Enzymes have been developed through rational design or directed evolution. Rational design is a top-down method which considers enzyme behaviour and functionality, and makes proteins from scratch by predicting their gene sequences that fold to specific structures; directed evolution is a bottom-up method that screens from multiple experimentally evolved protein structures to satisfy specific objectives. These two methods are usually combined to accelerate enzyme development. Apart from conventional approaches, machine learning is also frequently applied to enzyme engineering. Most recently, Google's DeepMind published its open-sourced deep learning protein structure prediction tool AlphaFold, and then its upgraded version AlphaFold2, which use new neural network architectures to learn from three-dimensional structures of proteins and make predictions at a near-experiential precision. A comprehensive discussion of machine learning applications in enzyme engineering can be found in the review papers by Mazurenko et al. and Yang et al. Apart from evolving novel enzymes, synthetic biologists also benefit from enzyme promiscuity, which is the capability of enzyme's protein structure to bind non-native substrates and catalyse multiple reactions. Enzyme promiscuity is efficient in exploring enzyme's substrate specificity, since at least one third of protein superfamilies are functionally diverse and each superfamily is able to catalyse more than one reaction. The development of a promiscuous enzyme suggestion model relies on NLP deep learning.