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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

A graphicalsummary of the POVME 2.0 algorithm. A) The user definesan inclusion region. B) The user defines an exclusion region. C) Theportion of the inclusion region that is not also in the exclusionregion is flooded with equidistant points. D) Any of the points thatare close to receptor atoms are deleted. E) Any points outside theconvex hull are optionally deleted. F) The user can optionally definea contiguous-points region. G) All points that are not contiguouswith that region are similarly deleted.
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fig2: A graphicalsummary of the POVME 2.0 algorithm. A) The user definesan inclusion region. B) The user defines an exclusion region. C) Theportion of the inclusion region that is not also in the exclusionregion is flooded with equidistant points. D) Any of the points thatare close to receptor atoms are deleted. E) Any points outside theconvex hull are optionally deleted. F) The user can optionally definea contiguous-points region. G) All points that are not contiguouswith that region are similarly deleted.

Mentions: The user must next define “inclusion”(Figure 2A) and “exclusion” (Figure 2B) regions, respectively. Both of these regionsare constructed from a combination of user-specified spheres and rectangularprisms. The required inclusion region should entirely encompass allthe binding-pocket conformations of the trajectory. POVME includesa helper script called “POVME Pocket ID,” describedin greater detail below, to assist in identifying this region if necessary.The optional exclusion region defines portions of the inclusion regionthat should be ignored, perhaps because they are not truly associatedwith the pocket. To generate a grid of equidistant points that encompassesall the binding-pocket conformations of the trajectory (spaced 1.0Å apart by default), POVME first floods the user-specified inclusionregion with points and then removes any points also contained in theoptional exclusion region (Figure 2C).


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)

A graphicalsummary of the POVME 2.0 algorithm. A) The user definesan inclusion region. B) The user defines an exclusion region. C) Theportion of the inclusion region that is not also in the exclusionregion is flooded with equidistant points. D) Any of the points thatare close to receptor atoms are deleted. E) Any points outside theconvex hull are optionally deleted. F) The user can optionally definea contiguous-points region. G) All points that are not contiguouswith that region are similarly deleted.
© Copyright Policy
Related In: Results  -  Collection

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

fig2: A graphicalsummary of the POVME 2.0 algorithm. A) The user definesan inclusion region. B) The user defines an exclusion region. C) Theportion of the inclusion region that is not also in the exclusionregion is flooded with equidistant points. D) Any of the points thatare close to receptor atoms are deleted. E) Any points outside theconvex hull are optionally deleted. F) The user can optionally definea contiguous-points region. G) All points that are not contiguouswith that region are similarly deleted.
Mentions: The user must next define “inclusion”(Figure 2A) and “exclusion” (Figure 2B) regions, respectively. Both of these regionsare constructed from a combination of user-specified spheres and rectangularprisms. The required inclusion region should entirely encompass allthe binding-pocket conformations of the trajectory. POVME includesa helper script called “POVME Pocket ID,” describedin greater detail below, to assist in identifying this region if necessary.The optional exclusion region defines portions of the inclusion regionthat should be ignored, perhaps because they are not truly associatedwith the pocket. To generate a grid of equidistant points that encompassesall the binding-pocket conformations of the trajectory (spaced 1.0Å apart by default), POVME first floods the user-specified inclusionregion with points and then removes any points also contained in theoptional exclusion region (Figure 2C).

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