Short communicationPOVME: An algorithm for measuring binding-pocket volumes
Research highlights
▶ An algorithm has been developed to measure the volume of protein binding pockets. ▶ The steps of this algorithm are explained in detail. ▶ To demonstrate its utility, the algorithm is used to identify differences in the binding-pocket dynamics of three structurally similar matrix metalloproteinases.
Introduction
The volume of a binding site has great pharmacological significance, both as one of the many structural features comprising the pharmacophore and as one of the characterizations used in QSAR. When the volume of a target binding site is known, potential ligands that are too large to fit within that volume can be eliminated early in the lead-generation process, prior to virtual or high-throughput screening. Additionally, variations in pocket size when multiple structures of the same protein are considered can provide pharmacologically useful insights into protein dynamics.
Our lab has recently developed a simple algorithm called POVME (POcket Volume MEasurer) for measuring the volume of ligand-binding sites. POVME has been implemented in Python and so is editable, customizable, and platform independent. Additionally, the algorithm is fast, open source, and easy to use.
Section snippets
The POVME algorithm
The POVME algorithm includes four steps. First, the user must select a region defined by overlapping spheres and right rectangular prisms that entirely encompasses the binding pocket but does not extend beyond the volumes of the outermost, solvent-exposed protein atoms (Fig. 1a). Additionally, volume can be subtracted from this region using exclusion spheres and prisms. For example, the region encompassing the matrix-metalloproteinase binding pocket shown in Fig. 1a was defined using six
Results and discussion
To demonstrate the utility of POVME, we used the new algorithm to study the pocket dynamics of three members of the matrix-metalloproteinase (MMP) family of proteins: MMP-2, MMP-3, and MMP-9. MMPs are known to degrade components of the extracellular matrix and have been implicated in arthritis, multiple sclerosis, vascular disease [6], Alzheimer's disease [7], asthma [8], [9], [10], and cancer [11], [12], [13].
MMP-2, MMP-3, and MMP-9 all have similar structures; when crystallographic structures
Conclusion
Herein we have presented a novel algorithm, called POVME, for measuring the volume of ligand-binding pockets. POVME has been implemented in Python and is easily editable, platform independent, fast, and open source. To demonstrate the utility of this new algorithm, we used POVME to show that MMP-2, MMP-3, and MMP-9, three matrix metalloproteinases that are structurally similar, in fact have dynamically different binding sites, potentially explaining the differences in their substrate
Acknowledgements
This work was supported in part by funding from NIH GM31749, NSF MCB-0506593, and MCA93S013 to JAM. Additional support from the Howard Hughes Medical Institute, the National Center for Supercomputing Applications, the San Diego Supercomputer Center, the W.M. Keck Foundation, the National Biomedical Computational Resource, the Center for Theoretical Biological Physics, and the NSF Supercomputer Centers is gratefully acknowledged.
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