Modeling pH-Dependent Protein Dynamics by Integrating Coarse-Grained Molecular Simulation and the Deep Neural Network...

The pK_a values of titratable residues are fundamental parameters that shape protein behavior in a given solution environment. They govern electrostatic interactions and thereby influence protein folding, conformational dynamics, and molecular recognition. Existing pK_a-prediction algorithms typically rely on detailed all-atom structures. However, although coarse-grained models are widely used to simulate protein structure and dynamics, estimating pK_a values within these frameworks remains challenging because ionizable groups are often not explicitly represented. In this work, we introduce DeepCGpKa, a deep-learning-based pK_a predictor designed for coarse-grained protein structures. Benchmark tests show that DeepCGpKa attains accuracy comparable to state-of-the-art methods based on all-atom structures. It also retains robust predictive performance for partially unfolded protein structures. When coupled with coarse-grained molecular simulations, DeepCGpKa successfully captures the pH dependence of protein conformational changes. Integrating data-driven pK_a prediction with physics-based molecular simulations provides a practical route to improve the treatment of electrostatic interactions at the coarse-grained level, which is a critical issue for most currently available coarse-grained biomolecular models.