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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.
- Geometric representation and detection methods of cavities on protein surfacesPublication . Dias, Sérgio Emanuel Duarte; Gomes, Abel João PadrãoMost living organisms are made up of cells, while cells are composed by molecules. Molecules play a fundamental role in biochemical processes that sustain life. The functions of a molecule depend not only on its interaction with other molecules, but also on the sites of its surface where such interactions take place. Indeed, these interactions are the driving force of almost all cellular processes. Interactions between molecules occur on specific molecular surface regions, called binding sites. The challenge here is to know the compatible sites of two coupling molecules. The compatibility is only effective if there is physico-chemical compatibility, as well as geometric compatibility in respect to docking of shape between the interacting molecules. Most (but not all) binding sites of a molecule (e.g., protein) correspond to cavities on its surface; conversely, most (but not all) cavities correspond to binding sites. This thesis essentially approaches cavity detection algorithms on protein surfaces. This means that we are primarily interested in geometric methods capable of identifying protein cavities as tentative binding sites for their ligands. Finding protein cavities has been a major challenge in molecular graphics and modeling, computational biology, and computational chemistry. This is so because the shape of a protein usually looks very unpredictable, with many small downs and ups. These small shape features on the surface of a protein are rather illusive because they are too small when compared to cavities as tentative binding sites. This means that the concept of curvature (a local shape descriptor) cannot be used as a tool to detect those cavities. Thus, more enlarged shape descriptors have to be used to succeed in determining such cavities on the surface of proteins. In this line of thought, this thesis explores the application of mathematical theory of scalar fields, including its topology, as the cornerstone for the development of cavity detection algorithms described herein. Furthermore, for the purpose of graphic visualisation, this thesis introduces a GPU-based triangulation algorithm for molecular surfaces.
- 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.
- Rendering molecular surfaces as implicit surfaces on GPUsPublication . Dias, Sérgio Emanuel Duarte; Gomes, Abel João PadrãoModeling molecular surfaces enables us to extract useful information about interactions with other molecules, as well as measurements of molecular areas and volumes. Many types of algorithms have been developed to represent and rendering molecular surfaces. However, these algorithms have questionable time performance in the visualization of molecular surfaces because they are usually designed to run CPU. A possible solution to resolve this problem is the use of parallel computing, but parallel computing systems are in general very expensive. Fortunately, the appearance of the new generation of low-cost programmable GPUs with massive computational power can, in principle, solve this problem. So, in this thesis we present a GPU-based algorithm to speed up the rendering of molecular surfaces. Besides we carry out a study that compares a sequential version (CPU) to a parallel version (GPU) of well-know Marching Cubes (MC) algorithm to render Connolly surface, as well as van der Waals surfaces.