Proteins are biomolecules that carry out the cellular processes that are fundamental to life. It has recently become well-established that concerted motions (dynamics) within proteins are directly correlated to their function, but an incomplete understanding of this relationship currently exists. To further elucidate the link between protein dynamics and function, recently developed computational methods will be used to first identify and subsequently classify networks of interactions in proteins that are directly responsible for defining the motions that underpin their functions. Information regarding these networks will be organized in a database that will be made freely available to researchers interested in both studying protein function or rationally engineering proteins that possess desired functions. The potential utility of this database will then be directly tested by using the information contained therein to improve protein engineering efforts. Enhanced proteins generated using this database will be experimentally characterized in an effort to both validate and subsequently improve the information contained therein. This will not only lead to a deeper understanding of the relationship between protein dynamics and protein function but will also find immediate use in myriad biotechnological applications including the development of new protein-based drugs, enhanced agricultural products or the production of new, functional biomaterials. An enhanced understanding of the relationship between protein dynamics and function can also readily be incorporated into educational materials using a popular online protein folding game, FoldIt, which will be used in outreach activities that seek to inspire interest in STEM fields at the high school educational level.

The primary goals of the proposed research are 1) to better elucidate the complex relationship between protein dynamics and function and 2) to develop new computational protein design algorithms that enable the rational design of functional proteins from scratch. Our current understanding of the relationship between protein sequences, their structures and functions has benefitted substantially from the aggregation and dissemination of this information in publicly accessible databases. Although a direct link between correlated motions in proteins and the functions they carry out is well-established, no database of protein dynamics currently exists, which we believe severely limits our ability rationally engineer functional proteins. We will address this challenge by generating the first large-scale database of the dynamic signatures of proteins of known function and then cluster these data both within and across enzyme functional classes. Our findings will be incorporated into the Rosetta protein design software to develop dynamics-based computational protein design methods that will subsequently be used to engineer new dynamic networks within proteins. This will serve as a direct test of our understanding of the relationship between protein structure, dynamics and function. Experimental characterization of our designed proteins will serve to improve our computational methods and will further enhance our understanding of the interplay between dynamics and protein function. The long-term goal of this study will be to use the lessons learned in this study to generate cutting edge computational protein design tools that could be used to study enzyme function, enhance the catalytic efficiencies of existing designed enzymes and enable the de novo design of artificial enzymes with desired activities.Results of this project will be reported at and

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.

National Science Foundation (NSF)
Division of Biological Infrastructure (DBI)
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Jean Gao
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Arizona State University
United States
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