Engineering enzymes in all their dimensions: from inhibition to biotransformation

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The overarching goal our research is to accelerate the laboratory evolution of applied proteins, by putting more engineering' knowledge-based design and creation into enzyme engineering. With applications ranging from stereoselective reactions for pharma synthesis to large-scale conversion for value-added products and functionalized materials, enzymes accelerate chemical transformations in a globally sustainable way. However, enzymes need to be engineered to broaden the spectrum of reactions they catalyze; we also need knowledge to inhibit enzymes of pharmacological relevance. The design of better enzymes and of their inhibitors is increasingly informed by computational methods that rank hypothetical designs into those most likely to be correct, thereby focussing experimental efforts. Machine learning will dramatically increase the power of computational predictions. The predictive accuracy of machine learning stems from the quality and quantity of the information provided for learning' to occur. The current state of knowledge on the enzyme structure/function relationship does not yet reliably fuel predictive algorithms for enzyme engineering. Our specific focus in the upcoming funding period is to translate molecular details of enzyme function into descriptors for machine-learning algorithms. The descriptors are varied: we will investigate multiple physical and chemical features of enzyme active-sites and of substrate entry/exit paths, by performing cycles of computational simulations, creation of mutated enzyme libraries, functional screening, structural characterization and validation. We will test the predictive capacity of the descriptors by engineering enzymes belonging to different classes because we strive to understand general rules of enzyme engineering rather than overtraining' on single type of enzyme. An important innovation that distinguishes our approach to enzyme engineering is to consider the motions of enzymes. Their motions range from rapid side-chain fluctuations to slow domain motions. This fourth dimension is an intrinsic descriptor of enzyme function and can underlie the synergy exhibited between multiple mutations, particularly when they are focused inside the binding and active-site area. Using experimental and computational methods, we will gain a better understanding of the impact of specific types of protein motions on enzyme catalysis, and how motions play into potential for an enzyme to evolve.Our output will include more powerful inhibitors of an emerging source of antibiotic resistance, and biocatalysts for the efficient generation of value-added products. Importantly, the knowledge gained will feed into calculations to enhance the predictive capacity of computational enzyme engineering.