Electrostatic networks in natural enzymes: What can we learn for protein engineering?
Tuesday, November 27, 2018
1:00 p.m., Room 202 MRB
Professor Mary Jo Ondrechen
Department of Chemistry and Chemical Biology
Electrostatic interactions across networks of residues are important features that give natural enzymes their catalytic power. Understanding these networks is necessary to learn how to build these properties into in silico enzyme designs. Partial Order Optimum Likelihood (POOL) is a machine learning method developed by us to predict residues important for function, using the 3D structure of the query protein. The input features to POOL are based on computed electrostatic and chemical properties from THEMATICS. These input features are measures of the strength of coupling between protonation events, as catalytic sites in proteins are characterized by networks of strongly coupled protonation states. These networks impart the necessary electrostatic, proton- transfer, and ligand binding properties to the active residues in the first layer around the reacting substrate molecule(s). Typically these networks include first-, second-, and sometimes third- layer residues. POOL-predicted, multi-layer active sites with significant participation by distal residues have been verified experimentally by site-directed mutagenesis and kinetics assays for Ps. putida nitrile hydratase, human phosphoglucose isomerase, E. coli replicative DNA polymerase Pol III, E. coli Y family DNA polymerase DinB, and E. coli ornithine transcarbamoylase. In designed enzymes, such as retroaldolases, the residue-specific input features to POOL – measures of the strength of coupling between protonation equilibria – rise as the enzymes evolve to higher rates of catalytic turnover. We show that high values for these measures in the catalytic residues constitute one necessary feature for catalytic activity. An approach to build these properties into the initial designs is proposed. Acknowledgment: NSF MCB-1517290.