Machine Learning Applied to Predicting Microorganism Growth Temperatures and Enzyme Catalytic Optima

• Published in: ACS synthetic biology Vol. 8; no. 6; pp. 1411 - 1420
• Main Authors: Li, Gang, Rabe, Kersten S, Nielsen, Jens, Engqvist, Martin K. M
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 21-06-2019
• Subjects: enzyme temperature optima; machine learning; optimal growth temperature; sequence-based prediction; thermostable enzymes; optimal growth temperature; enzyme temperature optima; sequence-based prediction; machine learning; thermostable enzymes
• ISSN: 2161-5063; 2161-5063
• DOI: 10.1021/acssynbio.9b00099

Enzymes that catalyze chemical reactions at high temperatures are used for industrial biocatalysis, applications in molecular biology, and as highly evolvable starting points for protein engineering. The optimal growth temperature (OGT) of organisms is commonly used to estimate the stability of enzymes encoded in their genomes, but the number of experimentally determined OGT values are limited, particularly for thermophilic organisms. Here, we report on the development of a machine learning model that can accurately predict OGT for bacteria, archaea, and microbial eukaryotes directly from their proteome-wide 2-mer amino acid composition. The trained model is made freely available for reuse. In a subsequent step we use OGT data in combination with amino acid composition of individual enzymes to develop a second machine learning modelfor prediction of enzyme catalytic temperature optima (T opt). The resulting model generates enzyme T opt estimates that are far superior to using OGT alone. Finally, we predict T opt for 6.5 million enzymes, covering 4447 enzyme classes, and make the resulting data set available to researchers. This work enables simple and rapid identification of enzymes that are potentially functional at extreme temperatures.