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

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dc.contributor.authorSimões, Tiagoen_US
dc.contributor.authorLopes, Danielen_US
dc.contributor.authorDias, Sérgioen_US
dc.contributor.authorFernandes, Franciscoen_US
dc.contributor.authorPereira, Joãoen_US
dc.contributor.authorJorge, Joaquimen_US
dc.contributor.authorBajaj, Chandrajiten_US
dc.contributor.authorGomes, Abelen_US
dc.contributor.editorChen, Min and Zhang, Hao (Richard)en_US
dc.date.accessioned2018-01-10T07:43:26Z
dc.date.available2018-01-10T07:43:26Z
dc.date.issued2017
dc.description.abstractDetecting 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.Detecting 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.en_US
dc.description.documenttypestar
dc.description.number8
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume36
dc.identifier.doi10.1111/cgf.13158
dc.identifier.issn1467-8659
dc.identifier.pages643-683
dc.identifier.urihttps://doi.org/10.1111/cgf.13158
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13158
dc.publisher© 2017 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectbiological modelling
dc.subjectmodelling
dc.subjectgeometric modelling
dc.subjectcomputational geometry
dc.subjectI.3.5 [Computer Graphics]: Computational Geometry and Object Modeling; I.3.8 [Computer Graphics]: Applications – Molecular Graphics; J.3 [Life and Medical Sciences]: Biology and Genetics – Computational Biology
dc.titleGeometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Surveyen_US
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