Protein Pockets: Inventory, Shape, and Comparison

• Published in: Journal of chemical information and modeling 2010-04, Vol.50 (4), p.589-603
• Main Authors: Coleman, Ryan G, Sharp, Kim A
• Format: Article
• Language: English
• Subjects: Adenylate Kinase - chemistry; Adenylate Kinase - metabolism; Analytical, structural and metabolic biochemistry; Binding Sites; Biological and medical sciences; Comparative analysis; Computational Biochemistry; Computational Biology; Fundamental and applied biological sciences. Psychology; Interactions. Associations; Intermolecular phenomena; Models, Molecular; Molecular biophysics; Molecules; Mutation; Protein Conformation; Protein Tyrosine Phosphatase, Non-Receptor Type 1 - chemistry; Protein Tyrosine Phosphatase, Non-Receptor Type 1 - metabolism; Proteins; Proteins - chemistry; Proteins - genetics; Proteins - metabolism; Software
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/ci900397t

The shape of the protein surface dictates what interactions are possible with other macromolecules, but defining discrete pockets or possible interaction sites remains difficult. First, there is the problem of defining the extent of the pocket. Second, one has to characterize the shape of each pocket. Third, one needs to make quantitative comparisons between pockets on different proteins. An elegant solution to these problems is to sort all surface and solvent points by travel depth and then collect a hierarchical tree of pockets. The connectivity of the tree is determined via the deepest saddle points between each pair of neighboring pockets. The resulting pocket surfaces tessellate the entire protein surface, producing a complete inventory of pockets. This method of identifying pockets also allows one to easily compute important shape metrics, including the problematic pocket volume, surface area, and mouth size. Pockets are also annotated with their lining residue lists and polarity and with other residue-based properties. Using this tree and the various shape metrics pockets can be merged, grouped, or filtered for further analysis. Since this method includes the entire surface, it guarantees that any pocket of interest will be found among the output pockets, unlike all previous methods of pocket identification. The resulting hierarchy of pockets is easy to visualize and aids users in higher level analysis. Comparison of pockets is done by using the shape metrics, avoiding the complex shape alignment problem. Example applications show that the method facilitates pocket comparison along mutational or time-dependent series. Pockets from families of proteins can be examined using multiple pocket tree alignments to see how ligand binding sites or how other pockets have changed with evolution. Our method is called CLIPPERS for complete liberal inventory of protein pockets elucidating and reporting on shape.