bioRxivImmunology
Rational structure-guided design of a blood stage malaria vaccine immunogen presenting a single epitope from PfRH5
T. Harrison, N. Alam, B. Farrell, D. Quinkert, A. Lias, L. King, S. J. Draper, M. K. Higgins, I. Campeotto
Abstract
Despite recent success in licencing of two malaria vaccines, there continues to be an urgent need for improved vaccine immunogens as the world aims for malaria eradication. The invasion of erythrocytes by Plasmodium falciparum is an essential step in the life cycle of the parasite, preceding symptoms of disease and parasite transmission. Antibodies which target the PfRH5 protein are highly effective at preventing erythrocyte invasion and the most effective growth-inhibitory antibodies bind to a single epitope. Here we used Rosetta-based protein design to produce a focused synthetic immunogen on which this epitope is presented on a small scaffold. Structural biology and biophysics were used to demonstrate that the immunogen is correctly folded and binds neutralising monoclonal antibodies with at least nanomolar affinity. In immunised rats, the immunogen induced PfRH5-targeting antibodies that inhibit parasite growth at a thousand-fold lower concentration that those induced through immunisation with PfRH5. Finally, we show that priming with the focused immunogen and boosting with PfRH5 achieves the best balance between antibody quality and quantity and induces the most effective growth-inhibitory response. This rationally designed vaccine immunogen is now available for use as part of future malaria vaccines, alone or in combination with other immunogens. One Sentence SummaryRational, structure-guided design has been used to produce a vaccine immunogen in which the epitope for the most growth-inhibitory monoclonal antibody targeting PfRH5 has been correctly presented on a synthetic scaffold for use in a vaccine to protect from blood-stage malaria.
- organism: rat
- assay: protein structure
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
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