Can Machine Learning Revolutionize Directed Evolution of Selective Enzymes?

Abstract:
    Machine learning as a form of artificial intelligence consists of algorithms and statistical models for improving computer performance for different tasks. Training data are utilized for making decisions and predictions. Since directed evolution of enzymes produces huge amounts of potential training data, machine learning seems to be ideally suited to support this protein engineering technique. Machine learning has been used in protein science for a long time with different purposes. This mini-review focuses on the utility of machine learning as an aid in the directed evolution of selective enzymes. Recent studies have shown that the algorithms ASRA and InnovQSAR are well suited as guides when performing saturation mutagenesis at sites lining the binding pocket for enhancing stereoselectivity and activity.