CAVER 3.0: a tool for the analysis of transport pathways in dynamic protein structures

PLoS Comput Biol. 2012;8(10):e1002708. doi: 10.1371/journal.pcbi.1002708. Epub 2012 Oct 18.

Abstract

Tunnels and channels facilitate the transport of small molecules, ions and water solvent in a large variety of proteins. Characteristics of individual transport pathways, including their geometry, physico-chemical properties and dynamics are instrumental for understanding of structure-function relationships of these proteins, for the design of new inhibitors and construction of improved biocatalysts. CAVER is a software tool widely used for the identification and characterization of transport pathways in static macromolecular structures. Herein we present a new version of CAVER enabling automatic analysis of tunnels and channels in large ensembles of protein conformations. CAVER 3.0 implements new algorithms for the calculation and clustering of pathways. A trajectory from a molecular dynamics simulation serves as the typical input, while detailed characteristics and summary statistics of the time evolution of individual pathways are provided in the outputs. To illustrate the capabilities of CAVER 3.0, the tool was applied for the analysis of molecular dynamics simulation of the microbial enzyme haloalkane dehalogenase DhaA. CAVER 3.0 safely identified and reliably estimated the importance of all previously published DhaA tunnels, including the tunnels closed in DhaA crystal structures. Obtained results clearly demonstrate that analysis of molecular dynamics simulation is essential for the estimation of pathway characteristics and elucidation of the structural basis of the tunnel gating. CAVER 3.0 paves the way for the study of important biochemical phenomena in the area of molecular transport, molecular recognition and enzymatic catalysis. The software is freely available as a multiplatform command-line application at http://www.caver.cz.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Cluster Analysis
  • Computational Biology / methods*
  • Crystallography
  • Hydrolases / chemistry
  • Hydrolases / metabolism
  • Molecular Dynamics Simulation
  • Protein Conformation*
  • Proteins / chemistry*
  • Proteins / metabolism*
  • Software*

Substances

  • Proteins
  • Hydrolases
  • haloalkane dehalogenase

Grant support

This work was supported by the European Regional Development Fund (CZ.1.05/2.1.00/01.0001 and CZ.1.05/1.1.00/02.0123), the Grant Agency of the Czech Republic (P202/10/1435 and P503/12/0572) and the Grant Agency of the Czech Academy of Sciences (IAA401630901). MetaCentrum provided access to computing facilities, supported by the Ministry of Education of the Czech Republic (LM2010005). The work of AG was supported by SoMoPro programme No. SIGA762 funded by the European Community within the 7th FP under grant agreement No. 229603, and cofinanced by the South Moravian Region. The work of AP was supported by Brno Ph.D. Talent Scholarship provided by Brno City Municipality. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.