Functional annotation and peptide-based drug discovery from Plectranthus zeylanicus leaf lectin reveal a potential EGFR-targeting therapeutic candidate

Abstract

This study presents a comprehensive bioinformatics analysis of peptide sequences derived from selected protein datasets, emphasizing their functional annotation and structural characterization. A total of 25 peptide sequences were retrieved and subjected to multi-layered computational evaluation to elucidate their biological roles, physicochemical properties, and structural integrity. Functionally, domain prediction using InterProScan and Pfam revealed that 72% of the peptides contained conserved motifs associated with antimicrobial and immunomodulatory activities. Signal peptide analysis via SignalP 6.0 identified 16 peptides (64%) with secretory potential, while TMHMM predicted transmembrane helices in 28% of the sequences, suggesting membrane-associated functionality. Gene Ontology (GO) mapping classified 88% of the peptides under biological processes such as defense response, cell signaling, and metabolic regulation. KEGG pathway enrichment linked 14 peptides to immune-related pathways, including cytokine-cytokine receptor interaction and Toll-like receptor signaling. Physicochemical profiling using ProtParam indicated that 80% of the peptides had molecular weights ranging from 1.2 to 3.5 kDa, with isoelectric points (pI) between 6.8 and 9.5, suggesting moderate basicity. The average instability index was 34.2, classifying most peptides as stable. Hydrophobicity scores (GRAVY) ranged from –0.45 to +0.32, indicating a balanced distribution of polar and non-polar residues. Structurally, homology modeling via SWISS-MODEL and I-TASSER yielded high-confidence 3D models for 21 peptides, with QMEAN scores above 0.6 and Ramachandran plot validation showing >90% residues in favored regions. Secondary structure prediction using PSIPRED revealed that 60% of the peptides predominantly formed α-helices, while 24% exhibited β-sheet-rich conformations. Molecular docking simulations with immune receptors (e.g., TLR4, MHC-I) demonstrated binding affinities ranging from –6.2 to –9.1 kcal/mol, with 12 peptides showing strong interaction profiles. This integrative approach combining functional annotation with structural modeling provides a robust framework for identifying bioactive peptides with therapeutic potential. The quantified insights support their application in immunotherapy, antimicrobial design, and peptide-based diagnostics, paving the way for experimental validation and translational research.