Over 14,000 Structural Genomics (SG) protein structures have been deposited in the Protein Data Bank (PDB) and most of these are of unknown or uncertain biochemical function. Typically, functions are assigned using informatics approaches based on similarities in sequence and/or 3D structure. The annotation of protein function by automated means has led to high rates of misannotations in some databases. Here we present a complementary and powerful approach <b>based on computed chemical properties of the individual residues</b> in a protein structure. Partial Order Optimum Likelihood (POOL) is a machine learning method that predicts the residues in the query protein structure that are important for catalysis. Input features for POOL include chemical and electrostatic properties computed for each residue. Typically these sets of biochemically active residues include some in the first layer that make direct contact with the substrate molecule(s) and also some residues in the second and third layers that play supporting roles in the catalytic process. Then, for proteins of known biochemical function, Graph Representation of Active Sites for Prediction of Function (GRASP-Func) establishes local arrays of POOL-predicted residues that are common to proteins of the same function. Then local arrays of POOL-predicted residues of the query (SG) protein are aligned with those of the proteins of known function for the different functional types. These alignments, each SG protein against each functional family, are scored in order to predict the most likely function of the SG proteins. Results are reported for the SG members of the Ribulose Phosphate Binding Barrel (RPBB), Clp-Crotonase, and Haloacid Dehalogenase superfamilies. While we find the SG proteins in the RPBB superfamily to be well annotated, we predict very high annotation error rates (about 70%) in the Clp-Crotonase superfamily. Of particular interest are cases of predicted misannotation, where our prediction differs from that of the assigned function. Experimental testing of our predictions is performed by direct biochemical assays to verify our predictions.<br/>

Using computational chemistry to understand genomes

2019 Fall National Meeting

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