AI-directed gene fusing prolongs the evolutionary half-life of synthetic gene circuits.

Abstract

Evolutionary instability is a persistent challenge in synthetic biology, often leading to the loss of heterologous gene expression over time. Here, we present STABLES, a gene fusion strategy that links a gene of interest (GOI) to an essential endogenous gene (EG), with a "leaky" stop codon in between. This ensures both selective pressure against deleterious mutations and the high expression of the GOI. By leveraging a machine learning framework, we predict optimal GOI-EG pairs on the basis of bioinformatic and biophysical features, identify linkers likely to minimize protein misfolding, and optimize DNA sequences for stability and expression. Experimental validation in <i>Saccharomyces cerevisiae</i> demonstrated substantial improvements in stability and productivity for fluorescent proteins and human proinsulin. The results highlight a scalable, adaptable, and organism-agnostic method to enhance the evolutionary stability of engineered strains, with broad implications for industrial biotechnology and synthetic biology.