Exploring conformational dynamics of the HER2 DFG-flip using machine-learning-guided metadynamics for type II inhibitor design.

Abstract

Protein kinases regulate cellular proliferation and survival through tightly controlled signalling mechanisms, and their dysregulation is a major driver of cancer. Human epidermal growth factor receptor 2 (HER2) is a clinically validated kinase target, yet structural and drug-discovery efforts have largely focused on type I inhibitors binding the active DFG-in conformation. The absence of experimentally resolved structures for HER2 in the inactive DFG-out state has limited structure-based development of inactive-state type II inhibitors. Here, we employ molecular dynamics (MD) simulations coupled with machine-learning-guided well-tempered metadynamics (MetaD) to characterize the conformational landscape underlying the DFG-in to DFG-out transition in HER2. Deep learning-based collective variable was constructed by the Deep Targeted Discriminant Analysis (DeepTDA) method employing unbiased MD data from both metastable states. DeepTDA enabled efficient sampling of the DFG-flip pathway, capturing multiple recrossing events and allowing reliable estimation of the free energy difference between the two states within accessible simulation time. The inactive DFG-out conformation is found to be energetically favoured by approximately 25 kJ/mol relative to the DFG-in state, in close agreement with experimental and computational data from other apo kinases. In addition to the canonical active conformation observed crystallographically, we identify an alternative DFG-in conformer that may contribute to the low intrinsic kinase activity reported for HER2. A DFG-up intermediate conformer is also observed, resembling crystal structures reported for Aurora-A kinase. A combined covalent docking and molecular dynamics approach followed to assess the MetaD-predicted DFG-out structure for ligand binding reliability, yielding comparable predicted/experimental binding affinity. Together, these results provide a structurally grounded inactive-state HER2 model and a mechanistic framework for structure-based design of HER2 inhibitors targeting inactive kinase conformations, highlighting the applicability of deep learning CVs to systems of complex free energy landscape.