In Silico Identification of Multi-target Anti-SARS-CoV-2 Peptides from Quinoa Seed Proteins

Abstract

Peptides are promising antagonists against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). To expedite drug discovery, a computational approach is widely employed for the initial screening of anti-SARS-CoV-2 candidates. This study aimed to investigate the potential of peptides from quinoa seed proteins as multi-target antagonists against SARS-CoV-2 spike glycoprotein receptor-binding domain, main protease, and papain-like protease. Five quinoa proteins were hydrolyzed in silico by papain and subtilisin. Among the 1465 peptides generated, seven could interact stably with the key binding residues and catalytic residues of the viral targets, mainly via hydrogen bonds and hydrophobic interactions. The seven peptides were comparable or superior to previously reported anti-SARS-CoV-2 peptides based on docking scores. Key residues in the seven peptides contributing to binding to viral targets were determined by computational alanine scanning. The seven peptides were predicted in silico to be non-toxic and non-allergenic. The peptides ranged between 546.66 and 3974.87 g/mol in molecular mass, besides exhibiting basic and cationic properties (isoelectric points: 8.26–12.10; net charges: 0.1–4.0). Among the seven peptides, VEDKGMMHQQRMMEKAMNIPRMCGTMQRKCRMS was found to bind the largest number of key residues on the targets. In conclusion, seven putative non-toxic, non-allergenic, multi-target anti-SARS-CoV-2 peptides were identified from quinoa seed proteins. The in vitro and in vivo efficacies of the seven peptides against SARS-CoV-2 deserve attention in future bench-top testing. Supplementary Information The online version contains supplementary material available at 10.1007/s10989-021-10214-y.

Fruit Bromelain-Derived Peptide Potentially Restrains the Attachment of SARS-CoV-2 Variants to hACE2: A Pharmacoinformatics Approach

“…An in silico study reported that these peptides can bind to the RBD at key residues [ 75 ]. The peptide VEDKGMMHQQRMMEKAMNIPRMCGTMQRKCRMS, derived from quinoa seed protein, was shown to form hydrophobic interactions with the key binding residues Leu455, Phe456, Phe486, and Gln493 on the RBD [ 76 ]. However, not all peptides can bind to key RBD residues.…”

In Silico Analysis of Inhibiting Papain-like Protease from SARS-CoV-2 by Using Plant-Derived Peptides

“…One of the interesting in silico studies on screening therapeutic against SARS-CoV-2 was performed by Wong et al, in which they investigated the efficacy of Peptides from Quinoa seed proteins on various targets from SARS-CoV-2, including PLpro. They reported that seven peptides from the mentioned source could effectively and stably bind to the viral targets, and none of these peptides were allergenic or toxic to human body, which requires future in vitro and in vivo examinations (Wong et al 2021a , b ). Another study by Srivastavaa et al, investigated the efficacy of phytoconstituents derived from Withania somnifera (a medicinal plant) against SARS-CoV-2 target proteins and human ACE2 through an in silico approach, which showed that Withanolides A and B could effectively bind to spike protein and PLpro components (Srivastava et al 2020 ).…”

“…To present an effective therapeutic against SARS-CoV-2, having a conclusive knowledge about the viral structures is necessary (Mohabatkar et al 2021a , 2021b ; Valle et al 2020 ), which could enable to find a more appropriate candidates for targeting. Regarding the potential target for COVID-19 drug development, some molecular structures have been suggested including spike glycoprotein, main protease, and papain-like protease (Wong et al 2021a , 2021b ). Suppressing these components or their activity could potentially regress the malady condition of COVID-19 (Amin et al 2021 ).…”

Conventional and in silico approaches to select promising food-derived bioactive peptides: A review

“…On the other hand, AllerTOP v.2.0 is a powerful tool that classifies allergens and non-allergens, using the k-Nearest Neighbors method, with an accuracy of 87% ( Aminnezhad, Abdi-Ali, Ghazanfari, Bandehpour, & Zarrabi, 2020 ). This tool has been used to predict the allergenicity of quinoa seed proteins, in which only 1 of 8 peptides showed probable allergenicity ( Wong, Ong, Kumar, & Chai, 2021 ).…”

“…While only ALPMHIR showed interactions with the spike proteins of the virus, pointing to possible prevention of the binding of SARS-CoV-2 with the host cells. Similarly, Wong et al (2021) identified the ability of seven peptides from quinoa seed proteins to bind three targets of SARS-CoV-2 (spike glycoprotein RBD, M PRO , and PL PRO ) employing molecular docking, mainly through hydrogen bonding and hydrophobic interaction.…”