New methods to capture protein dynamics of the TSC-mTOR signalling axis.

蛋白质的灵活性，也就是蛋白质运动的方式，对它们的功能起着重要作用。然而，我们仍然没有工具来分析这种灵活性。我们的细胞进化出了许多复杂而灵活的系统来感知和响应环境。例如，从面包酵母到人类，TSC-mTOR系统在生命中都有发现，但人们对它的了解仍然很少。这项提议将研究TSC作为范例，以开发新的机器学习方法来捕获蛋白质的灵活性和形状。这一建议将促进对TSC-mTOR途径的基本理解，并建立更广泛地研究柔性蛋白质的变革性方法。

Protein flexibility, the way proteins move, has a major role in how they function. However, we still do not have the tools to analyse this flexibility. Our cells have evolved many complex and flexible systems to sense and respond to their environment. For example, the TSC-mTOR system is found across life, from baker’s yeast to humans, however it remains poorly understood. This proposal will study TSC as an exemplar to develop novel machine-learning approaches to capture protein flexibility and shape. This proposal will advance fundamental understanding of the TSC-mTOR pathway and build transformative methodologies to study flexible proteins more broadly.