Career: new routes to designing dynamic protein structures

The National Science Foundation Division of Molecular and Cellular Biosciences awarded $763,328 to the Research Foundation of the City University of New York (doing business as the Advanced Science Research Center) on June 15, 2026, under the Biological Sciences program (CFDA 47.074) to develop artificial intelligence-driven methodologies for designing dynamic protein structures.

The project will test whether existing designed proteins exhibit different conformational dynamics than natural proteins and use AI to design new proteins with varied motional characteristics. The work aims to move beyond conventional protein design approaches that target a single rigid structure toward designs that incorporate the movement natural proteins require to function—including induced-fit binding, catalytic motions, and allosteric conformational changes. The research will generate designed proteins applicable to disease biomarker detection, clean fuel generation, drug transport, and toxin defusal. Public engagement activities include AI-powered protein design competitions in classrooms nationwide and science comedy events.

Performance will take place in New York, New York. The award obligates $763,328 over a period of performance extending from June 15, 2026, through May 31, 2031. This is a CAREER-track award under NSF's Biological Sciences program, aligning with NSF priorities in artificial intelligence, biotechnology, and advanced manufacturing.

Abstract

Natural proteins perform critical functions in our bodies. Proteins can be designed to perform new functions that help society. For example, designed proteins could detect diseases, transport drugs, generate clean fuels, and defuse toxins. However, designed proteins perform worse than natural ones. This project aims to use lessons from natural proteins to produce better designed proteins. This will be done by testing whether existing designed proteins move differently than natural ones. The work will also use artificial intelligence (AI) to design new proteins with different types of motions. Overall, this research will leverage AI to help design powerful new proteins. This will pave the way for the use of designed proteins in biotechnology, medicine, and chemical manufacturing. To connect with the public, AI-powered protein design competitions will be held in classrooms nationwide, along with science comedy events. This project advances NSF?S priorities in AI, biotechnology, and advanced manufacturing. Protein design has vast potential to generate new proteins that address unmet societal needs. For example, designed proteins could detect disease biomarkers, catalyze the creation of clean fuel sources, and defuse toxic chemicals in our environment. However, the functions of designed proteins to date pale in comparison to those of natural proteins, preventing US from achieving these goals. Notably, most protein design efforts to date have focused on stabilizing a single rigid target structure. By contrast, natural proteins must move to function, including induced fit upon ligand binding, catalytic motions in enzymes, and allosteric conformational changes. The hypothesis underlying this project is that existing designed proteins populate unnatural conformational landscapes that are too rigid, too dynamic, or otherwise different from those of highly functional natural proteins ? And that explicitly designing for desired conformational landscapes will improve the functions of designed proteins. The recent revolution in fast, highly accurate AI-based protein design and structure prediction algorithms presents a prime opportunity to test these ideas. First, patterns of ?hidden? Conformational landscapes will be contrasted in crystal structures of traditional physics-based designed proteins, new AI-based designed proteins, and fold-matched natural proteins. Second, sequence space and conformational landscapes will be iteratively explored with AI-based methods for rigid VS. Dynamic proteins. Third, the set of conformational inputs needed for efficient enzymatic function will be defined using crystallographic ensembles and AI-based multistate design, coupled to biochemical and biophysical experiments. This project will illuminate new paths forward for rational design of functional proteins to address myriad challenges in biotechnology, medicine, and other industries, and will help prepare the next generation of AI-aware protein scientists. 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.- subawards are not planned for this award.