CAREER: Protein evolution in high-dimensional sequence space

蛋白质是生物用来进食、生长和繁殖的分子机器；它们随着时间的推移而进化，但我们没有工具来预测它们对新环境的适应。例如，蛋白质进化是细菌对药物产生抗药性的关键。作为另一个例子，工程师可以应用新蛋白质功能的进化来帮助分解漏油。提前预测这些适应性变化将极大地增强我们对适应的理解，但也将为新的生物工具应对环境挑战打开大门。该项目将开发计算工具，帮助科学家预测和了解蛋白质如何进化。为了做到这一点，研究人员正在实验研究从珊瑚中分离出来的色彩鲜艳的蛋白质的进化。然后，他们利用这些实验研究的信息来开发通用的计算模型，以了解和预测蛋白质的进化。这样的工具将是强大的，既可以更有效地进行蛋白质工程，也可以干预抗药性等不良特征的进化。最后，该项目旨在为俄勒冈州的高中STEM教师提供直接参与研究的机会。教师研究经验明显改善了STEM课堂的教育成果。作为蛋白质进化的实验模型，研究人员正在研究一组可能的进化轨迹，这些进化轨迹来自于一种珊瑚属珊瑚的祖先和现代类似绿色荧光蛋白(GFP)的蛋白质。祖先的蛋白质是绿色的；现代的蛋白质是红色的。这一表型变化是通过15次历史更替实现的。研究人员正在构建这些突变的所有组合，并表征每种基因的荧光。这将允许他们绘制连接开始和结束状态的可能轨迹的集合。与此同时，他们正在开发计算工具，以剖析上位性，描述连通性，并预测未测量的表型。这项工作将提供：1)公开可用的高维基因-表型图谱，涵盖新蛋白质功能的进化；2)用于研究进化轨迹的强大软件；以及3)深入洞察高维基因-表型图谱中蛋白质进化轨迹的决定因素。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Proteins are molecular machines that living things use to eat, grow, and reproduce; they evolve over time and yet we have no tools to predict their adaptation to novel environments. Protein evolution is key to how germs become resistant to drugs, for example. As another example, engineers can apply the evolution of new protein functions to help break down oil spills. Predicting these adaptive changes in advance would greatly enhance our understanding of adaptation but would also open the door for new biological tools to confront environmental challenges. This project will develop computational tools to help scientists both predict and understand how proteins evolve. To do so, the researchers are experimentally studying the evolution of brightly colored proteins isolated from coral. They are then using the information from these experimental studies to develop general computational models to understand and predict protein evolution. Such tools will be powerful, enabling both more effective protein engineering and intervention in the evolution of undesired features such as drug-resistance. Finally, the project is designed to provide opportunities for high school STEM teachers in Oregon to directly participate in the research. Teacher research experiences demonstrably improve educational outcomes in STEM classrooms. As an experimental model for protein evolution, the researchers are studying a collection of possible evolutionary trajectories between an ancestral and modern green fluorescent protein (GFP)-like protein from a coral in the genus Favia. The ancestral protein was green; the modern protein is red. This phenotypic change was achieved through 15 historical substitutions. The researchers are constructing all combinations of these mutations and characterizing the fluorescence of each genotype. This will allow them to map the collection of possible trajectories connecting the starting and end states. In parallel, they are developing computational tools to dissect epistasis, to describe connectivity, and to predict unmeasured phenotypes. This work will provide: 1) A publicly available, high-dimensional genotype-phenotype map covering the evolution of a new protein function; 2) Powerful software for studies of evolutionary trajectories; and 3) Deep insight into the determinants of protein evolutionary trajectories in high-dimensional genotype-phenotype maps.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.