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. 2024 Jan 4;16(1):175.
doi: 10.3390/nu16010175.

Antibacterial Effect of Sesame Protein-Derived Peptides against Escherichia coli and Staphylococcus aureus: In Silico and In Vitro Analysis

Zehui Zhu  1 , Fei Pan  2 , Ou Wang  3 , Liang Zhao  1 , Lei Zhao  1
Affiliations

Antibacterial Effect of Sesame Protein-Derived Peptides against Escherichia coli and Staphylococcus aureus: In Silico and In Vitro Analysis

Zehui Zhu et al. Nutrients. .

Abstract

This study aimed to screen out antibacterial peptides derived from sesame (Sesamum indicum L.) through in silico and in vitro methods. In silico proteolysis of sesame proteins with pepsin, trypsin, and chymotrypsin was performed with the online server BIOPEP-UWM. The CAMPR3 online server was used to predict the antimicrobial effect of peptides. The ToxinPred, PepCalc, and AllergenFP tools were utilized to forecast the physicochemical properties, toxicity, and allergen of the peptides. Molecular docking analysis showed that six cationic antimicrobial peptides could directly interact with the key sites of dihydropteroate synthase, whereas Ala-Gly-Gly-Val-Pro-Arg and Ser-Thr-Ile-Arg exhibited the strongest binding affinity. In vitro antibacterial experiment showed the minimum inhibitory concentration (MIC) of Ser-Thr-Ile-Arg against Escherichia coli and Staphylococcus aureus was 1024 and 512 µg/mL, respectively. Meanwhile, MIC of Ala-Gly-Gly-Val-Pro-Arg against both bacterial species was 512 µg/mL. Our results suggest that peptides from sesame possess the ability to potentially hinder bacterial activity.

Keywords: Escherichia coli; Staphylococcus aureus; antibacterial peptides; dihydropteroate synthase; molecular docking; sesame protein.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Peptides from (a) 11S globulin; (b) 2S globulin by gastrointestinal digestion simulated by pepsin, chymotrypsin A, and trypsin in the BIOPEP database.
Figure 2
Figure 2
Prediction of antimicrobial peptides by statistical models in CAMPR3 database including random Forest (RF), support vector machines (SVM), artificial neural network (ANN), and discriminant analysis (DA); (a) 11S globulin, (b) 2S globulin.
Figure 3
Figure 3
Molecular docking analysis: best-chosen conformation of the binding sites of (A1) Ser-Thr-Ile-Arg and (A2) Ala-Gly-Gly-Val-Pro-Arg in the docking pocket, where the ligand is represented by a green stick and the protein by a gray surface; (B1) 3D view of Ser-Thr-Ile-Arg and (B2) Ala-Gly-Gly-Val-Pro-Arg peptide binding site interactions, where the ligand is indicated in gray and the hydrogen bonding with the protein amino acid is indicated in green; (C1) 2D view of the interaction of Ser-Thr-Ile-Arg and (C2) Ala-Gly-Gly-Val-Pro-Arg binding sites.
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