Computer-directed evolution of enzyme selectivity for bioproduct synthesis

Machine learning techniques can be applied to comprehensive datasets (e.g., protein sequences, 3D structural, functional annotations, and enzymatic activities, etc.) to enable more efficient and accurate predictive modeling. Chapter 1 covers the theoretical background and computational methods, including traditional structure-based rational design approaches and the fundamental concepts of the state-of-the-art machine learning techniques.

Chapter 2 presents a case study demonstrating rational design strategies where key catalytic motifs identification and co-evolution analysis were employed to guide the selective synthesis of bicyclogermacrene enantiomers. Chapter 3 presents a case study on the selective synthesis of farnesene diastereomers guided by computational simulations, combining structural modelling, molecular docking and molecular dynamics simulations.

Chapter 4 presents theoretical study on the origin of enantioselectivity in alcohol dehydrogenase using QM/MM methods. Chapter 5 presents how machine learning algorithms were utilized in overcoming the limitations of rational enzyme design in sequence landscape exploration, we were able to obtain enantioselective products by large-scale predictions and evaluation of mutations. Chapter 6 concludes this thesis and summarizes the strategy of integrating computational simulations and machine learning techniques in enzyme design.<br><br><i>Thesis embargoed until 31st July 2030</i>