Computational design of self-assembling peptide chassis materials for synthetic cells

Abstract

Giant lipid vesicles have been used extensively as a synthetic cell model to recapitulate various life-like processes, including in vitro protein synthesis, DNA replication, and cytoskeleton organization. Cell-sized lipid vesicles are mechanically fragile in nature and prone to rupture due to osmotic stress, which limits their usability. Recently, peptide vesicles have been introduced as an alternative chassis material for synthetic cells that are more robust and stable than lipid vesicles, and can withstand harsh conditions including pH, thermal, and osmotic variations. In this work, we combine coarse-grained molecular simulation, enhanced sampling free energy calculations, Gaussian process regression, and Bayesian optimization to construct an active learning screening for diblock amphiphilic elastin-like polypeptides capable of forming thermodynamically stable vesicular structures suitable for the self-assembly of synthetic peptide vesicles. Our computational screen identifies a number of promising sequences that form peptidic vesicles with high thermodynamic stabilities relative to isolated peptides in bulk solvent on the order of 10–15k_BT per amino acid residue.