Di Rienzo Lorenzo, Miotto Mattia, Milanetti Edoardo, Ruocco Giancarlo
Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy.
Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy.
Comput Struct Biotechnol J. 2023 May 9;21:3002-3009. doi: 10.1016/j.csbj.2023.05.004. eCollection 2023.
Organisms have developed effective mechanisms to sense the external environment. Human-designed biosensors exploit this natural optimization, where different biological machinery have been adapted to detect the presence of user-defined molecules. Specifically, the pheromone pathway in the model organism Saccharomyces cerevisiae represents a suitable candidate as a synthetic signaling system. Indeed, it expresses just one G-Protein Coupled Receptor (GPCR), Ste2, able to recognize pheromone and initiate the expression of pheromone-dependent genes. To date, the standard procedure to engineer this system relies on the substitution of the yeast GPCR with another one and on the modification of the yeast G-protein to bind the inserted receptor. Here, we propose an innovative computational procedure, based on geometrical and chemical optimization of protein binding pockets, to select the amino acid substitutions required to make the native yeast GPCR able to recognize a user-defined ligand. This procedure would allow the yeast to recognize a wide range of ligands, without a-priori knowledge about a GPCR recognizing them or the corresponding G protein. We used Monte Carlo simulations to design on Ste2 a binding pocket able to recognize epinephrine, selected as a test ligand. We validated Ste2 mutants via molecular docking and molecular dynamics. We verified that the amino acid substitutions we identified make Ste2 able to accommodate and remain firmly bound to epinephrine. Our results indicate that we sampled efficiently the huge space of possible mutants, proposing such a strategy as a promising starting point for the development of a new kind of S.cerevisiae-based biosensors.
生物体已经进化出有效的机制来感知外部环境。人类设计的生物传感器利用了这种自然优化，其中不同的生物机制已被改造用于检测用户定义分子的存在。具体而言，模式生物酿酒酵母中的信息素途径是一种合适的合成信号系统候选对象。事实上，它只表达一种能够识别信息素并启动信息素依赖性基因表达的G蛋白偶联受体（GPCR），即Ste2。迄今为止，改造该系统的标准方法依赖于用另一种GPCR替换酵母GPCR，并对酵母G蛋白进行修饰以结合插入的受体。在此，我们提出一种基于蛋白质结合口袋几何和化学优化的创新计算方法，以选择使天然酵母GPCR能够识别用户定义配体所需的氨基酸替换。该方法将使酵母能够识别广泛的配体，而无需事先了解识别它们的GPCR或相应的G蛋白。我们使用蒙特卡罗模拟在Ste2上设计了一个能够识别肾上腺素的结合口袋，肾上腺素被选作测试配体。我们通过分子对接和分子动力学对Ste2突变体进行了验证。我们证实我们鉴定出的氨基酸替换使Ste2能够容纳并牢固结合肾上腺素。我们的结果表明，我们有效地对可能的突变体的巨大空间进行了采样，提出这种策略作为开发新型基于酿酒酵母的生物传感器的有希望的起点。
