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- Multi-GPU-Based Detection of Protein Cavities using Critical PointsPublication . Dias, Sérgio; Nguyen, Quoc; Jorge, Joaquim A; Gomes, AbelProtein cavities are specific regions on the protein surface where ligands (small molecules) may bind. Such cavities are putative binding sites of proteins for ligands. Usually, cavities correspond to voids, pockets, and depressions of molecular surfaces. The location of such cavities is important to better understand protein functions, as needed in, for example, structure-based drug design. This article introduces a geometric method to detecting cavities on the molecular surface based on the theory of critical points. The method, called CriticalFinder, differs from other surface-based methods found in the literature because it directly uses the curvature of the scalar field (or function) that represents the molecular surface, instead of evaluating the curvature of the Connolly function over the molecular surface. To evaluate the accuracy of CriticalFinder, we compare it to other seven geometric methods (i.e., LIGSITE-CS, GHECOM, ConCavity, POCASA, SURFNET, PASS, and Fpocket). The benchmark results show that CriticalFinder outperforms those methods in terms of accuracy. In addition, the performance analysis of the GPU implementation of CriticalFinder in terms of time consumption and memory space occupancy was carried out.
- GPU-Based De- tection of Protein Cavities using Gaussian SurfacesPublication . Dias, Sérgio; Martins, Ana Mafalda; Nguyen, Quoc; Gomes, AbelProtein 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.
- Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A SurveyPublication . Simões, Tiago M. C.; Lopes, Daniel Simões; Dias, Sérgio Emanuel Duarte; Fernandes, Francisco; Jorge, Joaquim A; Pereira, João; Bajaj, Chandrajit; Gomes, AbelDetecting and analysing protein cavities provides significant information about active sites for biological processes (e.g. protein–protein or protein–ligand binding) in molecular graphics and modelling. Using the three‐dimensional (3D) structure of a given protein (i.e. atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution‐based, energy‐based and geometry‐based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere‐, grid‐ and tessellation‐based methods, but also surface‐based, hybrid geometric, consensus and time‐varying methods. Finally, we detail those techniques that have been customized for GPU (graphics processing unit) computing.
- CavVis - A field-of-view geometric algorithm for protein cavity detectionPublication . Simões, Tiago M. C.; Gomes, AbelSeveral geometric-based methods have been developed for the last two to three decades to detect and identify cavities (i.e., putative binding sites) on proteins, as needed to study protein–ligand interactions and protein docking. This paper introduces a new protein cavity method, called CavVis, which combines voxelization (i.e., a grid of voxels) and an analytic formulation of Gaussian surfaces that approximates the solvent-excluded surface. This method builds upon visibility of points on protein surface to find its cavities. Specifically, the visibility criterion combines three concepts we borrow from computer graphics, the field-of-view of each surface point, voxel ray casting, and back-face culling.