Toward a Next Gen Toolkit for Modelling pH Effects in Proteins
Pedro Reis, PhD
Bayer AG
Capturing biomolecular pH effects is critical for understanding proton-coupled changes in structure, function, binding, and dynamics. This talk presents an integrated toolkit for modelling pH effects in proteins.
PypKa provides Poisson–Boltzmann/Monte Carlo-based pKₐ predictions from Protein Data Bank structures or MD snapshots through a flexible Python API with parallel execution. With it, we developed pKPDB which offers rapid access to >12 million precomputed site pKₐ estimates across ~120 k protein structures. Extending these capabilities to dynamics, PypKa-MD implements a streamlined stochastic-titration CpHMD approach compatible with GROMOS and CHARMM force fields and modern GROMACS versions, enabling simulations of pH-coupled conformational transitions. By leveraging pKPDB we trained pKAI to predict pKₐ shifts and pH-independent energies with physics-level accuracy while achieving >1000× faster inference than traditional continuum electrostatics methods.
Together, these developments constitute a next-generation, open, and extensible platform for providing insights into protonation equilibria. Remaining challenges, ongoing improvements, and opportunities will be discussed.