Computational Prediction of Resistance Induced Alanine-Mutation in ATP Site of Epidermal Growth Factor Receptor

Abstract

Epidermal growth factor receptor (EGFR) resistance to tyrosine kinase inhibitors can cause low survival rates in mutation-positive non-small cell lung cancer patients. It is necessary to predict new mutations in the development of more potent EGFR inhibitors since classical and rare mutations observed were known to affect the effectiveness of the therapy. Therefore, this research aimed to perform alanine mutagenesis scanning on ATP binding site residues without COSMIC data, followed by molecular dynamic simulations to determine their molecular interactions with ATP and erlotinib compared to wild-type complexes. Based on the result, eight mutations were found to cause changes in the binding energy of the ATP analogue to become more negative. These included G779A, Q791A, L792A, R841A, N842A, V843A, I853A, and D855A, which were predicted to enhance the affinity of ATP and reduce the binding ability of inhibitors with the same interaction site. Erlotinib showed more positive energy among G779A, Q791A, I853A, and D855A, due to their weaker binding energy than ATP. These four mutations could be anticipated in the development of the next inhibitor to overcome the incidence of resistance in lung cancer patients.

Keywords

• EGFR
• erlotinib
• mutation
• prediction
• resistance