CUDA-based triangulations of convolution molecular surfaces

Dias, Sérgio; Bora, Kuldeep; Gomes, Abel

doi:10.1145/1851476.1851553

Cited by 20 publications

(19 citation statements)

References 20 publications

(17 reference statements)

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“…In the last decade, we have noted an increasing use of GPU computing in molecular modelling, rendering and visualization [KBE09, SSE*10, DBG10, LBH11, KKC*11, CVT*11, PTRV12, TPS12, PRV13, DG13, PTRV13, LLNW14, DCD*14, DG15, HGVV16]. However cavity detection methods taking advantage of GPU processing power are not so commonly found in the literature; the exception lies in the methods we describe below.…”

Section: Gpu‐based Methodsmentioning

confidence: 99%

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

Simões

Lopes

Dias

et al. 2017

Computer Graphics Forum

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Detecting and analyzing protein cavities provides significant information about active sites for biological processes (e.g., protein-protein or protein-ligand binding) in molecular graphics and modeling. Using the three-dimensional 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.

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Section: Gpu‐based Methodsmentioning

confidence: 99%

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

Simões

Lopes

Dias

et al. 2017

Computer Graphics Forum

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“…The graphics visualization of each protein requires the triangulation of the Gaussian molecular surface defined by F in ( x )= c . This triangulation is carried out entirely on GPU side using the variant of the marching cubes algorithm introduced by Dias and Gomes [ 31 – 34 ].…”

Section: Methodsmentioning

confidence: 99%

GPU-based detection of protein cavities using Gaussian surfaces

et al. 2017

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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.

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“…As a result, we avoid the need to store in the GPU global memory the five large arrays that are required for the global prefix sum operations used in [NVI15b, CJD15,DBG10]. As a result, we avoid the need to store in the GPU global memory the five large arrays that are required for the global prefix sum operations used in [NVI15b, CJD15,DBG10].…”

Section: Features Of Pmb That Contribute To Its Efficiencymentioning

confidence: 99%

Parallel Marching Blocks: A Practical Isosurfacing Algorithm for Large Data on Many‐Core Architectures

Liu

Clapworthy

Dong

et al. 2016

Computer Graphics Forum

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Interactive isosurface visualisation has been made possible by mapping algorithms to GPU architectures. However, current state-of-the-art isosurfacing algorithms usually consume large amounts of GPU memory owing to the additional acceleration structures they require. As a result, the continued limitations on available GPU memory mean that they are unable to deal with the larger datasets that are now increasingly becoming prevalent. This paper proposes a new parallel isosurface-extraction algorithm that exploits the blocked organisation of the parallel threads found in modern many-core platforms to achieve fast isosurface extraction and reduce the associated memory requirements. This is achieved by optimising thread co-operation within thread-blocks and reducing redundant computation; ultimately, an indexed triangular mesh can be produced. Experiments have shown that the proposed algorithm is much faster (up to 10×) than state-of-the-art GPU algorithms and has a much smaller memory footprint, enabling it to handle much larger datasets (up to 64×) on the same GPU.

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CUDA-based triangulations of convolution molecular surfaces

Cited by 20 publications

References 20 publications

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

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

GPU-based detection of protein cavities using Gaussian surfaces

Parallel Marching Blocks: A Practical Isosurfacing Algorithm for Large Data on Many‐Core Architectures

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