Predicting Functional Diversity Within Enzyme Families: A Practical Perspective

Machine learning has become an increasingly powerful tool in enzyme discovery, enabling the prediction of enzyme activity, substrate scope, and functional specificity across diverse protein families. However, many reported models rely on narrowly sampled datasets and task-specific benchmarks, limiting their robustness and generalizability to distantly related enzymes or novel chemical substrates. In this concept article, we provide a practical, best-practice-oriented perspective on developing enzyme family- or class-specific machine learning (ML) predictors with an emphasis on data quality, representation, and model interpretability. We first discuss strategies for assembling biochemical datasets from literature, highlighting common sources of bias, redundancy, and uncertainty impacting downstream model performance. We then outline key considerations for generating new experimental data that are better suited for ML applications, including dataset design and coverage of enzyme and chemical space. Next, we summarize core computational steps involved in constructing enzyme and substrate representations, training and testing predictive models, and analyzing learned embedding spaces. Finally, we discuss outstanding technical challenges, such as mitigating taxonomic sampling bias, computing representations of enzymes with cofactors and post-translational modifications, and integrating predictions into design-build-test-learn cycles for real-world applications.