Designing interactions mediated by disordered protein regions

蛋白质是细胞的主要成分，它们几乎参与了细胞内发生的几乎所有过程。它们做出贡献的关键是调节细胞功能和传递信号的相互作用。在过去的三十年里，在分子内和分子间蛋白质相互作用的结构和功能表征方面取得了巨大的进展。此外，还开发了计算和实验蛋白质工程工具，使我们能够设计和选择具有所需相互作用特征的新蛋白质。这一进展对折叠成明确的三级结构的“经典”蛋白质尤其有影响。然而，许多蛋白质含有大的固有无序区(IDR)。对于这些通常更具活力的蛋白质元件，在结构表征和功能重组方面的进展要温和得多。鉴于IDR在变构和空间活性调节中的重要作用，以及调节和信号网络中枢轴蛋白的动态组装，显然我们需要开发新的工具，使我们能够更好地理解IDR介导的相互作用，并设计具有所需相互作用性质的IDR。 在这个研究项目中，我们建议采用计算和实验相结合的方法，以便建立新的管道，以成功地设计具有所需相互作用性质的IDR。在我们的第一个目标中，我们将开发基于深度学习的工具来识别IDR，这些工具通过瞬时的分子内相互作用，充当蛋白质活性的开关。利用这些新工具和基于分子动力学(MD)模拟的大规模突变筛选，我们将设计具有特定开关行为的IDR的转录因子。在第二个目标中，我们将使用基于流式细胞仪的筛选方法结合深度学习来识别哪些序列特征分别赋予IDR相分离能力，定位应激颗粒的能力。我们将利用已学到的知识来设计具有可调相分离行为的IDR，即所需的分子间相互作用特性。 这里提出的研究计划旨在为开发最近进化的和非常多功能的IDR开辟新的途径，这将对我们设计蛋白质内部和蛋白质之间的相互作用的能力产生重大影响，从而引导特定的细胞过程在学术界和工业中的应用。

Proteins are the principle constituents of cells, and they are involved in nearly all processes that take place in cells. Key to their contributions are interactions that regulate cellular functions and transmit signals. In the last three decades, tremendous progress has been made in the structural and functional characterization of intra and intermolecular protein interactions. Moreover, computational and experimental protein engineering tools have been developed that enable us to design and select for new proteins with desired interaction features. This progress has been particularly impactful for “classical” proteins that fold into well-defined tertiary structures. However, many proteins contain large intrinsically disordered regions (IDRs). For these generally more dynamic protein elements, progress in structural characterization and functional reengineering has been much more modest. Given the important role IDRs play in allosteric and steric activity regulation as well as dynamic assembly of pivot proteins in regulatory and signaling networks, it is clear that we need to develop new tools that allow us better understand IDR-mediated interactions as well as engineer IDRs with desired interaction properties. In this research program, we propose to employ a combination of computational and experimental approaches in order to set up new pipelines for the successful engineering of IDRs with desired interaction properties. In our first aim, we will develop deep learning-based tools for the identification of IDRs that, via transient intramolecular interactions, act as switches of protein activity. Taking advantage of these new tools and large-scale, molecular dynamics (MD) simulation-based mutation screens, we will then design transcription factors that have IDRs with specific switch behaviour. In the second aim, we will use a flow cytometry-based screening method in combination with deep learning to identify which sequence features confer phase-separation capabilities to IDRs, respectively, the ability to locate to stress granules. We will exploit what has been learned to design IDRs with tunable phase separation behaviors, i.e. desired intermolecular interaction properties. The research program proposed here aims to open up new avenues for the exploitation of the recently evolved and very versatile IDRs, which will have significant impact on our ability to design interactions within and between proteins and thus steer specific cellular processes for applications in academia and industry.