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CAVER 3.0: A Tool for the Analysis of Transport Pathways in Dynamic Protein Structures

Author

Listed:
  • Eva Chovancova
  • Antonin Pavelka
  • Petr Benes
  • Ondrej Strnad
  • Jan Brezovsky
  • Barbora Kozlikova
  • Artur Gora
  • Vilem Sustr
  • Martin Klvana
  • Petr Medek
  • Lada Biedermannova
  • Jiri Sochor
  • Jiri Damborsky

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.

Suggested Citation

  • Eva Chovancova & Antonin Pavelka & Petr Benes & Ondrej Strnad & Jan Brezovsky & Barbora Kozlikova & Artur Gora & Vilem Sustr & Martin Klvana & Petr Medek & Lada Biedermannova & Jiri Sochor & Jiri Damb, 2012. "CAVER 3.0: A Tool for the Analysis of Transport Pathways in Dynamic Protein Structures," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-12, October.
  • Handle: RePEc:plo:pcbi00:1002708
    DOI: 10.1371/journal.pcbi.1002708
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    References listed on IDEAS

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    1. Youxing Jiang & Alice Lee & Jiayun Chen & Martine Cadene & Brian T. Chait & Roderick MacKinnon, 2002. "Crystal structure and mechanism of a calcium-gated potassium channel," Nature, Nature, vol. 417(6888), pages 515-522, May.
    2. Atsuo Miyazawa & Yoshinori Fujiyoshi & Nigel Unwin, 2003. "Structure and gating mechanism of the acetylcholine receptor pore," Nature, Nature, vol. 423(6943), pages 949-955, June.
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    1. Yang, Jianming & Li, Zhengfeng & Guo, Lizhong & Du, Juan & Bae, Hyeun-Jong, 2016. "Biosynthesis of β-caryophyllene, a novel terpene-based high-density biofuel precursor, using engineered Escherichia coli," Renewable Energy, Elsevier, vol. 99(C), pages 216-223.
    2. Keri A McKiernan & Anna K Koster & Merritt Maduke & Vijay S Pande, 2020. "Dynamical model of the CLC-2 ion channel reveals conformational changes associated with selectivity-filter gating," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-24, March.

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