Enzyme promiscuity prediction using hierarchy-informed multi-label classification

TL;DR

This paper introduces a hierarchy-informed multi-label neural network model for predicting enzyme promiscuity, leveraging hierarchical enzyme class data and inhibitor information to improve accuracy over other models.

Contribution

It presents a novel hierarchical multi-label neural network model, EPP-HMCNF, for enzyme promiscuity prediction that outperforms existing models and utilizes enzyme hierarchy and inhibitor data.

Findings

Hierarchical neural network outperforms other models

Inhibitor data improves prediction accuracy

Performance drops under realistic data splits

Abstract

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.