CAREER: Evolutionary Principles of Intrinsically Disordered Proteins

内在无序蛋白区域（IDRs）是一种不折叠成特定三维形状但在生物学中发挥重要作用的蛋白质区域。蛋白质是细胞的主力。这些微小的分子由我们的基因编码，执行许多关键的生物功能，从将一种化学物质转化为另一种化学物质到让细胞相互交流。从历史上看，人们认为蛋白质需要折叠成特定的三维形状才能正常工作。然而，在过去的十年中，人们已经清楚地认识到，某些蛋白质区域在没有采用确定的三维形状的情况下发挥着重要作用。这些内在无序的蛋白质区域（idr）存在于大约70%的人类蛋白质中，但由于它们不采用特定的三维形状，因此很难研究。一个特别具有挑战性的领域是理解无序的蛋白质区域是如何进化的。这很重要，因为进化信息对于理解突变如何影响蛋白质功能，设计新的生物启发材料以及阐明细胞如何工作的基本原理至关重要。该项目将开发新的计算方法，以了解与无序蛋白质中进化信息相关的特征类型，并部署一种在实验室中进行加速人工进化的新方法，以研究无序区域如何因进化压力而变化。与此同时，该项目还将通过计算生物学、机器学习和计算机科学的结合来教育下一代科学家，使学生能够开发研究idr的新方法。该项目涉及开发新的计算方法，以识别超越传统的基于对齐的度量的守恒特征。这些工具将使用IDR氨基酸序列信息，并利用它来识别分子相互作用和环境响应背景下的进化保护特征，利用先前开发的定向进化方法（OrthoRep）在不同类型的选择压力下进化无序区域。目的是研究IDR序列在不同类型的进化压力下如何变化，如果来自相同起点的进化“轨迹”沿着序列空间中的相似路径移动，以及如果来自不同起点的轨迹最终在同一位置。总体目标是开发可访问的方法，使其他研究人员能够轻松分析无序区域的保护，同时，扩展对与IDR进化相关的生物物理和生理限制的基础理解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Intrinsically disordered protein regions (IDRs) are protein regions that do not fold into a specific three-dimensional shape yet play essential roles across biology. Proteins are the workhorses of the cell. Encoded by our genes, these tiny molecules perform many key biological functions that range from converting one chemical to another to letting cells talk to each other. Historically, it was assumed that a protein needed to fold into a specific three-dimensional shape to work properly. However, over the last decade, it has become clear that certain protein regions play important roles without adopting a defined three-dimensional shape. These intrinsically disordered protein regions (IDRs) are found in around 70% of human proteins, yet because they don’t adopt a specific three-dimensional shape, they have been hard to study. One area that has been particularly challenging is understanding how disordered protein regions evolve. This is important because evolutionary information is critical for understanding how mutations impact protein function, for designing new bio-inspired materials, and for elucidating the fundamental principles of how cells work. This project will develop new computational methods to understand the types of features associated with evolutionary information in disordered proteins, as well as deploy a new way of doing accelerated artificial evolution in the lab to study how disordered regions change because of evolutionary pressure. In parallel, this project will also educate the next generation of scientists with a mixture of computational biology, machine learning, and computer science, enabling students to develop new methods to study IDRs. This project involves the development of new computational methods to identify signatures of conservation that go beyond conventional alignment-based metrics. These tools will use IDR amino acid sequence information and use it to identify signatures of evolutionary conservation in the context of molecular interactions and environmental responsiveness, taking advantage of a previously developed approach for directed evolution (OrthoRep) to evolve disordered regions under different types of selective pressure. The aim is to study how IDR sequences change under different types of evolutionary pressure if evolutionary ‘trajectories’ from the same starting point travel along similar paths in sequence space, and if trajectories from different starting points end up in the same place. The overall goal is to develop accessible methods to allow other researchers to easily analyze conservation in disordered regions, while, in parallel, expanding the foundational understanding of the biophysical and physiological constraints associated with IDR evolution.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.