Riboswitch development for clinical, technological, and synthetic biology applications constantly seeks to optimize regulatory behavior. Here, we present a machine learning approach to improve the regulation of a tetracycline (tc)-dependent riboswitch device composed of two individual tc aptamers. We developed a bioinformatics model that combines random forest analysis with a convolutional neural network to predict the switching behavior of such tandem riboswitches. We found that both biophysical parameters and the hydrogen bond pattern influence regulation. Our new design pipeline led to significant improvement of the tc riboswitch device with a dynamic range extension from 8.5 to 40-fold. We are confident that our novel method not only results in an excellent tc-dependent riboswitch device but further holds great promise and potential for the optimization of other riboswitches.

中文翻译：

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使用机器学习调整合成核糖开关的性能。

用于临床，技术和合成生物学应用的Riboswitch开发一直在寻求优化监管行为。在这里，我们提出了一种机器学习方法，以改善由两个单独的tc适体组成的依赖四环素（tc）的核糖开关设备的调节。我们开发了一种生物信息学模型，该模型结合了随机森林分析和卷积神经网络来预测此类串联核糖开关的开关行为。我们发现，生物物理参数和氢键模式都影响调节。我们的新设计流水线显着改进了tc核糖开关设备，其动态范围从8.5倍扩展到了40倍。