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



  • Non-technical description

    One 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 description

    The 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.

date/time interval

  • August 2017 - July 2021