AI-discovered protein fragments as generalizable regulators of biomolecular condensates

Abstract Biomolecular condensates are a major driver of cellular organization; however, we lack a predictable and systematic approach to modulate the multivalent interactions underlying their formation. Here, we demonstrate that the AI-driven FragFold method enables robust and generalizable design of protein fragments to control biomolecular condensate formation. We apply this approach across diverse proteins: G3BP1, SARS-CoV-2 nucleocapsid, TDP-43, and focal adhesion kinase (FAK). Computationally screening 2,235 fragments, we selected 18 candidates for further investigation. Overall, we attain a 50% success rate (9/18 designs) in discovering condensate-controlling protein fragments, experimentally testing just 3-5 candidates per protein. For each condensate-forming protein, the success rate is at least 40%. Furthermore, FragFold-predicted fragment binding modes align with their condensate-inhibitory or -enhancing activities, revealing both known and newly identified interactions underlying condensate formation. In FAK, a condensate-inhibitory fragment uncovered a domain interaction required for phase separation, and mutational analysis validated its importance. Notably, this inhibitory fragment also suppresses FAK condensate formation in living mammalian cells. Together, these results establish AI-guided protein fragment discovery as a generalizable strategy to dissect and control the molecular interactions that govern biomolecular condensates. Competing Interest Statement The authors have declared no competing interest.