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 and drug development. Although general-purpose computational tools exist, 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-guided 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 iodide 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 iodide, isopropyl iodide, and allyl iodide) combined with experimental characterization for a test of a priori predictivity. All the tests demonstrate SubTuner’s ability to accelerating 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 solid physical hypothesis, quantitative accuracy, and mechanism-informing ability, holds a significant potential to aid enzyme engineering for substrate scope expansion.