Mary Jo Ondrechen

Machine Learning for protein function prediction and for understanding how enzymes work

Our Machine Learning methodology, Partial Order Optimum Likelihood (POOL) is used to predict biochemically active amino acids in the structures of gene products (proteins). Computed electrostatic and chemical properties of individual amino acids, derived only from the 3D structure of the query protein, serve as input features. Our most recent applications of POOL are described. From predicted local sites of biochemical activity, the biochemical functions of Structural Genomics proteins of unknown function are predicted by local structure matching of predicted spatial arrays of active amino acids with those of proteins of known function. Examples from the Haloacid Dehalogenase superfamily, with experimental testing of function by direct biochemical assay, are featured. POOL analysis also uncovers the types of interactions that enable protein structures to transform the weakly acidic or basic side chains of amino acids into strong acids, strong bases, or nucleophiles in the active site. Finally, the prediction and characterization of druggable sites is presented for SARS-CoV-2 targets.