The transmembrane complex formed by TREM2 (Triggering Receptor Expressed on Myeloid cells 2) and DAP12 (DNAX-activating protein of 12 kDa) constitutes a pivotal therapeutic target for Alzheimer's disease. However, the intrinsic plasticity of its interface poses a formidable challenge for structure-based discovery of protein-protein interaction stabilizers (PPI stabilizers). Conventional approaches, ranging from static molecular docking to geometric deep learning, fail to capture the subtle interfacial energetics required for stabilizing this assembly, often leading to erroneous activity predictions. Here, we present MechMemDyn, a novel predictive framework that uniquely integrates protein frustration analysis with membrane-embedded molecular dynamics (MD) simulations. To our knowledge, this is the first systematic application of frustration analysis to rationalize PPI-stabilizing activity and guide ligand design. By mapping the local frustration landscape, we identify critical, minimally frustrated contact networks at the protein interface, which are essential for stabilizing the PPI. We demonstrate that the ability of a ligand to dampen distance fluctuations within these key networks, a metric rooted in thermodynamic rigor, correlates strongly with experimental potency. This method outperforms conventional static docking, AI-driven dynamic docking approaches, and standard molecular simulations, providing a more accurate and reproducible basis for cross-ligand comparison. This work not only resolves the intractability of the TREM2-DAP12 complex but also establishes a physics-driven paradigm for targeting dynamic transmembrane interfaces via frustration-optimized PPI stabilizers.
Cao et al. (Mon,) studied this question.