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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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Related in: MedlinePlus

Comparison of the DhaA tunnels identified by CAVER 3.0 with the previously proposed pathways.(A) The top ranked collective pathways identified throughout the molecular dynamics simulation of DhaA by CAVER 3.0 are all depicted in one frame as pathway centerlines. The p2a and p2b tunnels were initially identified as one collective pathway—p2ab—using the clustering threshold of 4.3. Decreasing the clustering threshold to 3.5 led to the separation of the p2a and p2b tunnels as well as the splitting of the p1 collective pathway into three clusters—p1a, p1a′ and p1b. A random subsample of identified tunnels is shown for clarity. (B) Representative DhaA pathways (surface representation) for the release of products and/or exchange of water solvent as identified previously by RAMD and classical MD simulations [15].
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pcbi-1002708-g002: Comparison of the DhaA tunnels identified by CAVER 3.0 with the previously proposed pathways.(A) The top ranked collective pathways identified throughout the molecular dynamics simulation of DhaA by CAVER 3.0 are all depicted in one frame as pathway centerlines. The p2a and p2b tunnels were initially identified as one collective pathway—p2ab—using the clustering threshold of 4.3. Decreasing the clustering threshold to 3.5 led to the separation of the p2a and p2b tunnels as well as the splitting of the p1 collective pathway into three clusters—p1a, p1a′ and p1b. A random subsample of identified tunnels is shown for clarity. (B) Representative DhaA pathways (surface representation) for the release of products and/or exchange of water solvent as identified previously by RAMD and classical MD simulations [15].

Mentions: CAVER 3.0 was used for the analysis of 10,000 snapshots from a 10 ns classical MD simulation of DhaA in explicit water solvent (Protocol S2 and S3). In each snapshot, all possible pathways with the bottleneck radius equal or larger than 0.9 Å were identified, leading to a set of nearly 30,000 pathways. The pathways were clustered by the average-link algorithm based on the pairwise distances of the pathways. All five previously described DhaA tunnels [15] were identified among the top four ranked pathway clusters (Figure 2, Table S2). The p2a and p2b tunnels possess a common opening and thus were initially identified as a single pathway (p2ab). Therefore, the clustering threshold was decreased in order to evaluate the relative importance of the p2a and p2b tunnels to each other (Figure 2). We note that the localization of the p2a and p2b tunnels differs slightly from the study of Klvana et al.[15], where these tunnels were observed to run roughly parallel to each other. In our analysis, we found out that these tunnels may cross each other and consequently four different pathways may in theory be identified by geometrical analysis. The lower clustering threshold also led to the splitting of the p1 tunnel into three branches—the dominant p1a tunnel, corresponding to the p1 tunnel observed in previous analyses [15] and the less frequent and narrower p1a′ and p1b tunnels (Figure 2, Table 1).


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 DhaA tunnels identified by CAVER 3.0 with the previously proposed pathways.(A) The top ranked collective pathways identified throughout the molecular dynamics simulation of DhaA by CAVER 3.0 are all depicted in one frame as pathway centerlines. The p2a and p2b tunnels were initially identified as one collective pathway—p2ab—using the clustering threshold of 4.3. Decreasing the clustering threshold to 3.5 led to the separation of the p2a and p2b tunnels as well as the splitting of the p1 collective pathway into three clusters—p1a, p1a′ and p1b. A random subsample of identified tunnels is shown for clarity. (B) Representative DhaA pathways (surface representation) for the release of products and/or exchange of water solvent as identified previously by RAMD and classical MD simulations [15].
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002708-g002: Comparison of the DhaA tunnels identified by CAVER 3.0 with the previously proposed pathways.(A) The top ranked collective pathways identified throughout the molecular dynamics simulation of DhaA by CAVER 3.0 are all depicted in one frame as pathway centerlines. The p2a and p2b tunnels were initially identified as one collective pathway—p2ab—using the clustering threshold of 4.3. Decreasing the clustering threshold to 3.5 led to the separation of the p2a and p2b tunnels as well as the splitting of the p1 collective pathway into three clusters—p1a, p1a′ and p1b. A random subsample of identified tunnels is shown for clarity. (B) Representative DhaA pathways (surface representation) for the release of products and/or exchange of water solvent as identified previously by RAMD and classical MD simulations [15].
Mentions: CAVER 3.0 was used for the analysis of 10,000 snapshots from a 10 ns classical MD simulation of DhaA in explicit water solvent (Protocol S2 and S3). In each snapshot, all possible pathways with the bottleneck radius equal or larger than 0.9 Å were identified, leading to a set of nearly 30,000 pathways. The pathways were clustered by the average-link algorithm based on the pairwise distances of the pathways. All five previously described DhaA tunnels [15] were identified among the top four ranked pathway clusters (Figure 2, Table S2). The p2a and p2b tunnels possess a common opening and thus were initially identified as a single pathway (p2ab). Therefore, the clustering threshold was decreased in order to evaluate the relative importance of the p2a and p2b tunnels to each other (Figure 2). We note that the localization of the p2a and p2b tunnels differs slightly from the study of Klvana et al.[15], where these tunnels were observed to run roughly parallel to each other. In our analysis, we found out that these tunnels may cross each other and consequently four different pathways may in theory be identified by geometrical analysis. The lower clustering threshold also led to the splitting of the p1 tunnel into three branches—the dominant p1a tunnel, corresponding to the p1 tunnel observed in previous analyses [15] and the less frequent and narrower p1a′ and p1b tunnels (Figure 2, Table 1).

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