Controlling Protein Post-translational Modification by Separating Affinity and Catalysis in Designer Enzymes

University Of North Carolina At Chapel Hill, Chapel Hill NC

Investigators

Abstract

With the support of the Chemistry of Life Processes (CLP) program in the Chemistry Division, Dr. Albert A. Bowers from the University of North Carolina at Chapel Hill will investigate methods for making artificial enzymes. Enzymes are proteins that act as the key drivers of metabolic transformations and processes in all cell-based life. Enzymes are also increasingly important ‘green’ tools for a variety of industrial processes, including the make and manufacture of pharmaceuticals, materials, fuels, and foods. Despite significant advances in computational protein design, the creation of useful, efficient, artificial enzymes remains a major challenge. This is due to the need to fashion proteins that are capable of simultaneously 1) holding a substrate (or binding) and 2) modifying the substrate (or catalysis). The proposed experiments will test a potentially generalizable strategy for enzyme design that separates substrate binding and catalysis for peptide-based substrates. This work employs cutting edge technologies and will provide rich training grounds for graduate students in high-throughput experimentation, DNA-encoded technology, computational protein design, and machine learning and artificial intelligence. These methods will also form the foundations of a focused outreach program to bring emerging concepts in biotechnology to the broader public. Ultimately, if successful, this research has the potential to open up new avenues for the design of enzyme function and enable the biocatalytic synthesis of peptides with new properties and biomaterials applications. This research project seeks to quantitatively characterize efficiency gains in designer peptide post-translational modification (PTM) enzymes comprised of separate substrate recruitment and modification domains. This constitutes a potentially generalizable approach to creating artificial PTM enzymes. Rosetta design suite will be used to fashion designer interfaces between the fyn-SH3 substrate recruitment domain and two different catalytic domains for comparison against naive (or undesigned) fusions as well as the parent, wild-type catalytic subunit in kinetic assays. Generalizability and flexibility of the strategy will be measured by an innovative, mRNA display-based substrate display assay approach and results analyzed and integrated with advanced machine learning (ML) methods. These model enzymes will further be employed to assess the additive effect of the design strategy on small, designer biosynthetic pathways. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.