De novo design of beta hairpin peptides with custom installed halogen-bonding groups.

Abstract

Much of the success of computational protein design relies on the huge number of experimental crystal structures available for training and testing. Such information is simply not available for designing solution-stable hairpin peptides, much less ones that incorporate non-canonical amino acids. We tackle this problem using a two-step physics-based approach. First, we use a simple statistical mechanical model, trained on a number of designed hairpin sequences known to be stable in solution, to rank a number of potential designs with desired properties. Then, we perform all-atom folding simulations of the highest-ranked sequences using large-scale distributed computing. To validate our predictions, a selection of designed sequences predicted to have sensitive halogen bond donor-acceptor pairs are synthesized and experimentally characterized using solution-state NMR spectroscopy. Our results show the utility of simple statistical mechanical models, coupled with massively parallel simulations for de novo molecular design of non-natural foldamers. Such designed sequences will help develop model systems to understand the role of halogen bonding in peptide stability.