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 their underlying multivalent interactions. Here, we demonstrate a generalizable AI-driven method for designing protein fragments to control condensate formation, applying this approach across G3BP1, SARS-CoV-2 nucleocapsid, TDP-43, and focal adhesion kinase (FAK). Computationally screening 2,235 fragments, we selected 18 for experimental investigation, attaining a 50% success rate. Furthermore, predicted fragment binding modes align with their activities, revealing known and novel interactions driving condensate formation. For example, a fragment which suppresses FAK condensates in mammalian cells uncovered an interdomain interaction required for phase separation. Together, our results establish AI-guided protein fragment discovery as a generalizable strategy to dissect and control the molecular interactions that govern biomolecular condensates.