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Cavities are structural features of proteins and they are often important for drug discovery and design. There are different kinds of cavities, including pockets, voids, tunnels, and channels. The types of cavities are listed in more detail in figure 1.
Cavities are structural features of proteins and they are often important for drug discovery and design. There are different kinds of cavities, including pockets, voids, tunnels, and channels. The types of cavities are listed in more detail in figure 1.


[[File:proteinCavityTypes.jpg|center|frame| Figure 1: The basic types of protein cavities]]


There are several methods for detecting protein cavities. There are sphere based methods, grid based methods, grid-and-sphere based methods, surface based methods, grid-and-surface based methods, time-varying methods, GPU based methods, and finally tessellation based methods. Tessellation based methods mainly rely on Voronoi tessellation. Some methods rely on weighted Voronoi tessellation because of its ability to more accurately represent the volume of atoms. The Voronoi tessellation in these methods is computed from a set of points representing the atomic centers of the underlying protein.
[[File:proteinCavityTypes.jpg|center|frame| Figure 1: The basic types of protein cavities [1]]]
 
 
Of course, the types of cavities listed in figure 1 are only the basic ones. Combinations of these are also possible as shown in figure 2.
 
 
[[File:proteinCavityAdvancedTypes.jpg|center|frame| Figure 2: Combination types of protein cavities [1]]]
 
 
There are several methods for detecting protein cavities. There are sphere based methods, grid based methods, grid-and-sphere based methods, surface based methods, grid-and-surface based methods, time-varying methods, GPU based methods, consensus based methods, and finally tessellation based methods. These algorithms are shown in more detail in figure 3. Tessellation based methods mainly rely on Voronoi tessellation. Some methods rely on weighted Voronoi tessellation because of its ability to more accurately represent the volume of atoms. The Voronoi tessellation in these methods is computed from a set of points representing the atomic centers of the underlying protein.
 
 
[[File:proteinCavityDetectionAlgorithms.jpg|center|frame| Figure 3: A hierarchy of protein cavity detection algorithms [1]]]
 
== References ==
[1] APA Simões, T., Lopes, D., Dias, S., Fernandes, F., Pereira, J., Jorge, J., Bajaj, C., & Gomes, A. (2017). Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey. Computer graphics forum : journal of the European Association for Computer Graphics, 36(8), 643–683. https://doi.org/10.1111/cgf.13158

Revision as of 12:18, 14 May 2020

Introduction

Cavities are structural features of proteins and they are often important for drug discovery and design. There are different kinds of cavities, including pockets, voids, tunnels, and channels. The types of cavities are listed in more detail in figure 1.


Figure 1: The basic types of protein cavities [1]


Of course, the types of cavities listed in figure 1 are only the basic ones. Combinations of these are also possible as shown in figure 2.


Figure 2: Combination types of protein cavities [1]


There are several methods for detecting protein cavities. There are sphere based methods, grid based methods, grid-and-sphere based methods, surface based methods, grid-and-surface based methods, time-varying methods, GPU based methods, consensus based methods, and finally tessellation based methods. These algorithms are shown in more detail in figure 3. Tessellation based methods mainly rely on Voronoi tessellation. Some methods rely on weighted Voronoi tessellation because of its ability to more accurately represent the volume of atoms. The Voronoi tessellation in these methods is computed from a set of points representing the atomic centers of the underlying protein.


Figure 3: A hierarchy of protein cavity detection algorithms [1]

References

[1] APA Simões, T., Lopes, D., Dias, S., Fernandes, F., Pereira, J., Jorge, J., Bajaj, C., & Gomes, A. (2017). Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey. Computer graphics forum : journal of the European Association for Computer Graphics, 36(8), 643–683. https://doi.org/10.1111/cgf.13158