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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.
- CavBench: a benchmark for protein cavity detection methodsPublication . Dias, Sérgio; Simões, Tiago M. C.; Fernandes, Francisco; Martins, Ana Mafalda; Ferreira, Alfredo; Jorge, Joaquim A; Gomes, AbelExtensive research has been applied to discover new techniques and methods to model protein-ligand interactions. In particular, considerable efforts focused on identifying candidate binding sites, which quite often are active sites that correspond to protein pockets or cavities. Thus, these cavities play an important role in molecular docking. However, there is no established benchmark to assess the accuracy of new cavity detection methods. In practice, each new technique is evaluated using a small set of proteins with known binding sites as ground-truth. However, studies supported by large datasets of known cavities and/or binding sites and statistical classification (i.e., false positives, false negatives, true positives, and true negatives) would yield much stronger and reliable assessments. To this end, we propose CavBench, a generic and extensible benchmark to compare different cavity detection methods relative to diverse ground truth datasets (e.g., PDBsum) using statistical classification methods.