SubTuner: a Physics-Guided Computational Tool for Modifying Enzymatic Substrate Preference and Its Application to Anion Methyltransferases

Engineering enzymes to catalyze non-native substrates is critical for chemical synthesis. A significant challenge is to create a tool specialized in shifting an enzyme’s activity toward a specified non-native substrate. We developed SubTuner, a physics-based computational tool that automates enzyme engineering for catalyzing desired non-native substrates. To test the performance of SubTuner, we designed three tasks – all aiming to identify beneficial anion methyltransferase mutants for synthesis of non-native S-adenosyl-L-methionine analogs: first in the conversion of ethyl iodide from a pool of 190 AtHOL1 single-point mutants for an initial test of accuracy and speed; second of ethyl and n-propyl iodide from a pool of 600 acl-MT multi-point mutants for a test of generalizability; and eventually of bulkier substrates (n-propyl, isopropyl, and allyl iodide) combined with experimental characterization for a test of a priori predictivity. We have also tested SubTuner against bioinformatics and machine learning-based enzyme engineering tools. All tests demonstrated SubTuner’s superior ability to accelerate the discovery of function-enhancing mutants for non-native substrates. Moreover, utilizing molecular simulation data derived from SubTuner, we elucidated how beneficial mutations promote catalysis. SubTuner, with its physical hypothesis, quantitative accuracy, and mechanism-informing ability, holds a significant potential to aid enzyme engineering for substrate scope expansion.