Extraction of Robust Voids and Pockets in Proteins

Sridharamurthy, Raghavendra; Masood, Talha Bin; Doraiswamy, Harish; Patel, Siddharth; Varadarajan, Raghavan; Natarajan, Vijay

doi:10.1007/978-3-319-24523-2_15

Cited by 8 publications

(8 citation statements)

References 26 publications

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“…CAST is the basis of other methods and systems, namely: CASTp web server [BNL03], SplitPocket web server [TDCL09] and RobustVoids [SDP*13], just to mention a few of them. CASTp is also based on the theory of α‐shapes, and arguably can detect all pockets and voids of a given protein, as well as the surface atoms participating at each cavity.…”

Section: Tessellation‐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: Tessellation‐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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“…Sridharamurthy et al [SDP*13] also used the α‐complex to identify robust voids and pockets, which are stable with respect to small perturbations in the atomic radii. The notion of robust voids is based on the stability and the topological persistence with respect to the α‐complex.…”

Section: Algorithms For the Extraction Of Cavitiesmentioning

confidence: 99%

Visual Analysis of Biomolecular Cavities: State of the Art

Krone

Kozlíková

Lindow

et al. 2016

Computer Graphics Forum

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International audienceIn this report we review and structure the branch of molecular visualization that is concerned with the visual analysis of cavities in macromolecular protein structures. First the necessary background, the domain terminology, and the goals of analytical reasoning are introduced. Based on a comprehensive collection of relevant research works, we present a novel classification for cavity detection approaches and structure them into four distinct classes: grid-based, Voronoi-based, surface-based, and probe-based methods. The subclasses are then formed by their combinations. We match these approaches with corresponding visualization technologies starting with direct 3D visualization, followed with non-spatial visualization techniques that for example abstract the interactions between structures into a relational graph, straighten the cavity of interest to see its profile in one view, or aggregate the time sequence into a single contour plot. We also discuss the current state of methods for the visual analysis of cavities in dynamic data such as molecular dynamics simulations. Finally, we give an overview of the most common tools that are actively developed and used in the structural biology and biochemistry research. Our report is concluded by an outlook on future challenges in the field

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“…In this paper, we report the development of a method that applies cycle analysis from geometric graph theory to the positions of atomic-column centers and is capable of detecting a wide range of defects in STEM images with no prior knowledge of the material. Although graph theoretical techniques have been used previously for the segmentation of spatial regions and identification of voids in imaged materials [18][19][20], these applications do not necessarily provide information on slight structural deviations in the imaged material at an atomic-column by atomic-column level of detail. In graph theory, a cycle is a path between points that connects a point back to itself.…”

Section: Introductionmentioning

confidence: 99%

Detection of defects in atomic-resolution images of materials using cycle analysis

Ovchinnikov

O’Hara

Jesse

et al. 2020

Adv Struct Chem Imag

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The automated detection of defects in high-angle annular dark-field Z-contrast (HAADF) scanning-transmissionelectron microscopy (STEM) images has been a major challenge. Here, we report an approach for the automated detection and categorization of structural defects based on changes in the material's local atomic geometry. The approach applies geometric graph theory to the already-found positions of atomic-column centers and is capable of detecting and categorizing any defect in thin diperiodic structures (i.e., "2D materials") and a large subset of defects in thick diperiodic structures (i.e., 3D or bulk-like materials). Despite the somewhat limited applicability of the approach in detecting and categorizing defects in thicker bulk-like materials, it provides potentially informative insights into the presence of defects. The categorization of defects can be used to screen large quantities of data and to provide statistical data about the distribution of defects within a material. This methodology is applicable to atomic column locations extracted from any type of high-resolution image, but here we demonstrate it for HAADF STEM images.

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Extraction of Robust Voids and Pockets in Proteins

Cited by 8 publications

References 26 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

Visual Analysis of Biomolecular Cavities: State of the Art

Detection of defects in atomic-resolution images of materials using cycle analysis

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