Predicting the Molecular Mechanism of EGFR Domain II Dimer Binding Interface by Machine Learning to Identify Potent Small Molecule Inhibitor for Treatment of Cancer

Epidermal growth factor receptor (EGFR) is a transmembrane druggable target controlling cellular differentiation, proliferation, migration, survival and invasion. EGFR activation mainly occurs by its homo/hetro dimerization molecular phenomenon leading to tumor development and invasion. Several tyrosine kinase based inhibitors were discovered as potent anti-cancer drugs. However, mutations in its kinase domain confer resistance to most of these drugs. To overcome this drug resistance, development of small molecule inhibitors disrupting the EGFR Domain II dimer binding by machine learning methods are promising. Based on this insight, a structure-based drug repurposing strategy was adopted to repurpose the existing FDA approved drugs in blocking the EGFR Domain II mediated dimerization. We identified five best repurposed drug molecules showing good binding affinity at its key arm-cavity dimer interface residues by different machine learning methods. The molecular mechanisms of action of these repurposed drugs were computationally validated by molecular electrostatics potential mapping, point mutations at the dimer arm-cavity binding interface, molecular docking and receptor interaction studies. The present machine learning strategy thus forms the basis of identifying potent and putative small molecule drugs for the treatment of different types of cancer.