Accurate and Scalable Continuum Electrostatics for Large Biomolecular Systems: The pyDelPhi Poisson-Boltzmann...

Area of Science:

• Computational biophysics
• Molecular modeling
• Biomolecular simulations

Background:

• Electrostatic interactions are crucial for biomolecular function.
• Traditional Poisson-Boltzmann equation solvers struggle with large systems.
• Efficient computation is vital for drug discovery and materials science.

Purpose of the Study:

• Introduce pyDelPhi, a high-performance Poisson-Boltzmann equation solver.
• Enhance accuracy, scalability, and reproducibility in biomolecular electrostatics.
• Enable efficient modeling of large-scale biomolecular systems.

Main Methods:

• Finite-difference Poisson-Boltzmann equation framework implemented in Python.
• Just-in-time compilation with Numba and CUDA backend for GPU acceleration.
• Support for multiple dielectric models and multiprecision execution.

Main Results:

• pyDelPhi achieves 7-20x speedup on GPUs for linearized PBE.
• Reproduces DelPhi energies within 0.1% accuracy.
• Cuboidal grid-box option reduces memory by up to 80% and accelerates anisotropic systems.
• Viral capsid calculation shows order-of-magnitude speedup with high numerical agreement.

Conclusions:

• pyDelPhi provides a unified, extensible platform for continuum electrostatics.
• Advances high-performance, reproducible modeling of biomolecular systems.
• Facilitates routine and large-scale biomolecular simulations.