Revolutionizing Enzyme Turnover Predictions with Non-Binary Reaction Fingerprints and AI

The turnover number (k<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cat</inf>) is a crucial measure for evaluating enzyme catalytic efficiency, significantly influencing studies of cellular metabolism and resource allocation. Due to the high costs associated with determining k<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cat</inf>, predictive machine learning models such as TurNuP have been developed to predict the turnover numbers of kinetically uncharacterized enzymes. Existing methods rely on binary Differential Reaction Fingerprints (DRFPs) to represent reactions. However, these binary reaction fingerprints have two main limitations. First, they only show changes between substrates and products, without specifying whether the change occurs in the substrates or products. Second, they omit information about similarities between substrates and products, missing crucial reaction details. To overcome these issues, we developed Quaternary Reaction Fingerprints (QRFPs) by incorporating quaternary structural features. Thus, QRFPs extend DRFPs, and DRFPs can be derived from QRFPs. We proposed a deep learning method based on QRFPs and pretrained protein language model ESM2, called TurNuP<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf>. Experimental results show that TurNuP<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> outperforms TurNuP by 18% in R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to predict k<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cat</inf> values for natural reactions of wild-type enzymes. Code is available at https://github.com/xfcui/TurNuP4.