutations with the best single mutations from our previous study. Molecular dynamics show that predicted binding affinities of three peptides (SPB25Q22R, SPB25F8R/K11W/L25R and SPB25F8R/K11F/Q22R/L25R) are better than ACE2. Moreover, their predicted stabilities may be slightly higher than SBP1 as suggested by their helicities. This study developed an approach to design SARS-CoV-2 peptide binders with predicted binding affinities better than ACE2. These designed peptides are promising candidates as SARS-CoV-2 inhibitors.Thassanai SitthiyothaSurasak ChunsrivirotNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Thassanai Sitthiyotha Surasak Chunsrivirot Computational design of SARS-CoV-2 peptide binders with better predicted binding affinities than human ACE2 receptor
description	Abstract SARS-CoV-2 is coronavirus causing COVID-19 pandemic. To enter human cells, receptor binding domain of S1 subunit of SARS-CoV-2 (SARS-CoV-2-RBD) binds to peptidase domain (PD) of angiotensin-converting enzyme 2 (ACE2) receptor. Employing peptides to inhibit binding between SARS-CoV-2-RBD and ACE2-PD is a therapeutic solution for COVID-19. Previous experimental study found that 23-mer peptide (SBP1) bound to SARS-CoV-2-RBD with lower affinity than ACE2. To increase SBP1 affinity, our previous study used residues 21–45 of α1 helix of ACE2-PD (SPB25) to design peptides with predicted affinity better than SBP1 and SPB25 by increasing interactions of residues that do not form favorable interactions with SARS-CoV-2-RBD. To design SPB25 with better affinity than ACE2, we employed computational protein design to increase interactions of residues reported to form favorable interactions with SARS-CoV-2-RBD and combine newly designed mutations with the best single mutations from our previous study. Molecular dynamics show that predicted binding affinities of three peptides (SPB25Q22R, SPB25F8R/K11W/L25R and SPB25F8R/K11F/Q22R/L25R) are better than ACE2. Moreover, their predicted stabilities may be slightly higher than SBP1 as suggested by their helicities. This study developed an approach to design SARS-CoV-2 peptide binders with predicted binding affinities better than ACE2. These designed peptides are promising candidates as SARS-CoV-2 inhibitors.
format	article
author	Thassanai Sitthiyotha Surasak Chunsrivirot
author_facet	Thassanai Sitthiyotha Surasak Chunsrivirot
author_sort	Thassanai Sitthiyotha
title	Computational design of SARS-CoV-2 peptide binders with better predicted binding affinities than human ACE2 receptor
title_short	Computational design of SARS-CoV-2 peptide binders with better predicted binding affinities than human ACE2 receptor
title_full	Computational design of SARS-CoV-2 peptide binders with better predicted binding affinities than human ACE2 receptor
title_fullStr	Computational design of SARS-CoV-2 peptide binders with better predicted binding affinities than human ACE2 receptor
title_full_unstemmed	Computational design of SARS-CoV-2 peptide binders with better predicted binding affinities than human ACE2 receptor
title_sort	computational design of sars-cov-2 peptide binders with better predicted binding affinities than human ace2 receptor
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d32c88726a6b4ff48b66bc613ee2b969
work_keys_str_mv	AT thassanaisitthiyotha computationaldesignofsarscov2peptidebinderswithbetterpredictedbindingaffinitiesthanhumanace2receptor AT surasakchunsrivirot computationaldesignofsarscov2peptidebinderswithbetterpredictedbindingaffinitiesthanhumanace2receptor
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Computational design of SARS-CoV-2 peptide binders with better predicted binding affinities than human ACE2 receptor

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