tetano
Editor, Senior Moderator
Sci Rep
. 2021 Aug 2;11(1):15650.
doi: 10.1038/s41598-021-94873-3.
Computational design of SARS-CoV-2 peptide binders with better predicted binding affinities than human ACE2 receptor
Thassanai Sitthiyotha[SUP] 1 [/SUP], Surasak Chunsrivirot[SUP] 2 3 [/SUP]
Affiliations
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 (SPB25[SUB]Q22R[/SUB], SPB25[SUB]F8R/K11W/L25R[/SUB] and SPB25[SUB]F8R/K11F/Q22R/L25R[/SUB]) 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.
. 2021 Aug 2;11(1):15650.
doi: 10.1038/s41598-021-94873-3.
Computational design of SARS-CoV-2 peptide binders with better predicted binding affinities than human ACE2 receptor
Thassanai Sitthiyotha[SUP] 1 [/SUP], Surasak Chunsrivirot[SUP] 2 3 [/SUP]
Affiliations
- PMID: 34341401
- DOI: 10.1038/s41598-021-94873-3
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 (SPB25[SUB]Q22R[/SUB], SPB25[SUB]F8R/K11W/L25R[/SUB] and SPB25[SUB]F8R/K11F/Q22R/L25R[/SUB]) 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.