Large scale ancestral reconstruction of protein sequence, structure and molecular function

使用统计模型来推断祖先分子的序列，使研究人员能够在实验室中直接研究古老的分子。这些研究极大地提高了我们对分子如何工作的理解，并被用于产生新的治疗方法和为生物化学工程工作提供信息。然而，在推断祖先序列时所犯的错误可能会破坏下游的实验室研究。该项目的目标是描述祖先序列重建可能失败的方式和原因，并开发克服这些问题的新方法。这项研究的主要好处是双重的。首先，了解序列重建错误的原因将有助于研究人员更好地评估其结果的可靠性，以便错误的序列不会无意中用于昂贵的功能研究。其次，更可靠的方法的发展将使研究人员能够研究古代分子的功能，否则无法在实验室中进行检查。通过改进用于推断祖先分子序列的方法，这项研究的结果有望提高我们对分子进化过程的理解，并使使用这些方法的研究能够开发有用的生物分子。更广泛的影响活动包括开发新的课程，以培训下一代科学家在祖先序列重建及其用途。开发强大的ASR方法取决于了解ASR何时以及如何失败。利用模拟和生物序列的分析，该项目将测试祖先序列重建对比对模糊性敏感的假设，并将开发新的方法将比对模糊性纳入ASR过程。高通量ASR依赖于对分子功能的准确预测。本项目将测试基于结构的功能预测对基础结构模型中的不确定性敏感的假设，但可以通过优化结构来提高准确性。该项目将ASR方法与基于结构的功能预测相结合，以表征双链RNA结合结构域的大规模进化，该结构域在生命的所有领域中介导蛋白质-RNA和蛋白质-蛋白质相互作用。通过确定蛋白质序列取代，结构变化和大型蛋白质家族的功能进化之间的联系，这项工作有可能改变我们对分子功能进化的一般原则的理解，从根本上推进分子-该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响审查标准。

Using statistical models to infer the sequences of ancestral molecules allows researchers to directly study ancient molecules in the laboratory. These studies have greatly improved our understanding of how molecules work and are being used to generate new therapeutics and inform biochemical engineering efforts. However, errors made when inferring ancestral sequences could undermine downstream laboratory studies. The goal of this project is to characterize how and why ancestral sequence reconstruction might fail and to develop new methods that overcome these problems. The primary benefit of this research is two-fold. First, understanding the causes of sequence reconstruction errors will help researchers better evaluate the reliability of their results, so that erroneous sequences are not inadvertently used in costly functional studies. Second, the development of more reliable methods will allow researchers to study the functions of ancient molecules that could not otherwise be examined in the laboratory. By improving the methods used to infer ancestral molecular sequences, the results of this research are expected to improve our understanding of molecular evolutionary processes and empower studies using these approaches to develop useful biological molecules. Broader impacts activities include developing new courses to train the next generation of scientists in ancestral sequence reconstruction and its uses.Developing robust ASR methods depends on understanding when and how ASR can fail. Using analyses of simulated and biological sequences, this project will test the hypothesis that ancestral sequence reconstruction is sensitive to alignment ambiguity and will develop new methods to incorporate alignment ambiguity into the ASR process. High-throughput ASR depends on accurate prediction of molecular function. This project will test the hypothesis that structure-based function prediction is sensitive to uncertainty in the underlying structural model, but accuracy can be improved by optimizing structures. This project will integrate ASR methods with structure-based function prediction to characterize the large-scale evolution of double-stranded RNA binding domains, which mediate protein-RNA and protein-protein interactions across a variety of gene contexts throughout all domains of life. By determining the links between protein sequence substitutions, structural changes and functional evolution across large protein families, this work has the potential to transform our understanding of the general principles by which molecular function evolves, fundamentally advancing molecular-evolutionary theory and providing a foundation for rational protein engineering.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.