Insights into the immunological properties of intrinsically disordered malaria proteins using proteome scale predictions

Malaria remains a significant global health burden. The development of an effective malaria vaccine remains as a major challenge with the potential to significantly reduce morbidity and mortality. While Plasmodium spp. have been shown to contain a large number of intrinsically disordered proteins (IDPs) or disordered protein regions, the relationship of protein structure to subcellular localisation and adaptive immune responses remains unclear. In this study, we employed several computational prediction algorithms to identify IDPs at the proteome level of six Plasmodium spp. and to investigate the potential impact of protein disorder on adaptive immunity against P. falciparum parasites. IDPs were shown to be particularly enriched within nuclear proteins, apical proteins, exported proteins and proteins localised to the parasitophorous vacuole. Furthermore, several leading vaccine candidates, and proteins with known roles in host-cell invasion, have extensive regions of disorder. Presentation of peptides by MHC molecules plays an important role in adaptive immune responses, and we show that IDP regions are predicted to contain relatively few MHC class I and II binding peptides owing to inherent differences in amino acid composition compared to structured domains. In contrast, linear B-cell epitopes were predicted to be enriched in IDPs. Tandem repeat regions and non-synonymous single nucleotide polymorphisms were found to be strongly associated with regions of disorder. In summary, immune responses against IDPs appear to have characteristics distinct from those against structured protein domains, with increased antibody recognition of linear epitopes but some constraints for MHC presentation and issues of polymorphisms. These findings have major implications for vaccine design, and understanding immunity to malaria. ? 2015 Guy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.