The solvent-excluded surface of proteins is extremely useful when studying their properties and interactions as it represents the portion of the outer protein contour that is available to interact with the solvent and other molecules. Given their simplicity and ability to represent geometrical and physico- chemical properties of proteins, voxelised surface representations have received a lot of interest in bioinformatics and computational biology applications such as protein-protein docking, interaction interface prediction and ligand-binding pocket prediction. Computing voxelised surfaces for large proteins can be challenging, as space-demanding data structures with associated high computational costs are required. In this paper we present a fast, OpenMP-based parallel algorithm for the computation of high-resolution voxelised solvent-excluded protein surfaces. The methodology is based on a region-growing implementation of the approximate Euclidean Distance Transform algorithm with Hierarchical Queues. The geometrical relationship between the solvent-accessible and solvent-excluded surfaces allows us to obtain the latter very efficiently by computing distance map values only for a small subset of the overall voxels representing the protein. The algorithm computes the contribution to the overall outer surface for each atom in parallel. The proposed methodology was experimentally compared to two previous MPI- based parallel implementations showing overall better speedup and efficiency metrics as well as lower surface computation times.

Fast Computation of High-resolution Solvent Excluded Protein Surface with OpenMP

Sebastian Daberdaku
;
Carlo Ferrari
2018

Abstract

The solvent-excluded surface of proteins is extremely useful when studying their properties and interactions as it represents the portion of the outer protein contour that is available to interact with the solvent and other molecules. Given their simplicity and ability to represent geometrical and physico- chemical properties of proteins, voxelised surface representations have received a lot of interest in bioinformatics and computational biology applications such as protein-protein docking, interaction interface prediction and ligand-binding pocket prediction. Computing voxelised surfaces for large proteins can be challenging, as space-demanding data structures with associated high computational costs are required. In this paper we present a fast, OpenMP-based parallel algorithm for the computation of high-resolution voxelised solvent-excluded protein surfaces. The methodology is based on a region-growing implementation of the approximate Euclidean Distance Transform algorithm with Hierarchical Queues. The geometrical relationship between the solvent-accessible and solvent-excluded surfaces allows us to obtain the latter very efficiently by computing distance map values only for a small subset of the overall voxels representing the protein. The algorithm computes the contribution to the overall outer surface for each atom in parallel. The proposed methodology was experimentally compared to two previous MPI- based parallel implementations showing overall better speedup and efficiency metrics as well as lower surface computation times.
2018
2018 International Conference on High Performance Computing Simulation (HPCS)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3282895
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