A Computational Approach to Uncovering The Antibacterial and Anti-Inflammatory Potential of Peptides from Piper Betle L. Leaves

Iskandar Iskandar (Public Health Study Program, Undergraduate Program, Lambung Mangkurat University, Banjarbaru, South Kalimantan, Indonesia)
Gusti Muhammad Perdana Putera (Faculty of Dentistry, Universitas Lambung Mangkurat, Banjarmasin, South Kalimantan, Indonesia)
Eko Suhartono (Department of Medical Chemistry and Biochemistry, Faculty of Medicine and Health Sciences, Universitas Lambung Mangkurat, Banjarbaru, South Kalimantan, Indonesia)
Hapsari Lintang Sekartaji (Veterinary Medicine Study Program, Faculty of Medicine and Health Sciences, Universitas Lambung Mangkurat, Banjarbaru, South Kalimantan,Indonesia)
Windy Yuliana Budianto (Department of Pediatric Nursing, Faculty of Medicine and Health Sciences, Universitas Lambung Mangkurat, Banjarmasin, South Kalimantan, Indonesia)

Dentino: Jurnal Kedokteran Gigi
Vol 11, No 2 (2026)

Background: Piper betle L. leaf contains bioactive compounds with potential benefits for oral health. Although its extracts and secondary metabolites have been extensively studied, peptides derived from Piper betle L. leaf proteins remain underexplored as antibacterial and anti-inflammatory agents. Purpose: This study aimed to identify and predict the antibacterial, anti-inflammatory, and toxicity profiles of theoretical peptides generated from a betel leaf protein sequence using a computational approach. Methods: A 561-amino-acid sesquiterpene synthase sequence attributed to Piper betle L. was retrieved from the UniProt database. In silico proteolysis was performed using trypsin, pepsin, and chymotrypsin to generate theoretical peptide fragments of 8–20 amino acids. These peptides represent putative digestion products derived computationally from the selected precursor protein and were not experimentally identified or quantified in Piper betle leaf tissue. Their physicochemical properties were assessed based on molecular weight, hydrophobicity, hydropathicity, hydrophilicity, charge, and isoelectric point. Antibacterial, anti-inflammatory, and toxicity potentials were predicted using AntiBP3, AIPpred, and ToxinPred3, respectively. Results: In silico proteolysis generated nine theoretical peptide sequences with molecular weights ranging from 849.00–1,508.64 Da. GDSPSGVQC, SDPTHETK, and QNIVVAASIICR were predicted to have antibacterial potential, while eight peptides showed anti-inflammatory potential. GDSPSGVQC and DHGCTEEEACMA were predicted to be toxic. SDPTHETK and QNIVVAASIICR showed the most favorable profiles, combining antibacterial and anti-inflammatory potential with predicted non-toxicity. Conclusion: SDPTHETK and QNIVVAASIICR were prioritized as putative peptide candidates based on their predicted antibacterial, anti-inflammatory, and non-toxic profiles. However, these findings are based exclusively on computational analyses and do not demonstrate actual biological activity. Experimental validation is required to confirm their endogenous occurrence, biological activity, safety, and mechanisms of action.