PROJECT ABSTRACT
Enzymes catalyze selective small-molecule transformations with activity and selectivity that is seldom matched
by non-biological catalysts. Metalloenzymes, such as non-heme iron enzymes (NHIEs), catalyze a diverse array
of reactions involving C–H activation that are relevant to natural product biosynthesis and would be of great
benefit if harnessed for medicinal chemistry. Understanding the role of the secondary sphere in catalysis is
essential for describing how these enzymes work and for designing mimic catalysts capable of similar
transformations under a wider range of conditions (i.e., pH and temperature) amenable to industrial catalysis.
Computational modeling provides insight into the sources of enzymatic rate enhancement, the dynamics of
substrates in the active site, and in the mechanism of biomimetic catalysts, all of which are challenging to
determine experimentally. However, for difficult cases such as NHIEs, existing modeling techniques provide
limited mechanistic insight because they are either too costly, too inaccurate, or require too much existing
knowledge and user intervention. The overall vision for the PI's research program is to develop systematic
methods and novel workflows to overcome cost–accuracy trade-offs in computational modeling for the discovery
of new catalysts and understanding of enzymes. The PI has advanced the first machine learning (ML) models to
discover new transition metal catalysts from millions of candidates, identifying opportunities to overcome trade-
offs in catalyst performance. She has developed novel strategies for unveiling noncovalent interactions in NHIEs
that determine their reaction selectivity and validated her predictions with experimental collaborators. The PI has
advanced methods for making QM/MM systematic and robust and applied them to identifying contributions of
rate enhancement in enzymes to determine where biomimetic counterparts fail. She has developed ML models
to identify and avoid errors in first-principles modeling. The central hypothesis of the proposed research is that
development of novel low-cost methods that enable the generation of larger datasets will reveal structure–
property relationships in enzymatic and biomimetic C–H activation. The rationale is that dynamic effects and
interactions with second-sphere residues that distinguish enzymes that catalyze different reactions cannot be
understood without sufficient sampling and a broad comparison of behavior across the enzyme family. Over the
next five years, the PI will 1) develop models for the prediction of regioselectivity in non-heme iron enzymes
using neural network potentials to enable sampling, 2) systematically determine environmental contributions to
catalysis, and 3) discover bioinspired homogeneous catalysts. The proposed research will produce a framework
for predictive modeling in biological and bioinspired catalysis. The goals build upon methods the PI's lab has
developed for modeling enzymes and non-biological systems and the recent results they have produced related
to ML-accelerated catalyst screening and non-heme iron enzyme modeling.
