Ernie-Switcher: RNA foundation model-guided design of programmable RNA devices in a navigable landscape
Xin Lei, Runting Li, Yuzhong Liu, Jiesheng Huang, Yicheng Yang, Ruyuan Li, Shimin Liu, Pengxia Wang, Huaiguang Jiang, Wei XiongAbstract
Motivation
Recent advances in biological foundation models have enabled new opportunities for data-driven molecular engineering. However, designing programmable RNA devices remains challenging because of the vast, nonlinear sequence-structure-function landscape. Here, we present Ernie-Switcher, a deep learning framework built upon the RNA foundation model (RNAErnie), comprising three coupled components: (i) integration of RNAErnie-derived sequence-phenotype representations for realistic design generation and filtering; (ii) characterization of RNA landscape navigability as a theoretical basis for sequence optimization; and (iii) incorporation of biophysical constraints to maintain optimization trajectories within biologically plausible bounds.
Results
We validated Ernie-Switcher using RNA toehold switches. Theoretically, the observed lower bound on threshold navigability (86.4%) of the RNA landscape supports the theoretical feasibility of RNA switch sequence optimization. Experimentally, the optimized toehold switches achieved ON/OFF expression ratios of up to 35-fold in Escherichia coli. Collectively, RNAErnie-guided sequence optimization provides a scalable strategy for engineering high-performance RNA devices. To facilitate broad adoption, we deployed Ernie-Switcher as a freely accessible web platform.
Availability
The Python code and pre-trained models are available at https://github.com/zhutiblah/Ernie-Switcher and are archived on Zenodo at https://doi.org/10.5281/zenodo.21859277. The RNA toehold switch design platform is available at https://ernie-switcher.bioinformatics-syn.org/.
Supplementary information
Supplementary data are available at Bioinformatics online.