The periplasmic chaperone SurA is the primary facilitator of outer membrane protein (OMP) biogenesis in E. coli , yet the molecular rules governing its client recognition remain unclear. This work builds on small-scale in vitro studies of model OMPs, by extending our analysis across the entire E. coli OMPome (60 known/predicted OMPs) to identify sequence motifs that mediate SurA binding. Using high-throughput peptide arrays, we systematically map SurA binding sites in OMPs, revealing consensus motifs, with the utilization of machine learning to refine these binding rules, enabling predictive models of SurA:OMP interactions. High-throughput in silico docking methods has been applied to this data, to refine the SurA binding site to residue-level detail, and to reveal site-specific preferences of the core and P1 domains. Critical residues in the SurA binding sites have been validated with in vitro and in vivo studies, concluding how SurA recognizes clients and illuminating how SurA chaperones OMPs through the periplasm for delivery to BAM. This study provides the first comprehensive organism-wide understanding of SurA client specificity, offering insights into OMP biogenesis and informing future strategies to target SurA:OMP binding as a novel antibacterial approach.
Herbert et al. (Sun,) studied this question.