SmarTSzyme: biasing the mutational landscape in enzymes with QM/MM and chemical dynamics to manipulate catalytic activity

Navigating the mutational landscape of enzymes to alter substrate scope, stereoselectivity or catalytic performance are complex problems. While predicting the total energy of a chemical system like a protein scaffold has been solved with current computational tools, fine-tuning the catalytic properties of a given enzyme scaffold still relies on extensive trial and error experiments. We present in this work SmarTSzyme, a python-based code for the identification of residues in the enzyme that modulate the energetics of the chemical transformation at the active site to bias the selection of residues to mutate. By analysis of the simulation of the catalytic reaction, SmarTSzyme makes uses of the interaction networks along the catalytic mechanism using graph representation and clasify the residues of the enzyme based on a mutability index. Using two enzymes as showcases, we illustrate how SmarTSzyme unveils positions in the enzyme sequence that has been shown important to improve catalysis by others.