Directed evolution of broadly fungible biosensors

广泛可替代生物传感器的定向进化

• 批准号: 10587024
• 负责人: Andrew D Ellington
• 金额: $ 31.45万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-05 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10587024
• 关键词: 3-Dimensional; Acceleration; Affinity; Algorithms; Alkaloids; Anabolism; Artificial Intelligence; Binding Sites; Bioinformatics; Biosensor; Chemicals; Complex; Computer Analysis; Data; Data Engineering; Databases; Development; Directed Molecular Evolution; Docking; Drug Efflux; Engineering; Enzymes; Evolution; Family; General Practitioners; Genome; Hydrophobicity; Industrialization; Infrastructure; Learning; Libraries; Ligands; Literature; Machine Learning; Medical; Methods; Microbe; Mining; Molecular; Molecular Conformation; Monoterpenes; Organism; Pathway interactions; Protein Engineering; Proteins; Relaxation; Reporting; Residual state; Route; Solubility; Source; Specificity; Structure; Terpenes; Time; Training; X-Ray Crystallography; computational pipelines; convolutional neural network; cost; deep learning model; design; efflux pump; functional improvement; high throughput screening; improved; interest; metabolic engineering; molecular dynamics; neural network; non-Native; novel; protein folding; protein function; quantum chemistry; rational design; receptor; sensor; synergism; tool; transcription factor; virtual

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).

抽象的 新蛋白质生物传感器的开发在很大程度上取决于寻找一种蛋白质 已经响应已知效应子。而存在理性设计和定向演化方法用于更改 转录因子的效应子特异性，这些方法通常是复杂而缓慢的，并且失败了 解决随意鉴定新蛋白质传感器的更普遍的问题。特别是，通常很难 查找对给定最终产品或中间产品既敏感又特异的接收器，即使在努力的情况下 为了生成新的传感器，他们通常会识别结构上与 他们的自然对应物。特别是，几乎所有工业和医学上有用的萜烯都不存在 相应的生物传感器。我们现在建议开发一种合并的计算和定向演化方法 这应该使我们能够从各种各样的“通才”复制品中进行，以创造高度敏感的 以及针对结构多样的萜烯和萜类化合物的特定生物传感器，没有生物传感器是 目前已知。为此，我们开发了一种新颖的定向进化方法来改变生物传感器 特异性和建议将它们与功能强大的机器学习工具合成以改善蛋白质功能。 广泛的初步结果表明，转录因子的Tetr家族可以容易被操纵 采用新的效应子规范，可以使用机器学习来改善各种各样的功能 蛋白质。现在，我们进一步建议将半特异性转录因子确定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规范（AIM 2）；和 通过定向进化和高通量筛选来完善这些预测（AIM 3）。