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POVME 2.0: An Enhanced Tool for Determining Pocket Shape and Volume Characteristics.

Durrant JD, Votapka L, Sørensen J, Amaro RE - J Chem Theory Comput (2014)

Bottom Line: Analysis of macromolecular/small-molecule binding pockets can provide important insights into molecular recognition and receptor dynamics.The POVME analysis characterizes the full dynamics of a potentially druggable transient binding pocket and so may guide future antitrypanosomal drug-discovery efforts.We are hopeful that this new version will be a useful tool for the computational- and medicinal-chemist community.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry & Biochemistry, University of California San Diego , La Jolla, California 92093, United States ; National Biomedical Computation Resource, Center for Research in Biological Systems, University of California San Diego , La Jolla, California 92093, United States.

ABSTRACT
Analysis of macromolecular/small-molecule binding pockets can provide important insights into molecular recognition and receptor dynamics. Since its release in 2011, the POVME (POcket Volume MEasurer) algorithm has been widely adopted as a simple-to-use tool for measuring and characterizing pocket volumes and shapes. We here present POVME 2.0, which is an order of magnitude faster, has improved accuracy, includes a graphical user interface, and can produce volumetric density maps for improved pocket analysis. To demonstrate the utility of the algorithm, we use it to analyze the binding pocket of RNA editing ligase 1 from the unicellular parasite Trypanosoma brucei, the etiological agent of African sleeping sickness. The POVME analysis characterizes the full dynamics of a potentially druggable transient binding pocket and so may guide future antitrypanosomal drug-discovery efforts. We are hopeful that this new version will be a useful tool for the computational- and medicinal-chemist community.

No MeSH data available.


Related in: MedlinePlus

POVME 1.0 and 2.0 benchmarks. The graph showsbenchmark REL1 pocketvolumes as a function of simulation time. POVME 1.0 and 2.0 give nearlythe same volume measurements (in black). When the POVME 2.0 convex-hulloption is enabled, the volumes are smaller (in gray). The bottom panel,generated using the 1XDN crystal structure, illustrates the difference.When the convex-hull option is enabled, the region of the bindingpocket is more accurately captured (solid gray) than when it is deactivated(black wireframe). Some portions of the protein have been removedto facilitate visualization.
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fig4: POVME 1.0 and 2.0 benchmarks. The graph showsbenchmark REL1 pocketvolumes as a function of simulation time. POVME 1.0 and 2.0 give nearlythe same volume measurements (in black). When the POVME 2.0 convex-hulloption is enabled, the volumes are smaller (in gray). The bottom panel,generated using the 1XDN crystal structure, illustrates the difference.When the convex-hull option is enabled, the region of the bindingpocket is more accurately captured (solid gray) than when it is deactivated(black wireframe). Some portions of the protein have been removedto facilitate visualization.

Mentions: To verify that the resultsof POVME 2.0 are comparable to those of the previous version, we similarlyanalyzed a REL1 trajectory using POVME 1.0. When the convex-hull algorithmwas disabled, both POVME 1.0 and 2.0 gave nearly identical volumemeasurements (Figure 4 graph, in black). Whenthe new convex-hull feature was enabled, POVME-calculated volumeswere lower, as expected (Figure 4 graph, ingray). To verify that the volumes calculated both with and withoutthe convex-hull feature were correlated, we performed a linear regression.A two-tailed t-test suggested that the correlationwas statistically significant (Pearson correlation coefficient: 0.19; p-value: 0.0).


POVME 2.0: An Enhanced Tool for Determining Pocket Shape and Volume Characteristics.

Durrant JD, Votapka L, Sørensen J, Amaro RE - J Chem Theory Comput (2014)

POVME 1.0 and 2.0 benchmarks. The graph showsbenchmark REL1 pocketvolumes as a function of simulation time. POVME 1.0 and 2.0 give nearlythe same volume measurements (in black). When the POVME 2.0 convex-hulloption is enabled, the volumes are smaller (in gray). The bottom panel,generated using the 1XDN crystal structure, illustrates the difference.When the convex-hull option is enabled, the region of the bindingpocket is more accurately captured (solid gray) than when it is deactivated(black wireframe). Some portions of the protein have been removedto facilitate visualization.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4230373&req=5

fig4: POVME 1.0 and 2.0 benchmarks. The graph showsbenchmark REL1 pocketvolumes as a function of simulation time. POVME 1.0 and 2.0 give nearlythe same volume measurements (in black). When the POVME 2.0 convex-hulloption is enabled, the volumes are smaller (in gray). The bottom panel,generated using the 1XDN crystal structure, illustrates the difference.When the convex-hull option is enabled, the region of the bindingpocket is more accurately captured (solid gray) than when it is deactivated(black wireframe). Some portions of the protein have been removedto facilitate visualization.
Mentions: To verify that the resultsof POVME 2.0 are comparable to those of the previous version, we similarlyanalyzed a REL1 trajectory using POVME 1.0. When the convex-hull algorithmwas disabled, both POVME 1.0 and 2.0 gave nearly identical volumemeasurements (Figure 4 graph, in black). Whenthe new convex-hull feature was enabled, POVME-calculated volumeswere lower, as expected (Figure 4 graph, ingray). To verify that the volumes calculated both with and withoutthe convex-hull feature were correlated, we performed a linear regression.A two-tailed t-test suggested that the correlationwas statistically significant (Pearson correlation coefficient: 0.19; p-value: 0.0).

Bottom Line: Analysis of macromolecular/small-molecule binding pockets can provide important insights into molecular recognition and receptor dynamics.The POVME analysis characterizes the full dynamics of a potentially druggable transient binding pocket and so may guide future antitrypanosomal drug-discovery efforts.We are hopeful that this new version will be a useful tool for the computational- and medicinal-chemist community.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry & Biochemistry, University of California San Diego , La Jolla, California 92093, United States ; National Biomedical Computation Resource, Center for Research in Biological Systems, University of California San Diego , La Jolla, California 92093, United States.

ABSTRACT
Analysis of macromolecular/small-molecule binding pockets can provide important insights into molecular recognition and receptor dynamics. Since its release in 2011, the POVME (POcket Volume MEasurer) algorithm has been widely adopted as a simple-to-use tool for measuring and characterizing pocket volumes and shapes. We here present POVME 2.0, which is an order of magnitude faster, has improved accuracy, includes a graphical user interface, and can produce volumetric density maps for improved pocket analysis. To demonstrate the utility of the algorithm, we use it to analyze the binding pocket of RNA editing ligase 1 from the unicellular parasite Trypanosoma brucei, the etiological agent of African sleeping sickness. The POVME analysis characterizes the full dynamics of a potentially druggable transient binding pocket and so may guide future antitrypanosomal drug-discovery efforts. We are hopeful that this new version will be a useful tool for the computational- and medicinal-chemist community.

No MeSH data available.


Related in: MedlinePlus