Characterizing the Functions of Structural Genomics Proteins through Computed Chemical Properties and Experimental Chemistry
Authors: Mills Caitlyn L , Beuning Penny J , Ondrechen Mary Jo 
Published: November 2023

As of November 2016, the Protein Structure Initiative resulted in nearly 14,000 Structural Genomics (SG) protein structures deposited in the PDB. Initially it was believed that structural information would reveal the function of these proteins, but this proved to be more difficult than expected. Now we are left with proteins of unknown or uncertain functions or putative functions that are often incorrect. This accumulated structural information constitutes a tremendous contribution to structural biology and genomics. However, the addition of better functional predictions for SG proteins would add substantial value to the information already available. Our approach incorporates structure‐based computed chemical properties through local site prediction followed by local structural matching. First, Partial Order Optimum Likelihood (POOL) is a machine learning method used to predict computationally the residues important for catalysis in a given protein structure. Then, Structurally Aligned Local Sites of Activity (SALSA) analyzes a superfamily and develops spatially‐localized consensus signatures for the proteins of known function in each functional family based on POOL‐predicted residues, functionally characterized residues of importance, and multiple structure alignments. The POOL‐predicted residues for the SG proteins are then compared to the consensus signatures and the alignments are scored to determine the degree of similarity between the local structures.

This research focuses on two enzyme superfamilies: the Crotonase Superfamily and the 6‐Hairpin Glycosidase Superfamily. For the Crotonase Superfamily, we have computationally determined that many SG proteins are misannotated. In some cases, we are able to provide a better putative functional annotation for the SG proteins. We have also acquired experimental data supporting our predictions as well as proof of promiscuity within the superfamily. For example, we have assayed proteins that exhibit both hydrolase and hydratase activity, albeit higher activity for one function over the other. SG protein probable enoyl‐CoA hydratase from Thermus thermophilus (PDB 1WZ8) is predicted by our methods to be a hydrolase and we have demonstrated this activity as well as a lower hydratase activity in biochemical assays. For the 6‐Hairpin Glycosidase Superfamily, we have computationally determined a number of functional assignments for SG proteins in this superfamily, including substrate specificity. For example, SG protein putative glycoside hydrolase from Bacillus halodurans (PDB 2RDY) is shown through general bioinformatics analysis to be a fucosidase. However, due to the difference of a key substrate recognition residue in the local active site, our method predicts galactosidase activity instead. The chemical reactions are the same in both cases, but with different substrates. The goal of this research project is to provide a validated approach to functional annotation for wider application by the community, including drug discovery, controlling antibiotic‐resistant bacteria, alternative energy technology, and biofuel production.

Support or Funding Information

Support from NSF‐CHE‐1305655 and the PhRMA Foundation (predoctoral fellowship in informatics awarded to CLM).