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

Chovancova E, Pavelka A, Benes P, Strnad O, Brezovsky J, Kozlikova B, Gora A, Sustr V, Klvana M, Medek P, Biedermannova L, Sochor J, Damborsky J - PLoS Comput. Biol. (2012)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masaryk University, Brno, Czech Republic.

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.

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Comparison of the p1 tunnel of DhaA calculated by CAVER 3.0 and MOLE 1.2.(A) Time evolution of the bottleneck radius calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). Only a part of the 10 ns MD simulation is shown for clarity. The sample snapshot (black arrows) was taken at 0.81 ns. (B) Geometry of the p1 tunnel in the sample snapshot calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). Hydrogen atom of the bottleneck residue Ala145 (white ball) is shown together with the sulfur atom of the bottleneck residue Cys176 (yellow ball) and with sphere representation of the tunnels. The underestimation of the bottleneck radius by MOLE 1.2 is visible as an empty space between the tunnel and the hydrogen atom of Ala145. (C) Profile of the p1 tunnel in the sample snapshot calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). The dashed line indicates the profile representing the maximum possible difference between the CAVER-calculated (solid line) and the correct profile of the p1 tunnel.
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pcbi-1002708-g001: Comparison of the p1 tunnel of DhaA calculated by CAVER 3.0 and MOLE 1.2.(A) Time evolution of the bottleneck radius calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). Only a part of the 10 ns MD simulation is shown for clarity. The sample snapshot (black arrows) was taken at 0.81 ns. (B) Geometry of the p1 tunnel in the sample snapshot calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). Hydrogen atom of the bottleneck residue Ala145 (white ball) is shown together with the sulfur atom of the bottleneck residue Cys176 (yellow ball) and with sphere representation of the tunnels. The underestimation of the bottleneck radius by MOLE 1.2 is visible as an empty space between the tunnel and the hydrogen atom of Ala145. (C) Profile of the p1 tunnel in the sample snapshot calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). The dashed line indicates the profile representing the maximum possible difference between the CAVER-calculated (solid line) and the correct profile of the p1 tunnel.

Mentions: CAVER 3.0 and MOLE 1.2 were used for the analysis of 1,000 snapshots from a 10 ns MD simulation of haloalkane dehalogenase DhaA using various settings of clustering parameters (Protocol S2 and S3). MolAxis 1.4 does not employ an algorithm for pathway clustering and therefore is not suitable for automatic analysis of tunnels in MD simulations. The average-link algorithm implemented in CAVER 3.0 provided robust results with better separated clusters than the clustering algorithm implemented in MOLE 1.2 (Figure S1). Another advantage of the hierarchical algorithm implemented in CAVER 3.0 is the possibility to modify the results from clustering by adjusting the threshold value without a need to repeat the clustering procedure. This significantly speeds-up the search for the optimal clustering parameters. CAVER 3.0 additionally provides other options to control the clustering results—based on the user-defined parameters, the pairwise distance of the pathways may be calculated using only selected pathway sections, with constant weights along the entire pathway length or with a higher weight on either the beginning or end sections of the pathway. Settings that provided comparable clusters for the p1 tunnel (previously also named the upper or main tunnel [36]) were selected and the time evolution of the p1 tunnel calculated by both tools was compared (Figure S1C and S1F). This comparison revealed that the average bottleneck radius of the p1 tunnel calculated by CAVER 3.0 (1.4 Å, mean error<0.04 Å) differs from that calculated by MOLE 1.2 (1.1 Å; Figure 1A). This discrepancy is due to the calculation error of MOLE 1.2 (Figure 1B), which is proportional to the difference between the radii of individual atoms making up the pathway and considerably varies in individual snapshots. For example, in a sample snapshot, MOLE 1.2 identified a p1 tunnel with a bottleneck radius of 0.5 Å, while the bottleneck calculated by CAVER 3.0 was 1.2 Å with the maximum approximation error of 0.04 Å (Figure 1C).


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

Chovancova E, Pavelka A, Benes P, Strnad O, Brezovsky J, Kozlikova B, Gora A, Sustr V, Klvana M, Medek P, Biedermannova L, Sochor J, Damborsky J - PLoS Comput. Biol. (2012)

Comparison of the p1 tunnel of DhaA calculated by CAVER 3.0 and MOLE 1.2.(A) Time evolution of the bottleneck radius calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). Only a part of the 10 ns MD simulation is shown for clarity. The sample snapshot (black arrows) was taken at 0.81 ns. (B) Geometry of the p1 tunnel in the sample snapshot calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). Hydrogen atom of the bottleneck residue Ala145 (white ball) is shown together with the sulfur atom of the bottleneck residue Cys176 (yellow ball) and with sphere representation of the tunnels. The underestimation of the bottleneck radius by MOLE 1.2 is visible as an empty space between the tunnel and the hydrogen atom of Ala145. (C) Profile of the p1 tunnel in the sample snapshot calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). The dashed line indicates the profile representing the maximum possible difference between the CAVER-calculated (solid line) and the correct profile of the p1 tunnel.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC3475669&req=5

pcbi-1002708-g001: Comparison of the p1 tunnel of DhaA calculated by CAVER 3.0 and MOLE 1.2.(A) Time evolution of the bottleneck radius calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). Only a part of the 10 ns MD simulation is shown for clarity. The sample snapshot (black arrows) was taken at 0.81 ns. (B) Geometry of the p1 tunnel in the sample snapshot calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). Hydrogen atom of the bottleneck residue Ala145 (white ball) is shown together with the sulfur atom of the bottleneck residue Cys176 (yellow ball) and with sphere representation of the tunnels. The underestimation of the bottleneck radius by MOLE 1.2 is visible as an empty space between the tunnel and the hydrogen atom of Ala145. (C) Profile of the p1 tunnel in the sample snapshot calculated by CAVER 3.0 (blue) and MOLE 1.2 (red). The dashed line indicates the profile representing the maximum possible difference between the CAVER-calculated (solid line) and the correct profile of the p1 tunnel.
Mentions: CAVER 3.0 and MOLE 1.2 were used for the analysis of 1,000 snapshots from a 10 ns MD simulation of haloalkane dehalogenase DhaA using various settings of clustering parameters (Protocol S2 and S3). MolAxis 1.4 does not employ an algorithm for pathway clustering and therefore is not suitable for automatic analysis of tunnels in MD simulations. The average-link algorithm implemented in CAVER 3.0 provided robust results with better separated clusters than the clustering algorithm implemented in MOLE 1.2 (Figure S1). Another advantage of the hierarchical algorithm implemented in CAVER 3.0 is the possibility to modify the results from clustering by adjusting the threshold value without a need to repeat the clustering procedure. This significantly speeds-up the search for the optimal clustering parameters. CAVER 3.0 additionally provides other options to control the clustering results—based on the user-defined parameters, the pairwise distance of the pathways may be calculated using only selected pathway sections, with constant weights along the entire pathway length or with a higher weight on either the beginning or end sections of the pathway. Settings that provided comparable clusters for the p1 tunnel (previously also named the upper or main tunnel [36]) were selected and the time evolution of the p1 tunnel calculated by both tools was compared (Figure S1C and S1F). This comparison revealed that the average bottleneck radius of the p1 tunnel calculated by CAVER 3.0 (1.4 Å, mean error<0.04 Å) differs from that calculated by MOLE 1.2 (1.1 Å; Figure 1A). This discrepancy is due to the calculation error of MOLE 1.2 (Figure 1B), which is proportional to the difference between the radii of individual atoms making up the pathway and considerably varies in individual snapshots. For example, in a sample snapshot, MOLE 1.2 identified a p1 tunnel with a bottleneck radius of 0.5 Å, while the bottleneck calculated by CAVER 3.0 was 1.2 Å with the maximum approximation error of 0.04 Å (Figure 1C).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Loschmidt Laboratories, Department of Experimental Biology and Research Centre for Toxic Compounds in the Environment, Faculty of Science, Masaryk University, Brno, Czech Republic.

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.

Show MeSH
Related in: MedlinePlus