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Improving Enzyme Optimum Temperature Prediction with Resampling Strategies and Ensemble Learning

Cite this: J. Chem. Inf. Model. 2020, 60, 8, 4098–4107
Publication Date (Web):July 8, 2020
https://doi.org/10.1021/acs.jcim.0c00489
Copyright © 2020 American Chemical Society

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    Abstract

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    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 (temperature optima for microorganisms and enzymes, TOME) for predicting Topt was developed. TOME was trained on a normally distributed data set with a median Topt of 37 °C and less than 5% of Topt values above 85 °C, limiting the method’s predictive capabilities for thermostable enzymes. Due to the distribution of the training data, the mean squared error on Topt values greater than 85 °C is nearly an order of magnitude higher than the error on values between 30 and 50 °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 °C) by 60% and increase the overall R2 value from 0.527 to 0.632. The revised method, temperature optima for enzymes with resampling (TOMER), and the resampling strategies applied in this work are freely available to other researchers as Python packages on GitHub.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.0c00489.

    • Performance of resampling strategies for all hyperparameter combinations algorithms (pseudocode) for the RO, SMOTER, GN, WERCS, WERCS-GN, and REBAGG resampling strategies(Figure S1) (PDF)

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