RII Track-2 FEC: Using Biophysical Protein Models to Map Genetic Variation to Phenotypes

非技术描述现代生物学的一个重大挑战是理解构成蛋白质的氨基酸的变化是如何导致生物体特征的变化的。这项研究基础设施改进Track-2聚焦EPSCoR合作（RII Track-2 FEC）奖通过使用计算机模拟、数学建模和实验来确定氨基酸变化如何改变蛋白质与其他分子相互作用的方式，从而解决了这一挑战。反过来，这些信息将阐明这些变化如何改变生物体的特征。这项研究将有助于基础科学知识，并将通过推进生物技术、农业和人类健康造福社会。例如，对蛋白质结合的研究将提高预测哪些病原体会逃避药物治疗或哪些分子可能干扰蛋白质功能的能力。该项目将为早期职业教师提供资源支持，并指导他们建立强大的跨学科合作研究项目。它还将为学生和博士后提供合作机会，为他们从事尖端科学事业做好准备。这项研究以团队为基础，涉及爱达荷州、佛蒙特州和罗德岛州的科学家和学生。调查结果将通过互动动画、一个发布结果的网站，以及面向所有三个州的不同受众（包括当地科学中心、学校和地区部落社区）的演讲，与公众分享。该项目将促进多样化、充满活力和可持续的劳动力，并为研究、教育和推广提供机会。技术描述该研究将利用蛋白质生物物理工具开发基因组到表型分析，预测突变和突变组合对广泛系统的影响。该项目的核心科学假设是，蛋白质生物物理模型为预测突变如何影响蛋白质稳定性、对底物和伴侣的亲和力以及对更高水平表型的映射提供了有效的框架。为了验证这一假设，研究小组将通过计算预测突变对折叠和结合稳定性的影响，并通过蛋白质纯化、圆二色性和等温滴定量热法对预测进行实验验证。首先使用计算方法来选择生物物理上可行的突变体将导致有效的实验工作。最初的研究将集中在-内酰胺酶和呼吸道合胞病毒F蛋白上，因为它们代表了两种基本的相互作用类型：蛋白质-底物和蛋白质-蛋白质。研究人员将研究单个突变和突变组合如何影响这两个系统中的生物物理表型及其对更高水平表型的映射。进一步的研究将集中在更大的，自然发生的，突变组合和环境在修改基因型到表型映射中的作用。这项研究将确定基因型到表型映射的一般和系统特定的经验教训，例如生物物理和更高水平的表型显示上位性的频率，以及模型如何很好地预测加性的偏差。参与早期职业生涯的教师将获得学术生涯发展的指导，并将有专业发展的交流机会。该项目将开发一个网站，以帮助交流项目结果、工具和活动。学生和科学家将与动画师、虚拟技术和设计专家合作，制作名为“geno - phenomena - mations”的互动动画，向公众传播项目成果。

Non-technical descriptionOne of the great challenges in modern biology is understanding how changes in amino acids that are the building blocks of proteins lead to changes in the characteristics of a living organism. This Research Infrastructure Improvement Track-2 Focused EPSCoR Collaborations (RII Track-2 FEC) award addresses this challenge by using computer simulations, mathematical modeling, and experiments to determine how amino acid changes modify the way that proteins interact with other molecules. This information, in turn, will elucidate how these changes modify the characteristics of organisms. This research will contribute to basic scientific knowledge and will benefit society by advancing biotechnology, agriculture, and human health. The work on protein binding, for example, will improve the ability to predict which pathogens will evade drug therapy or which molecules might interfere with protein function. The project will provide resources to support early-career faculty and mentor them in building robust, interdisciplinary, collaborative research programs. It will also provide collaborative opportunities to students and postdocs and prepare them for cutting-edge scientific careers. The research is team-based, involving scientists and students in Idaho, Vermont, and Rhode Island. Findings will be shared with the public via interactive animations, a website for dissemination of results, and presentations for diverse audiences in all three states including local science centers, schools, and regional Tribal communities. The project will promote a diverse, vibrant, and sustainable workforce and provide opportunities for research, education, and outreach.Technical descriptionThe research will utilize protein biophysical tools to develop genome to phenome analyses that predict the impact of mutations and combinations of mutations on a broad range of systems. The central scientific hypothesis for the project is that protein biophysical models provide an efficient framework for predicting how mutations influence protein stability, affinity for substrates and partners, and the mappings to higher-level phenotypes. To test this hypothesis, the research team will computationally predict the effect of mutations on folding and binding stabilities and experimentally validate the predictions by protein purification, circular dichroism, and isothermal titration calorimetry. The use of computational approach first to select biophysically viable mutants will lead to efficient experimental efforts. Initial studies will focus on beta-lactamase and the respiratory syncytial virus F protein because they represent two fundamental interaction types: protein-substrate and protein-protein. The researchers will investigate how single mutations and combinations of mutations affect biophysical phenotypes and their mappings to higher-level phenotypes in these two systems. Further studies will focus on larger, naturally occurring, mutational combinations and the role of the environment in modifying the genotype to phenotype mappings. The research will identify generic and system-specific lessons about the mapping of genotypes to phenotypes, such as how often biophysical and higher-level phenotypes show epistasis and how well models predict deviations from additivity. Participating early career faculty will receive mentoring for advancement in their academic career and will have networking opportunities for professional development. The project will develop a website to aid in the communication of project results, tools, and activities. Students and scientists will work with animators and virtual technology and design experts to produce interactive animations called "Geno-Pheno-Mations" to disseminate the project results to the general public.