Michaelis constants (Km) are essential to predict the catalytic rate of enzymes, but are not widely available. A new study in PLOS Biology uses artificial intelligence (AI) to accurately predict Km on a proteome-wide scale, paving the way for dynamic, genome-wide modeling of metabolism.
Research Support, Non-U.S. Gov't
A.A.A. is primarily supported by an ICR Fellowship of the Institute of Cancer Research in London, UK. M.C. acknowledge support from the European Commission (HaemMetabolome [EC-675790]); Spanish Ministerio de Economia y Competitividad (MINECO) and Ministerio de Ciencia e Innovación -European Commission FEDER funds—“Una manera de hacer Europa” (SAF2017-89673-R and PID2020-115051RB-I00), the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) Generalitat de Catalunya (2017SGR1033) and CIBERehd (CB17/04/00023) (ISCIII, Spain). M.C. also received support through the prize “ICREA Academia” for excellence in research, funded by ICREA foundation–Generalitat de Catalunya. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.