Expanding genetic toolbox for heterologous protein expression in non-conventional yeasts using artificial intelligence.

异源蛋白质表达是为改善我们的食物来源和创造救命药物而产生的重要蛋白质的生产。然而，预测将产生多少蛋白质是困难的，因为我们并不完全了解基因是如何调控和控制的。一种生产蛋白质的方法是使用某些类型的酵母，称为非常规酵母(NCys)。这些酵母的特性使它们更善于生产蛋白质，例如使用复杂的碳源，并且在基因上更适合生产特定的产品。然而，我们没有足够的关于这些酵母的信息来有效地利用它们来生产蛋白质。为了利用NCys生产蛋白质，我们需要开发工具来控制每个宿主的基因表达。我们正在使用机器学习来帮助我们预测基因将如何受到调控，并使使用NCys生产蛋白质变得更容易。这将帮助我们创造可持续、低碳和具有成本效益的蛋白质生产方法。我们将在大约100个NCys中测试大约10,000个针对高蛋白表达进行优化的基因，看看我们是否可以控制这些酵母中的基因表达。这将帮助我们了解基因表达以及如何控制它，这可能会对生物技术产生重大影响。我们的最终目标是使用NCys以可持续和低成本的方式生产蛋白质。

Heterologous protein expression is the production of vital proteins for improving our food sources and creating life-saving drugs. However, predicting how much protein will be produced is difficult because we do not fully understand how genes are regulated and controlled. One way to produce proteins is by using certain types of yeast called Non-Conventional Yeasts (NCYs). These yeasts have traits that make them better at producing proteins, such as using complex carbon sources and being better suited genetically for producing specific products. However, we do not have enough information about these yeasts to use them effectively for protein production. To use NCYs for protein production, we need to develop tools to control gene expression in each host. We are using machine learning to help us predict how genes will be regulated and to make it easier to use NCYs for protein production. This will help us create sustainable, low-carbon, and cost-effective methods for producing proteins. We will test around 10,000 genes optimized for high protein expression in about 100 NCYs to see if we can control gene expression in these yeasts. This will help us understand gene expression and how it can be controlled, which could significantly impact biotechnology. Our ultimate goal is to use NCYs to produce proteins sustainably and cost-effectively.