Developing Computational Tools for Predicting and Designing Function-Enhancing Enzyme Variants

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Project Summary The a priori prediction and design of efficient mutant enzymes are broadly recognized as a “Holy Grail” in chemistry and biology because it will allow researchers to find effective enzyme variants to degrade environ- mental pollutants, conduct late-stage functionalization of fine chemicals, and treat diseases. Directed evolution has been widely applied to identify optimal enzyme variants for chemical reactions, but how to accelerate the screening cycles remains a critical roadblock due to the unknown relationship between sequence, structure, and kinetics for enzyme catalysis. To overcome this challenge, the PI has been developing three computational in- frastructures: 1) an integrated enzyme structure-function database, IntEnzyDB, that provides clean and tabulated data for data-driven modeling; 2) a new software module, RosettaQM, for evaluating enzyme-reacting species interactions using quantum mechanical methods; and 3) a high-throughput workflow, EnzyHTP, that allows com- putational screening of enzyme variants. Enabled by these tools, in this MIRA proposal, the PI emphasizes advancing new computational tools to predict and design new enzyme catalysts. First, the PI will develop a multistate kinetic scoring function to predict the influence of mutation on apparent enzyme kinetics by leveraging the enzymology data stored in IntEnzyDB and the QM-based enzyme-reacting species interaction scoring in RosettaQM (Project-1). The PI plans to develop a kinetic scoring function that accounts for contributions of mul- tiple reactive states along a reaction pathway and is distinct from existing computational rational engineering strategies that emphasize the stabilization of one hypothetical transition state. The multistate kinetic scoring will be experimentally validated to predict efficiency-enhancing mutations for FR29 esterase for a proof of concept and fluoroacetate dehalogenase FAcD for environmental pollutant degradation. Second, the PI will develop an integrated enzyme predicting protocol that augments molecular simulations and machine-learning models to design enzyme variants to accommodate non-native substrates for late-stage functionalization of drug-like mol- ecules (Project-2). EnzyHTP will be further developed to incorporate machine learning models to achieve multi- objective prediction of beneficial mutations, evaluating the impact of mutations on enzyme electrostatic environ- ment, substrate positioning, substrate-enzyme interactions, and enzyme stability, solubility, and promiscuity. En- zyHTP will be experimentally examined in the design of new group-transferases to accommodate S-adenosyl methionine analogues for late-stage functionalization of everninomicin, a promising drug for treating a broad spectrum of antibiotic-resistant bacterial infections. In summary, the proposed research will deliver enabling computational tools for virtual prediction and design of function-enhancing enzyme variants for biomedical and biocatalytic uses.