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Exploring conformational dynamics of the HER2 DFG-flip using machine-learning-guided metadynamics for type II
Muhammad I Ismail1, Mai Adel2, Eman M E Dokla2
1Department of Pharmaceutical Chemistry, Faculty of Pharmacy, The British University in Egypt, El-Sherouk City, Egypt.
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.
Insights
Researchers used advanced simulations to model the inactive DFG-out state of Human Epidermal Growth Factor Receptor 2 (HER2). This provides a new framework for designing targeted cancer drugs by understanding HER2
Area of Science:
- Computational Biology and Biochemistry
- Structural Biology
- Drug Discovery
Background:
- Protein kinases, like Human Epidermal Growth Factor Receptor 2 (HER2), are crucial for cell regulation, and their malfunction drives cancer.
- Current HER2 drug development primarily targets the active DFG-in conformation, overlooking the inactive DFG-out state due to a lack of structural data.
- Developing inhibitors for the inactive HER2 state is hindered by the absence of experimentally resolved structures.
Purpose of the Study:
- To characterize the conformational landscape of HER2, specifically the transition between the DFG-in and DFG-out states.
- To generate a reliable structural model of the inactive DFG-out conformation of HER2 for structure-based drug design.
- To explore the utility of deep learning in sampling complex conformational transitions in kinases.
Main Methods:
- Employed molecular dynamics (MD) simulations combined with machine-learning-guided well-tempered metadynamics (MetaD) to sample HER2 conformations.
- Utilized Deep Targeted Discriminant Analysis (DeepTDA) to construct a deep learning-based collective variable for efficient sampling of the DFG-flip pathway.
- Performed covalent docking and MD simulations to assess the ligand-binding reliability of the predicted DFG-out structure.
Main Results:
- Successfully sampled the DFG-in to DFG-out transition, revealing the inactive DFG-out conformation is energetically favored by ~25 kJ/mol.
- Identified an alternative DFG-in conformer and a DFG-up intermediate conformer, offering insights into HER2's basal activity and kinase dynamics.
- Validated the predicted DFG-out structure through docking, showing comparable binding affinity to experimental data.
Conclusions:
- Developed a structurally grounded model of inactive HER2, providing a mechanistic framework for designing type II inhibitors targeting this conformation.
- Demonstrated the effectiveness of deep learning-based collective variables (CVs) in efficiently characterizing complex free energy landscapes of kinases.
- The findings facilitate structure-based design of novel HER2 inhibitors targeting inactive conformations, potentially overcoming resistance mechanisms.
