Prediction of Michaelis constants from structural features using deep learning [article]

Alexander Kroll, David Heckmann, Martin J Lercher
2020 bioRxiv   pre-print
The Michaelis constant KM describes the affinity of an enzyme for a specific substrate, and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of KM are often difficult and time-consuming, experimental estimates exist for only a minority of enzyme-substrate combinations even in model organisms. Here, we build and train an organism-independent model that successfully predicts KM values for natural enzyme-substrate combinations using machine and deep
more » ... ning methods. Predictions are based on a task-specific molecular fingerprint of the substrate, generated using a graph neural network, and the domain structure of the enzyme. Model predictions can be used to estimate enzyme efficiencies, to relate metabolite concentrations to cellular physiology, and to fill gaps in the parameterization of kinetic models of cellular metabolism.
doi:10.1101/2020.12.01.405928 fatcat:btmzskpmdfgstmim3rmvhfi6gy