PowerBorn: A Barnes-Hut Tree Implementation for Accurate and Efficient Born Radii Computation

Area of Science:

• Computational biophysics
• Molecular modeling
• Computational chemistry

Background:

• Implicit solvent models are crucial in computational biophysics.
• Poisson-Boltzmann (PB) methods are accurate but computationally expensive.
• Generalized Born (GB) models offer efficiency but have limitations.

Purpose of the Study:

• To address the performance limitations of advanced Generalized Born models.
• To develop a computationally efficient algorithm for Born radii calculation.
• To maintain accuracy comparable to Poisson-Boltzmann methods.

Main Methods:

• Implementation of a novel algorithm for Born radii computation.
• Utilizing a Barnes-Hut tree code scheme for efficiency.
• Comparison of results with established Poisson-Boltzmann computations.

Main Results:

• The new algorithm accurately computes Born radii.
• Accurate polar solvation free energies were obtained.
• Performance improvement of up to an order of magnitude over existing methods was achieved.

Conclusions:

• The Barnes-Hut based algorithm enhances Generalized Born model efficiency.
• This method provides a viable alternative to computationally intensive Poisson-Boltzmann calculations.
• The C++ implementation will be publicly available.