Computationally Designed Enzyme Inhibitors Enable Rewiring of Metabolic Networks

Abstract

Traditional metabolic engineering has historically relied on static genetic interventions, such as gene knockouts or constitutive overexpression, to redirect carbon flux. However, these approaches are fundamentally limited by the physiological conflict between cellular growth and product synthesis. While dynamic control strategies like CRISPR interference and targeted proteolysis have been developed to decouple these phases, they primarily operate at the gene level by modulating enzyme abundance. Such coarse regulation can lead to physiological instability when entire enzymes are removed and often relies on host-specific regulatory machinery, limiting portability across different organisms.To address these limitations, this thesis presents a new modality for metabolic control based on computationally designed protein binders that modulate enzyme activity directly through molecular recognition. Glucose 1-dehydrogenase (GDH) was selected as a model system for this proof-of-concept study due to its central role in carbon partitioning within the gluconate-bypass (GBP) strain. Candidate nanobody (VHH) binders were generated using the RFantibody generative design framework, targeting three functionally distinct epitopes: the substrate-binding site (Site A), the catalytic active site (Site B), and the NADP+ binding site (Site C). A multi-stage in silico screening pipeline was implemented to enrich the design pool, utilizing Molecular Mechanics / Generalized Born Surface Area (MM/GBSA) for energetic ranking followed by molecular dynamics (MD) simulations to assess the structural stability and interface integrity of the predicted antibody-enzyme complexes. The results of this study demonstrate that de novo binders can be rationally designed to interact with key functional regions of an enzyme and potentially alter its catalytic activity. Computational analysis revealed that designs targeting Sites A and B achieved more favorable energetic distributions and structural equilibration compared to Site C, likely due to the more complex surface features surrounding those pockets. This work establishes the feasibility of using computational protein design to create post-translational regulatory tools that decouple enzyme abundance from metabolic flux. By providing a modular, portable, and tunable alternative to traditional genetic regulation, this framework offers a promising strategy for the sophisticated rewiring of metabolic networks.