Functional characterization of structural genomics proteins through computed chemical properties, graph representation of active sites, and biochemical validation

Abstract

There are now over 14,600 Structural Genomics (SG) protein structures, most of unknown or putative function annotation, deposited in the Protein Data Bank by protein structure initiatives. This accumulated information represents a tremendous contribution to structural biology and genomics. Still, the addition of accurate functional annotations for these SG proteins would add substantial value to this information. Our approach to functional annotation incorporates predicting functional assignments through structure-based computed chemical properties and local structure matching followed by biochemical validation. This research focuses on four superfamilies: 6-Hairpin Glycosidase, Concanavalin A-like Lectin/Glucanase, Ribulose Phosphate Binding Barrel, and Crotonase. Each superfamily is analyzed by our two methods to predict the function of its members. Structurally Aligned Local Sites of Activity (SALSA) develops spatially-localized consensus signatures for proteins of known function representing each functional family within each superfamily based on Partial Order Optimum Likelihood (POOL)-predicted residues and functionally characterized residues of importance. Then, the POOL-predicted residues for each SG protein are compared to each consensus signature and scored to determine their degree of similarity at the local active site. Alternatively, our new method Graph Representation of Active Sites for Prediction of Function (GRASP-Func) is used to sort the superfamilies and annotate protein function using local structure matching in graph representation based on Delaunay triangulation, which generates sets of tetrahedra for each protein using the alpha carbon of each residue. SALSA and GRASP-Func both correctly sort the superfamilies into their respective functional families and make similar functional predictions for SG proteins, although GRASP-Func is faster and more accurate. Finally, we tested a set of predictions from the Crotonase superfamily biochemically to confirm function. Here, we provide a validated approach to functional annotation to enable applications from drug target identification to green chemistry and biofuel production.