Enzyme promiscuity prediction using hierarchy-informed multi-label classification
This work addresses the costly and time-consuming experimental characterization of enzyme capabilities for researchers in bioinformatics and biochemistry, but it is incremental as it builds on existing hierarchical classification methods.
The study tackled the problem of predicting enzyme promiscuity by developing machine-learning models to identify which enzymes interact with a given molecule, using data from the BRENDA database. The best model, EPP-HMCNF, outperformed other methods, with inhibitor information improving predictive power, though performance degraded under realistic data splits and for non-natural substrates.
As experimental efforts are costly and time consuming, computational characterization of enzyme capabilities is an attractive alternative. We present and evaluate several machine-learning models to predict which of 983 distinct enzymes, as defined via the Enzyme Commission, EC, numbers, are likely to interact with a given query molecule. Our data consists of enzyme-substrate interactions from the BRENDA database. Some interactions are attributed to natural selection and involve the enzyme's natural substrates. The majority of the interactions however involve non-natural substrates, thus reflecting promiscuous enzymatic activities. We frame this enzyme promiscuity prediction problem as a multi-label classification task. We maximally utilize inhibitor and unlabelled data to train prediction models that can take advantage of known hierarchical relationships between enzyme classes. We report that a hierarchical multi-label neural network, EPP-HMCNF, is the best model for solving this problem, outperforming k-nearest neighbors similarity-based and other machine learning models. We show that inhibitor information during training consistently improves predictive power, particularly for EPP-HMCNF. We also show that all promiscuity prediction models perform worse under a realistic data split when compared to a random data split, and when evaluating performance on non-natural substrates compared to natural substrates. We provide Python code for EPP-HMCNF and other models in a repository termed EPP (Enzyme Promiscuity Prediction) at https://github.com/hassounlab/EPP.