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 Saccharomyces cerevisiae 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.

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