A major challenge in biochemistry is to understand how enzymes achieve remarkable catalytic power under mild conditions. While mechanisms of activity for hundreds of enzymes have been examined, nearly all of these studies focus on amino acid residues in direct contact with the reacting substrate molecule(s). However, our recent computational and experimental work demonstrates that distal residues can make significant contributions to catalysis. Most importantly, <b>the participation of specific distal residues in catalysis is predictable computationally</b>. Partial Order Optimum Likelihood (POOL) is a machine learning method developed by us to predict residues important for function, using the 3D structure of the query protein as input. POOL input features, based on computed electrostatic and chemical properties, are effectively measures of the strength of coupling between protonation events. Catalytic sites in proteins are characterized by networks of strongly coupled protonation states; these networks impart the necessary electrostatic and proton-transfer properties to the active residues in the first layer around the reacting substrate molecule(s). Typically these networks include not only first-layer residues but also residues in the second and third layers. POOL-predicted, multi-layer active sites, with significant participation by distal residues, have been verified experimentally by site-directed mutagenesis and kinetics assays for nitrile hydratase, phosphoglucose isomerase, and ornithine transcarbamoylase. For natural enzymes, we calculate high THEMATICS metrics in all of the catalytic residues; mutations to coupled residues in the second and third layers result in observed reduction in catalytic efficiency and also in reduction in THEMATICS metrics for at least one of the important first-layer residues. In designed enzymes that have not been fully evolved, these networks have not been optimized and it is possible to identify mutations in distal positions that increase THEMATICS metrics for all of the ionizable first-layer residues, indicating a stronger network of coupled protonation states. These metrics tend to rise as directed evolution proceeds to yield designed enzymes with higher activity. The importance of coupled protonation states and of distal residues in enzyme catalysis are features that should be considered in enzyme design.

Spatially extended active sites: Building favorable electrostatic interactions in natural and designed enzymes

253rd National Meeting (2017)

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