Publication details
CAVER: Algorithms for Analyzing Dynamics of Tunnels in Macromolecules
| Authors | |
|---|---|
| Year of publication | 2016 |
| Type | Article in Periodical |
| Magazine / Source | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
| MU Faculty or unit | |
| Citation | |
| Web | http://loschmidt.chemi.muni.cz/peg/wp-content/uploads/2015/08/tcbb15.pdf |
| Doi | http://dx.doi.org/10.1109/TCBB.2015.2459680 |
| Field | Informatics |
| Keywords | tunnel; pore; channel; pathway; macromolecule; molecular dynamics; CAVER; Voronoi diagram; Delaunay triangulation; average link hierarchical clustering |
| Description | The biological function of a macromolecule often requires that a small molecule or ion is transported through its structure. The transport pathway often leads through void spaces in the structure. The properties of transport pathways change significantly in time; therefore the analysis of a trajectory from molecular dynamics rather than of a single static structure is needed for understanding the function of pathways. The identification and analysis of transport pathways are challenging because of the high complexity and diversity of macromolecular shapes, the thermal motion of their atoms, and the large amount of conformations needed to properly describe conformational space of protein structure. In this paper, we describe the principles of the CAVER 3.0 algorithms for the identification and analysis of properties of transport pathways both in static and dynamic structures. Moreover, we introduce the improved clustering solution for finding tunnels in macromolecules, which is included in the latest CAVER 3.02 version. Voronoi diagrams are used to identify potential pathways in each snapshot of a molecular dynamics trajectory and clustering is then used to find the correspondence between tunnels from different snapshots. Furthermore, the geometrical properties of pathways and their evolution in time are computed and visualized. |
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