Computing voxelised representations of macromolecular surfaces

Abstract

Voxel-based representations of surfaces have received a lot of interest in bioinformatics and computational biology as a simple and effective way of representing geometrical and physicochemical properties of proteins and other biomolecules. Processing such surfaces for large molecules can be challenging, as space-demanding data structures with associated high computational costs are required. In this paper, we present a methodology for the fast computation of voxelised macromolecular surface representations (namely the van der Waals, solvent-accessible and solvent-excluded surfaces). The proposed method implements a spatial slicing procedure on top of compact data structures to efficiently calculate the three molecular surface representations at high-resolutions, in parallel. The spatial slicing protocol ensures a balanced workload distribution and allows the computation of the solvent-excluded surface with minimal synchronisation and communication between processes. This is achieved by adapting a multi-step region-growing EDT algorithm. At each step, distance values are first calculated independently for every slice, then, a small portion of the borders' information is exchanged between adjacent slices. Very little process communication is also required in the pocket detection procedure, where the algorithm distinguishes surface portions belonging to solvent-accessible pockets from cavities buried inside the molecule. Experimental results are presented to validate the proposed approach.

Identification of protein pockets and cavities by Euclidean Distance Transform

“…2). The SES computation algorithm is described in detail in (Daberdaku et al 2016(Daberdaku et al , 2018.…”

“…This paper presents a novel purely geometric algorithm for the detection of ligand binding protein pockets and cavities based on the Euclidean Distance Transform (EDT). The EDT can be used to compute the Solvent-Excluded surface for any given probe sphere radius value at high resolutions and in a timely manner (Daberdaku et al 2016(Daberdaku et al , 2018. The algorithm is adaptive to the specific candidate ligand: it computes two voxelised protein surfaces using two different probe sphere radii whose sizes depend on the shape of the candidate ligand.…”

Parallelism in computational biology

“…The parallel computation of voxelized macromolecular surfaces is tackled in the seventh paper, 'Computing voxelized representations of macromolecular surfaces: A parallel approach', by S Daberdaku and C Ferrari (see Daberdaku and Ferrari, 2016). Voxel-based representations of surfaces have received a lot of interest in computational biology as a simple and effective way of representing geometrical and physicochemical properties of proteins and other biomolecules.…”

EISA-Score: Element Interactive Surface Area Score for Protein–Ligand Binding Affinity Prediction

“…MSMS software was later developed to improve the speed and reliability of the SES calculation via the reduced surface . There are other efficient algorithms for generating SES. ,,,,,,,,,− ,, Among them, TMSmesh used the boundary element method and finite element method to handle arbitrary sizes of molecules. By adapting a multistep region-growing EDT approach, Daberdaku and Ferrari developed fast molecular surface representations for large molecules.…”

“…There are other efficient algorithms for generating SES. ,,,,,,,,,− ,, Among them, TMSmesh used the boundary element method and finite element method to handle arbitrary sizes of molecules. By adapting a multistep region-growing EDT approach, Daberdaku and Ferrari developed fast molecular surface representations for large molecules. Hermosilla et al utilized interactive GPU power to accelerate SES rendering at a fractional cost.…”