Computational Modeling of Enzyme Kinetics in Industrial Biocatalysis

Abstract

Industrial biocatalysis increasingly depends on computational prediction to shorten the path from a candidate
enzyme to an optimised, scalable process, yet the modelling toolkit available to a process chemist or biocatalysis
engineer spans methods of very different character, cost and predictive scope, from classical Michaelis-Menten
curve fitting through molecular dynamics simulation to modern machine-learning models trained on large
enzyme-substrate datasets. This perspective examines where each of these approaches currently sits on the
trade-off between mechanistic insight, predictive accuracy and computational cost, drawing on a comparative case
study we conducted across three computational strategies -- classical kinetic parameter fitting, molecular
dynamics-based transition state estimation, and a gradient-boosted machine-learning model trained on a curated
dataset of 3,412 enzyme-substrate kinetic measurements -- applied to predicting turnover number and Michaelis
constant for a panel of 22 industrially relevant hydrolase and transferase variants. The machine-learning model
achieved the best raw predictive accuracy (R2=0.71 for turnover number prediction on a held-out variant panel) at
a fraction of the computational cost of molecular dynamics simulation, but its predictions degraded sharply for
variants carrying mutations at positions absent from the training data, a failure mode that classical kinetic fitting
cannot exhibit because it does not extrapolate to unmeasured variants at all, and that molecular dynamics
simulation, while far more computationally expensive, tolerated considerably better. We argue that the most
productive near-term role for machine-learning kinetic prediction in industrial biocatalysis is not as a replacement
for mechanistic simulation or experimental characterisation but as a triage tool for prioritising which enzyme
variants merit the more expensive investment of either approach, and we outline what we consider the most
promising directions for combining these methods into integrated computational workflows over the next several
years.