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

The pKa 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 pKa-prediction algorithms typically rely on detailed all-atom structures. However, although coarse-grained models are widely used to simulate protein structure and dynamics, estimating pKa values within these frameworks remains challenging because ionizable groups are often not explicitly represented. In this work, we introduce DeepCGpKa, a deep-learning-based pKa 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 pKa 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.