In silico identification of peptide as epidermal growth factor receptor tyrosine kinase inhibitors in lung cancer treatment

Background and Objective: Epidermal growth factor receptor (EGFR) is the biomarker for lung cancer in which the protein has the most active mutated genes in lung cancer patients. Peptides have pharmacological potential as drugs because of their bioactivity and accessibility. The research objective was to obtain peptide compounds drug candidates with good interaction and pharmacological properties that can act as an inhibitor for EGFR for lung cancer treatment by using in silico method. Materials and Methods: EGFR protein structure was obtained from Protein Data Bank and the peptide compounds were retrieved from PubChem. Optimization and energy minimization process were done to prepare the peptides for the simulation. Protein-Ligand Interaction Fingerprint (PLIF) was used to determine the pharmacophore features in the EGFR binding site. Both proteins and ligands underwent a virtual screening through rigid and flexible molecular docking simulation and the best ligands were subjected to a computational ADME-Tox properties prediction. Results: After screening through molecular docking simulation, nine best compounds were identified to have a good interaction with EGFR protein according to its binding energy and RMSD value. The compounds were identified to form hydrogen bond interactions with the macromolecule. Conclusion: Two peptide compounds (PubChem ID: 20832941 and 9805315) have been predicted as the best ligands with desired pharmacological properties for the inhibition of EGFR tyrosine kinase.