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

07 Oct 2022, 18:56 (modified: 22 Nov 2022, 03:00)AI4Mat 2022 PosterReaders: Everyone
Abstract: Liquid-liquid phase separation (LLPS) is a fundamental cellular process that is driven by self-assembly of intrinsically disordered proteins (IDPs), protein-RNA complexes, or other bio-molecular systems which can form liquid droplets. Many natural materials including silk, elastin, and gels are a result of LLPS and thus rational 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 LLPS undergoing peptides remains challenging. We posit that automation, foundation models integrated with reinforcement learning approaches and multiscale molecular simulations can drive the design of novel peptides that undergo LLPS. We describe our progress towards the goal of end-to-end design of phase separating peptides by summarizing current work at the Argonne National Laboratory’s Advanced Photon Source 8ID-I beamline, where a robotic set up in the laboratory is enabled via simulation and extensive testing of such bio-materials. Together, our approach enables the design of novel bio-materials that can undergo phase separation under diverse physiological conditions
Paper Track: Papers
Submission Category: AI-Guided Design
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