CAREER: Developing Novel Models of Sequence Evolution for Protein Design and Molecular Recognition

该项目旨在通过研究生物分子（如蛋白质和RNA）如何相互作用以产生功能复合物来提高我们对分子进化过程的理解。这是一项具有挑战性的任务，因为破坏功能的可能分子相互作用的数量大大超过了保留或增强功能的数量。为了克服这一挑战，将采用计算方法从已知的蛋白质和RNA序列数据中得出结论。这项研究的结果应该使我们能够预测导致功能性分子相互作用的规则。反过来，发现的规则应该揭示历史如何塑造这些分子的功能以及如何设计新分子以获得所需特性的进化见解。该项目将涉及本科生和研究生创造新的工具，以促进对生物分子及其功能的理解。将开发3D打印技术和交互软件，让普通观众参与构建和操纵真实生物分子的模型。这种互动的，动手的策略被期望作为一种有效的机制来教授生物分子相互作用的基本原理。在这项研究中，将使用来自不同学科的工具，包括生物物理学、信息论、计算和进化生物学以及测序技术，来研究分子相互作用的进化效应。一个关键的方法是开发新的进化模型，使用统计推断来帮助统一现有的基于序列的模型的特性，同时提供一个框架来理解功能变化和分子设计。前提是，在一个新的进化模型中纳入上位性贡献可能会提高与自然序列特性的一致性，并有助于在现有家族成员之外设计功能性蛋白质。该项目将把蛋白质-蛋白质相互作用的假设扩展到蛋白质和核酸之间的假设。整合测序技术和计算方法将能够推断蛋白质-核苷酸识别的突变景观。然后，推断的景观将用于开发一个框架来预测和编码特定的识别，并且预测将在相关的RNA和dna结合蛋白中进行实验测试。该项目由生物科学理事会分子生物科学部的遗传机制和分子生物物理学项目共同资助。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。

This project aims to improve our understanding of the process of molecular evolution by studying how biomolecules, like proteins and RNA, interact with one another to produce functional complexes. This is a challenging task, given that the number of possible molecular interactions that disrupt function vastly exceeds the number that preserves or enhances function. To overcome this challenge, computational methods will be implemented to draw conclusions from known protein and RNA sequence data. The results from this research should allow us to predict the rules that lead to functional molecular interactions. In turn, the discovered rules should reveal evolutionary insights of how history shaped the function of such molecules and how new molecules could be engineered with desired properties. The project will involve undergraduate and graduate students in creating new tools to promote the understanding of biomolecules and their functions. 3D printing technologies and interactive software will be developed to engage general audiences in building and manipulating models of real biological molecules. This kind of interactive, hands-on strategy is expected to serve as an effective mechanism for teaching the fundamental principles of biomolecular interactions. For this research, tools from different disciplines, including biological physics, information theory, computational and evolutionary biology as well as sequencing technologies, will be implemented to study evolutionary effects on molecular interactions. A key approach is to develop novel evolutionary models that use statistical inference to help unify properties of existing sequence-based models and at the same time provide a framework to understand functional change and molecular design. The premise is that incorporating epistatic contributions in a novel evolutionary model might improve agreement with properties of natural sequences as well as help engineer functional proteins outside of the extant set of family members. The project will expand hypotheses for protein-protein interactions to those between proteins and nucleic acids. Integrating sequencing technology and computational approaches will enable inference of mutational landscapes of protein-nucleotide recognition. The inferred landscapes will then be used to develop a framework to predict and encode specific recognition, and the predictions will be tested experimentally in relevant RNA- and DNA-binding proteins. This project is co-funded by the Genetic Mechanisms and Molecular Biophysics Programs in the Division of Molecular Biosciences in the Biological Sciences Directorate.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.