Back

Structure-guided design of a synthetic mimic of an EPCR-binding PfEMP1 protein

Barber, N. M.; Lau, C.; Turner, L.; Watson, G.; Thrane, S.; Lusingu, J. P. A.; Lavstsen, T.; Higgins, M. K.

2019-08-28 microbiology
10.1101/749432 bioRxiv
Show abstract

Structure-guided vaccine design provides a route to elicit a focused immune response against the most functionally important regions of a pathogen surface. This can be achieved by identifying epitopes for neutralizing antibodies through structural methods and recapitulating these epitopes by grafting their core structural features onto smaller scaffolds. In this study, we have conducted a modified version of this protocol. We focused on the PfEMP1 protein family found on the surfaces of erythrocytes infected with Plasmodium falciparum. A subset of PfEMP1 proteins bind to endothelial protein C receptor (EPCR), and their expression correlates with development of the symptoms of severe malaria. Structural studies revealed the PfEMP1 to present a helix-kinked-helix motif which forms the core of the EPCR binding site. Using Rosetta-based design we successfully grafted this motif onto a three-helical bundle scaffold. We show that this synthetic binder interacts with EPCR with nanomolar affinity and adopts the expected structure. We also assessed its ability to bind to antibodies found in immunized animals and in humans from malaria endemic regions. Finally, we tested its capacity to effectively elicit antibodies that prevent EPCR binding and analysed the degree of cross-reactivity of these antibodies across a diverse repertoire of EPCR-binding PfEMP1. This provides a case study of immunogen design, assessing the effect of designing a focused immunogen that contains the core features of a ligand binding site, rather than those of a neutralizing antibody epitope.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.