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

蛋白质的灵活性，即蛋白质移动的方式，在它们如何发挥作用方面起着重要作用。然而，我们仍然没有分析这种灵活性的工具。我们的细胞已经进化出许多复杂而灵活的系统来感知和响应环境。例如，TSC-mTOR系统存在于从面包酵母到人类的整个生命中，但对它的了解仍然很少。该提案将研究TSC作为开发新的机器学习方法来捕获蛋白质柔性和形状的范例。该提案将推进对TSC-mTOR通路的基本理解，并建立变革性的方法来更广泛地研究柔性蛋白质。

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.