Benchmarking the Ligand–HER2 Interactions Using Machine Learning and Molecular Dynamics Simulations

Understanding the inhibitor-HER2 interaction mechanism remains a critical challenge in combating breast cancer. In the present work, the role of five critical residues that are deeply located in the HER2 active site was recognized. To win the race against time in learning the activities of the HER2 tyrosine kinase protein, we employed a stepwise computational procedure including a machine learning predictive regression model, atomistic molecular dynamics (MD) simulations, and the umbrella sampling MD method. A systematic mining of a data set of 8 million chemical compounds allowed us to finally identify 13 candidates whose capacities as anti-HER2 have not been reported before. Based on the computed results, a benchmark for the strength of the ligand-HER2 interaction has been established. Although van der Waals potential energy tends to stabilize ligand-protein associations, the ligand that electrostatically interacts with five residues, Lys753, Leu796, Thr798, Asp863, and Asp880, is a key factor in deciding the inhibitor strength. Significantly, the strong binding of compound lig233 was exemplified by its ability to form hydrogen bonds with Asp863 and Asp880 and maintain exceptionally short distances to many key residues, indicating the formation of strong chemical bonds. Lig233 also exhibits a binding free energy of -47 kcal/mol, two times as large as that of -21 kcal/mol for the known drug lapatinib. The fresh understanding achieved in the present study can lead to the necessary adjustments in the experimental development of HER2 inhibitors.