Improving enzyme optimum temperature prediction with resampling strategies and ensemble learning

Accurate prediction of the optimal catalytic temperature (Topt) of enzymes is vital in biotechnology, as enzymes with high Topt values are desired for enhanced reaction rates. Recently, a machine-learning method (TOME) for predicting Topt was developed. TOME was trained on a normally-distributed dataset with a median Topt of 37{degrees}C and less than five percent of Topt values above 85{degrees}C, limiting the methods predictive capabilities for thermostable enzymes. Due to the distribution of the training data, the mean squared error on Topt values greater than 85{degrees}C is nearly an order of magnitude higher than the error on values between 30 and 50{degrees}C. In this study, we apply ensemble learning and resampling strategies that tackle the data imbalance to significantly decrease the error on high Topt values (>85{degrees}C) by 60% and increase the overall R2 value from 0.527 to 0.632. The revised method, TOMER, and the resampling strategies applied in this work are freely available to other researchers as a Python package on GitHub.