OGTFinder: A Curated Growth Temperature Data Set and Its Application To Predict Optimal Growth Temperatures of Bacteria and Archaea

The optimal growth temperature (OGT) of organisms is
valuable in
bioprospecting enzymes that work under extreme conditions. Existing
OGT prediction models achieve high accuracy but mainly capture trends
of overrepresented groups in the training set including organisms
that thrive at moderate temperatures and those from well-described
taxa. In this study, we incorporated weighted scoring and phylogenetic
splits to improve the generalizability of the prediction models. We
first built a new growth temperature data set comprising more than
15,000 species distributed over all three domains of life, with special
attention to include OGT and extreme temperature data. We then trained
machine learning models on the prokaryotic OGT data using proteome-averaged
amino acid descriptors. The best-performing model was the multilayer
perceptron (MLP) with a test RMSE of 5.49 °C and an R2 of 0.84. The most important proteome features were related
to backbone flexibility and charged residues, as well as surface accessibility.
The MLP model is integrated in the command line tool OGTFinder and
available under MIT license at: https://github.com/SC-Git1/OGTFinder.