Divide-and-Conquer Strategy for Large-Scale Eulerian Solvent Excluded Surface

TL;DR

This paper presents a divide-and-conquer, out-of-core, parallel algorithm that significantly enhances the efficiency and scalability of Eulerian solvent excluded surface computation for large biomolecules, enabling practical use on standard hardware.

Contribution

The authors introduce a novel divide-and-conquer approach for ESES that reduces memory usage and computation time, allowing large biomolecular surfaces to be computed efficiently on commodity hardware.

Findings

Reduces memory footprint for large biomolecules

Speeds up computation through parallelization

Validates improved efficiency with extensive tests

Abstract

Motivation: Surface generation and visualization are some of the most important tasks in biomolecular modeling and computation. Eulerian solvent excluded surface (ESES) software provides analytical solvent excluded surface (SES) in the Cartesian grid, which is necessary for simulating many biomolecular electrostatic and ion channel models. However, large biomolecules and/or fine grid resolutions give rise to excessively large memory requirements in ESES construction. We introduce an out-of-core and parallel algorithm to improve the ESES software. Results: The present approach drastically improves the spatial and temporal efficiency of ESES. The memory footprint and time complexity are analyzed and empirically verified through extensive tests with a large collection of biomolecule examples. Our results show that our algorithm can successfully reduce memory footprint through a straightforward divide-and-conquer strategy to perform the calculation of arbitrarily large proteins on a typical commodity personal computer. On multi-core computers or clusters, our algorithm can reduce the execution time by parallelizing most of the calculation as disjoint subproblems. Various comparisons with the state-of-the-art Cartesian grid based SES calculation were done to validate the present method and show the improved efficiency. This approach makes ESES a robust software for the construction of analytical solvent excluded surfaces. Availability and implementation: http://weilab.math.msu.edu/ESES.