Integrating AI, automation and multiscale simulations for end-to-end design of phase-separating proteins

Liquid-liquid phase separation (LLPS) is a fundamental cellular process that isdriven by self-assembly of intrinsically disordered proteins (IDPs), protein-RNAcomplexes, or other bio-molecular systems which can form liquid droplets. Manynatural materials including silk, elastin, and gels are a result of LLPS and thusrational design of such phase-separating peptides can have transformative impact, from designing new biologically inspired materials (e.g., clothing) to selfcompartmentalized drug-delivery systems for biomedical applications. However,given the intrisinc complexity in the rules governing LLPS, rational design of LLPSundergoing peptides remains challenging. We posit that automation, foundationmodels integrated with reinforcement learning approaches and multiscale molecularsimulations can drive the design of novel peptides that undergo LLPS. We describeour progress towards the goal of end-to-end design of phase separating peptidesby summarizing current work at the Argonne National Laboratory’s AdvancedPhoton Source 8ID-I beamline, where a robotic set up in the laboratory is enabledvia simulation and extensive testing of such bio-materials. Together, our approachenables the design of novel bio-materials that can undergo phase separation underdiverse physiological conditions