An evolution-based model for designing chorismate mutase enzymes

TLDR

The rational design of enzymes is an important goal for both fundamental and practical reasons. We describe a process to learn protein constraints from evolutionary sequence data, design and build synthetic gene libraries, and test them for activity in vivo using a quantitative complementation assay. The approach learns constraints from evolutionary data, constructs synthetic gene libraries, evaluates them in vivo, and further optimizes the generative model toward function in a specific genomic context. For chorismate mutase, we achieved natural‑like catalytic function with substantial sequence diversity, demonstrating that sequence‑based statistical models can specify proteins and unlock an enormous space of functional sequences, thereby establishing a foundation for evolution‑based design of artificial proteins.

Abstract

The rational design of enzymes is an important goal for both fundamental and practical reasons. Here, we describe a process to learn the constraints for specifying proteins purely from evolutionary sequence data, design and build libraries of synthetic genes, and test them for activity in vivo using a quantitative complementation assay. For chorismate mutase, a key enzyme in the biosynthesis of aromatic amino acids, we demonstrate the design of natural-like catalytic function with substantial sequence diversity. Further optimization focuses the generative model toward function in a specific genomic context. The data show that sequence-based statistical models suffice to specify proteins and provide access to an enormous space of functional sequences. This result provides a foundation for a general process for evolution-based design of artificial proteins.