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

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 locatio...

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Bibliographic Details
Published in:Computer graphics forum 2017-12, Vol.36 (8), p.643-683
Main Authors: Simões, Tiago Miguel Carrola, Lopes, Daniel Simões, Dias, Sérgio, Fernandes, Francisco, Jorge, Joaquim, Pereira, João, Bajaj, Chandrajit, Padrão Gomes, Abel João
Format: Article
Language:English
Subjects:
Algorithms
Biological activity
biological modelling
computational geometry
Evolutionary algorithms
Geometric algorithms
Geometric detection of protein pockets
geometric modelling
Graphics processing units
Grooves
Holes
I.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
modelling
Protein cavity
Protein cavity detection
Protein pocket
Proteins
Taxonomy
Tessellation
Three dimensional models
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Summary: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. 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.
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.13158