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Identification of tuna protein-derived peptides as potent SARS-CoV-2 inhibitors via molecular docking and molecular dynamic simulation.

ABSTRACT: The present study aimed to identify potential SARS-CoV-2 inhibitory peptides from tuna protein by virtual screening. The molecular docking was performed to elicit the interaction mechanism between targets (M^pro and ACE2) and peptides. As a result, a potential antiviral peptide EEAGGATAAQIEM (E-M) was identified. Molecular docking analysis revealed that E-M could interact with residues Thr190, Thr25, Thr26, Ala191, Leu50, Met165, Gln189, Glu166, His164, His41, Cys145, Gly143, and Asn119 of M^pro via 11 conventional hydrogen bonds, 9 carbon hydrogen bonds, and one alkyl interaction. The formation of hydrogen bonds between peptide E-M and the residues Gly143 and Gln189 of M^pro may play important roles in inhibiting the activity of M^pro. Besides, E-M could bind with the residues His34, Phe28, Thr27, Ala36, Asp355, Glu37, Gln24, Ser19, Tyr83, and Tyr41 of ACE2. Hydrogen bonds and electrostatic interactions may play vital roles in blocking the receptor ACE2 binding with SARS-CoV-2.

SUBMITTER: Yu Z

PROVIDER: S-EPMC7553880 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Identification of tuna protein-derived peptides as potent SARS-CoV-2 inhibitors via molecular docking and molecular dynamic simulation.

Yu Zhipeng Z Kan Ruotong R Ji Huizhuo H Wu Sijia S Zhao Wenzhu W Shuian David D Liu Jingbo J Li Jianrong J

Food chemistry 20201014

The present study aimed to identify potential SARS-CoV-2 inhibitory peptides from tuna protein by virtual screening. The molecular docking was performed to elicit the interaction mechanism between targets (M<sup>pro</sup> and ACE2) and peptides. As a result, a potential antiviral peptide EEAGGATAAQIEM (E-M) was identified. Molecular docking analysis revealed that E-M could interact with residues Thr190, Thr25, Thr26, Ala191, Leu50, Met165, Gln189, Glu166, His164, His41, Cys145, Gly143, and Asn11 ...[more]

PMID: 33092925

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Project description:BackgroundIn seek of potent and non-toxic iminoguanidine derivatives formerly assessed as active Pseudomonas aeruginosa inhibitors, a combined mathematical approach of quantitative structure-activity relationship (QSAR), homology modeling, docking simulation, ADMET, and molecular dynamics simulations were executed on iminoguanidine derivatives.ResultsThe QSAR method was employed to statistically analyze the structure-activity relationships (SAR) and had conceded good statistical significance for eminent predictive model; (GA-MLR: Q2LOO = 0.8027; R2 = 0.8735; R2ext = 0.7536). Thorough scrutiny of the predictive models disclosed that the Centered Broto-Moreau autocorrelation - lag 1/weighted by I-state and 3D topological distance-based autocorrelation-lag 9/weighted by I-state oversee the biological activity and rendered much useful information to realize the properties required to develop new potent Pseudomonas aeruginosa inhibitors. The next mathematical model work accomplished here emphasizes finding a potential drug that could aid in curing Pseudomonas aeruginosa and SARS-CoV-2 as the drug targets Pseudomonas aeruginosa. This involves homology modeling of RNA polymerase-binding transcription factor DksA and COVID-19 main protease receptors, docking simulations, and pharmacokinetic screening studies of hits compounds against the receptor to identify potential inhibitors that can serve to regulate the modeled enzymes. The modeled protein exhibits the most favorable regions more than 90% with a minimum disallowed region less than 5% and is simulated under a hydrophilic environment. The docking simulations of all the series to the binding pocket of the built protein model were done to demonstrate their binding style and to recognize critical interacting residues inside the binding site. Their binding constancy for the modeled receptors has been assessed through RMSD, RMSF, and SASA analysis from 1-ns molecular dynamics simulations (MDS) run.ConclusionOur acknowledged drugs could be a proficient cure for SARS-CoV-2 and Pseudomonas aeruginosa drug discovery, having said that extra testing (in vitro and in vivo) is essential to explain their latent as novel drugs and manner of action.

Dataset Information

Identification of tuna protein-derived peptides as potent SARS-CoV-2 inhibitors via molecular docking and molecular dynamic simulation.

Publications

Identification of tuna protein-derived peptides as potent SARS-CoV-2 inhibitors via molecular docking and molecular dynamic simulation.

Similar Datasets

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