GPU-based detection of protein cavities using Gaussian surfaces

Abstract

BackgroundProtein cavities play a key role in biomolecular recognition and function, particularly in protein-ligand interactions, as usual in drug discovery and design. Grid-based cavity detection methods aim at finding cavities as aggregates of grid nodes outside the molecule, under the condition that such cavities are bracketed by nodes on the molecule surface along a set of directions (not necessarily aligned with coordinate axes). Therefore, these methods are sensitive to scanning directions, a problem that we call cavity ground-and-walls ambiguity, i.e., they depend on the position and orientation of the protein in the discretized domain. Also, it is hard to distinguish grid nodes belonging to protein cavities amongst all those outside the protein, a problem that we call cavity ceiling ambiguity.ResultsWe solve those two ambiguity problems using two implicit isosurfaces of the protein, the protein surface itself (called inner isosurface) that excludes all its interior nodes from any cavity, and the outer isosurface that excludes most of its exterior nodes from any cavity. Summing up, the cavities are formed from nodes located between these two isosurfaces. It is worth noting that these two surfaces do not need to be evaluated (i.e., sampled), triangulated, and rendered on the screen to find the cavities in between; their defining analytic functions are enough to determine which grid nodes are in the empty space between them.ConclusionThis article introduces a novel geometric algorithm to detect cavities on the protein surface that takes advantage of the real analytic functions describing two Gaussian surfaces of a given protein.

Predicting binding sites from unbound versus bound protein structures

“…Additionally, the number of sequence-related or sequence-unique proteins contained in the CavDataset is not specified by the authors. The authors contrast the performance of four prediction methods (Fpocket 21 , GuassianFinder 24 , Ghecom 25 , and KVFinder 26 ) on their CavDataset based on different classifications of pockets, as well as the apo or holo nature of the starting structures. The performance of all four methods appears completely unaffected by the presence or absence of ligands in the starting structure.…”

Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey

“…Larger proteins should lead to longer voxel length [VGGR10], and thus a less number of voxels, as well as an increasing of time performance. Recall that the time complexity of any algorithm based on a 3D grid is cubic unless one uses parallel computing [DG17].The second limitation concerns protein‐orientation sensitivity (POS). This means that a distinct orientation of the protein within the grid may result in finding a distinct set of cavities on the same protein surface [BAM*14].…”

“…It is clear that cavity detection algorithms usually start with the reading of the set of atoms in memory, that is, they inherently use the vdW surface. But, as explained throughout the paper, there is a trend to use analytical surfaces like SES and Gaussian surfaces to detect cavities using geometric properties as of differential geometry, as is usual in segmentation techniques studied in computer graphics [PTRV12] [DG17].…”

A Comprehensive Mapping of the Druggable Cavities within the SARS-CoV-2 Therapeutically Relevant Proteins by Combining Pocket and Docking Searches as Implemented in Pockets 2.0

“…Hence, it comes as no surprise that a remarkable number of computational approaches have been proposed in the last years with a view to mapping the cavities within a given protein structure and to evaluating the resulting druggability. They comprise methods based on Voronoi tessellation [4], grid search [5], surface analysis [6], void sphere clustering [7] or they can involve various combinations of these [8]. Moreover, the increasing number of experimentally resolved protein structures allows a markedly more accurate validation of these methods [9].…”