title: Local functional descriptors for surface comparison based binding prediction.
abstract: BACKGROUND
  Molecular recognition in proteins occurs due to appropriate arrangements of physical, chemical, and geometric properties of an atomic surface. Similar surface regions should create similar binding interfaces. Effective methods for comparing surface regions can be used in identifying similar regions, and to predict interactions without regard to the underlying structural scaffold that creates the surface.
  
  
  RESULTS
  We present a new descriptor for protein functional surfaces and algorithms for using these descriptors to compare protein surface regions to identify ligand binding interfaces. Our approach uses descriptors of local regions of the surface, and assembles collections of matches to compare larger regions. Our approach uses a variety of physical, chemical, and geometric properties, adaptively weighting these properties as appropriate for different regions of the interface. Our approach builds a classifier based on a training corpus of examples of binding sites of the target ligand. The constructed classifiers can be applied to a query protein providing a probability for each position on the protein that the position is part of a binding interface. We demonstrate the effectiveness of the approach on a number of benchmarks, demonstrating performance that is comparable to the state-of-the-art, with an approach with more generality than these prior methods.
  
  
  CONCLUSIONS
  Local functional descriptors offer a new method for protein surface comparison that is sufficiently flexible to serve in a variety of applications.
authors.affiliation: Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA.
authors.forename: Gregory M
authors.initials: GM
authors.lastname: Cipriano
authors.forename: George N
authors.initials: GN
authors.lastname: Phillips
authors.forename: Michael
authors.initials: M
authors.lastname: Gleicher
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D008024:Ligands; D008958:Models, Molecular; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods* / Q000706:statistics & numerical data*; D012984:Software
chemical_list: D008024:Ligands; D011506:Proteins