Directed evolution of broadly fungible biosensors

摘要 新的蛋白质生物传感器的开发在很大程度上依赖于找到一种蛋白质， 已经对已知的效应器有反应了虽然存在理性设计和定向进化方法来改变 转录因子的效应子特异性，这些方法通常复杂且缓慢， 来解决随意识别新蛋白质生物传感器的更普遍的问题。特别是，通常很难 找到对给定的最终产品或中间体既敏感又特异的受体，即使努力 产生新的传感器是成功的，他们通常识别效应器，结构上非常相似， 他们的天然对应物。特别地，对于几乎所有的工业和医学上有用的萜烯，不存在 相应的生物传感器。我们现在提出发展一种结合计算和定向进化的方法 这将使我们能够从各种各样的“通才”抑制剂中的任何一种出发， 以及用于结构多样范围的萜烯和萜类化合物的特定生物传感器， 目前已知。为此，我们开发了一种新的定向进化方法来改变生物传感器 特异性，并建议将这些与强大的机器学习工具协同作用，以改善蛋白质功能。 广泛的初步结果表明，TetR家族的转录因子可以很容易地操纵， 呈现新的效应子特异性，机器学习可用于改善各种各样的功能， 蛋白质。我们现在进一步提出鉴定半特异性转录因子作为生物传感器的起点。 设计和进化（目标1）;使用神经网络方法预测新的传感器特性（目标2）;以及 通过定向进化和高通量筛选来完善这些预测（目标3）。

Abstract The development of new protein biosensors has for the most part been dependent on finding a protein that is already responsive to a known effector. While rational design and directed evolution methods exist for altering the effector specificity of transcription factors, these methods are in general complex and slow, and have failed to solve the more general problem of identifying new protein biosensors at will. In particular, it is often difficult to find a receptor that is both sensitive and specific for a given end product or intermediate, and even when efforts to generate new sensors are successful, they generally recognize effectors that are structurally quite similar to their natural counterparts. In particular, for virtually all industrially and medically useful terpenes there exists no corresponding biosensor. We now propose to develop a combined computational and directed evolution method that should allow us to proceed from any of a wide variety of ‘generalist’ repressors to create highly sensitive and specific biosensors for a structurally diverse range of terpenes and terpenoids for which no biosensors are currently known. To this end, we have developed a novel directed evolution method for altering biosensor specificities, and propose to synergize these with powerful machine learning tools for improving protein function. Extensive Preliminary Results show that the TetR family of transcription factors can be readily manipulated to take on new effector specificities, and that machine learning can be used to improve the function of a wide variety of proteins. We now further propose to identify semi-specific transcription factors as starting points for biosensor design and evolution (Aim 1); use neural network approaches to predict new sensor specificities (Aim 2); and refine these predictions via directed evolution and high-throughput screening (Aim 3).