Classification of protein functional surfaces using structural characteristics.

Protein structure and function are closely related, especially in functional surfaces, which are local spatial regions that perform the biological functions. Also, protein structures tend to evolve more slowly than amino acid sequences. We have therefore developed a method to classify proteins using the structures of functional surfaces; we call it protein surface classification (PSC). PSC may reflect functional relationships among proteins and may detect evolutionary relationships among highly divergent sequences. We focused on the surfaces of ligand-bound regions because they represent well-defined structures. Specifically, we used structural attributes to measure similarities between binding surfaces and constructed a PSC library of ~2,000 binding surface types from the bound forms. Using flavin mononucleotide-binding proteins and glycosidases as examples, we show how the evolutionary position of an uncharacterized protein can be defined and its function inferred from the characterized members of the same surface subtype. We found that proteins with the same enzyme nomenclature may be divided into subtypes and that two proteins in the same CATH (Class, Architecture, Topology, Homologous superfamily) fold may belong to two different surface types. In conclusion, our approach complements the sequence-based and fold-domain classifications and has the advantage of associating the shape of a protein with its biological function. As an expandable library, PSC provides a resource of spatial patterns for studying the evolution of protein structure and function.