Robson James M, Green Alexander A
Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.
Biological Design Center, Boston University, Boston, MA 02215, USA.
bioRxiv. 2025 Aug 13:2025.08.12.669990. doi: 10.1101/2025.08.12.669990.
RNA-based biosensors have emerged as essential tools in synthetic biology and diagnostics, enabling precise and programmable responses to diverse RNA inputs. However, the time to design, produce, and screen high-performance RNA sensors remains a critical challenge. The fundamental rules governing RNA-RNA interactions-specifically the structure-function relationships that determine sensor performance-remain poorly understood. Here, we present a method enabling versatile in-silico RNA-targeting analysis (VISTA), a machine learning-guided framework for the rapid design of RNA sensors. VISTA integrates biophysical modeling of both sensor and target RNAs with a partial least squares discriminant analysis (PLS-DA) machine learning framework. Using high-throughput experimental measurements with sequence-structure feature extraction to train predictive models, we capture the key determinants of RNA sensor performance. We find that by using toehold switches as a model RNA sensor, Toehold-VISTA successfully designs RNA sensors with improved function against SARS-CoV-2 RNA. These findings establish a broadly applicable, target-aware design strategy for accelerating RNA sensor engineering across biotechnology and diagnostic applications.
基于RNA的生物传感器已成为合成生物学和诊断学中的重要工具，能够对多种RNA输入产生精确且可编程的响应。然而，设计、生产和筛选高性能RNA传感器所需的时间仍然是一项关键挑战。目前，对于RNA-RNA相互作用的基本规则，尤其是决定传感器性能的结构-功能关系，我们仍知之甚少。在此，我们提出了一种通用的计算机辅助RNA靶向分析方法（VISTA），这是一种机器学习引导的框架，用于快速设计RNA传感器。VISTA将传感器RNA和靶标RNA的生物物理建模与偏最小二乘判别分析（PLS-DA）机器学习框架相结合。通过使用高通量实验测量结合序列-结构特征提取来训练预测模型，我们捕捉到了RNA传感器性能的关键决定因素。我们发现，以toehold开关作为模型RNA传感器，Toehold-VISTA成功设计出了对SARS-CoV-2 RNA具有更好功能的RNA传感器。这些发现为加速跨生物技术和诊断应用的RNA传感器工程建立了一种广泛适用的、目标感知的设计策略。
