Electrostatic interactions in natural enzymes: What can we learn for enzyme design?

Abstract

The goal of this work is to understand the intrinsic properties that give natural enzymes their catalytic power and to learn how to build these properties in silico, for the design and expression of artificial enzymes that catalyze reactions that do not occur in nature. 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 effectively measures of the strength of coupling between protonation events. POOL is used to characterize the properties of natural enzymes that are necessary for efficient catalysis. Catalytic sites in proteins are characterized by networks of strongly coupled protonation states; these networks impart the necessary electrostatic and proton‐transfer 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 single‐point 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. An approach to build these properties into the initial designs is proposed.Support or Funding InformationNSF MCB‐1517290 (MJO & PJB) and a National Institute of Justice Fellowship (TAC)This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.