A Deep Learning-Based Approach for Predicting Michaelis Constants from Enzymatic Reactions

The Michaelis constant (Km) is defined as the substrate concentration at which an enzymatic reaction reaches half of its maximum reaction velocity. The determination of Km can be applied to the construction and optimization of metabolic networks. Conventional determinations of Km values based on in vitro experiments are time-consuming and expensive. Although there are a series of computational approaches of determining Km values based on deep learning, the complex biological information in enzymatic reactions still makes it challenging to achieve accurate predictions. In this study, we develop a novel deep learning approach called DLERKm for predicting Km by combining the features of enzymatic reactions including products. We constructed a new enzymatic reaction dataset from the Sabio-RK and UniProt databases for the training and testing of DLERKm, which include the information on substrates, products, enzyme sequences, and Km values. DLERKm utilizes pre-trained language models (ESM-2 and RXNFP), molecular fingerprints, and attention mechanisms to extract enzymatic reaction features for the prediction of Km values. To evaluate the performance of DLERKm, we compared it with a state-of-the-art model (UniKP) on the constructed enzymatic reaction datasets. The model prediction results demonstrate that DLERKm achieved superior prediction performances in terms of the evaluated metrics on the benchmark datasets, where the relative improvements of four metrics (RMSE, MAE, PCC, and R2) were 16.3%, 16.5%, 27.7%, and 14.9%, respectively. Ablation experiments and interpretability analysis demonstrate the importance of considering product information when predicting Km values. In addition, DLERKm exhibits reliable predictive performances for different types of enzymatic reactions.