Understanding how genotypes map to organismal phenotypes is one of the great unsolved problems in modern biology. This research is built on the principle that the biophysical properties of proteins represent general, computationally-predictable intermediaries for mapping genotypes to phenotypes. The central scientific hypothesis for the project is that protein biophysical models provide an efficient framework for predicting how mutations – alone, in combination, and in different environments -influence protein stability, affinity for substrates and partners, and the mappings to higher-level phenotypes. To test this hypothesis, an interdisciplinary team was assembled spanning three jurisdictions (Idaho, Rhode Island, and Vermont) with the necessary expertise in computational protein modeling, diverse biological systems, measurement of protein biophysical properties, and mathematical modeling.
The methodological hypothesis for the project is that a biophysics-first framework can be applied to diverse experimental systems to reveal general underlying principles of genotype to phenotype mapping, uncover gaps in understanding, and suggest potential generic and system-specific extensions. Initially, beta-lactamase and the respiratory syncytial virus F protein will be used as testbed systems because they represent two fundamental interaction types: protein-substrate and protein-protein. The objectives during the first 1.5 years are to investigate how single mutations (Objective 1), and combinations of mutations (Objective 2), affect biophysical phenotypes and their mappings to higher-level phenotypes. In years 2-4, lessons from Objectives 1 and 2 will be applied to other model systems (Objective 3), to consideration of larger, naturally-selected, mutational combinations (Objective 4), and to the role of the environment in modifying genotype to phenotype mappings (Objective 5).
To the team’s knowledge, this is the first attempt to test the ability of protein models to predict how mutations affect biophysical and higher-level phenotypes across such a broad range of systems. Among the vast number of possible mutational combinations, only a small fraction are expected to be viable. The proposed research has the potential to develop tools to predict these rare combinations, which would be nearly impossible to identify empirically. The research will identify generic and system-specific lessons about the mapping of genotypes to phenotypes, such as how often do biophysical and higher-level phenotypes show epistasis and how well can models predict deviations from additivity.
The research has the potential to benefit society by leading to advances in biotechnology and health sciences. For example, the work on protein-protein binding will improve the ability to predict which mutant pathogens will evade therapy.
The team consists of ten faculty members (four early-career), four postdoctoral associates, eight graduate students, and six undergraduate students. Goals and activities for each of these groups will lead to a diverse and vibrant workforce, with mentoring and resources to support early-career faculty in achieving tenure and promotion, and professional development for postdocs. Interactive animations will be created to engage the general public in research findings. A website will aid in dissemination of project results, tools, activities, and broader impacts. Presentations will be created and delivered at diverse venues, local science centers and schools, and regional Tribes.
Long-term sustainability of the interjurisdictional collaborations will be supported by establishing an infrastructure for project coordination, supporting team collaboration, and reducing barriers to participation.
Stay completely up-to-date on the RII Track-2 FEC: Using Biophysical Protein Models to Map Genetic Variation to Phenotypes (National Science Foundation award OIA-1736253) via the Genotypes to Phenotypes website.