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.
