Machine learning framework to extract physicochemical features of B-cell epitopes recognized by a cross-reactive antibody.

During infection with Plasmodium falciparum in pregnancy, parasites express a unique virulence factor, VAR2CSA, that mediates binding of infected red blood cells to the placenta. A major goal in designing vaccines to protect pregnant women from malaria is to elicit antibodies to VAR2CSA. The challenge is that VAR2CSA is highly polymorphic and identifying conserved epitopes is essential to elicit strain-transcending immunity. Unexpectedly, a mouse monoclonal antibody, 3D10, raised against region II of the unrelated Duffy binding protein from P. vivax (DBPII) cross-reacts with diverse alleles of VAR2CSA in vitro, suggesting that epitopes may be shared across this family of 'Duffy binding-like' (DBL) proteins. Peptide arrays spanning four DBL proteins from two Plasmodium spp, including two alleles of VAR2CSA, DBPII, and PvEBP2 (as a negative control), were screened with 3D10 but the data were too complex to manually identify common epitope sequences. As such, we designed a machine learning framework to analyse the array data. We applied decision trees to extract features correlated to 3D10 binding and evaluated the model on an independent dataset for a rodent Plasmodium DBL protein (PcDBP). Next, we analysed patterns of the features predicted by the model to be strongly associated with 3D10 binding and designed mutant peptides to test complex sequence motifs. Features associated with 3D10 reactivity were mapped onto predicted 3D structures of Plasmodium proteins and validated based on 3D10 reactivity to the recombinant antigens. While the array data identified certain linear epitopes, the framework predicted other epitopes to be conformational. This was demonstrated with PcDBP; as predicted by the model, no linear peptides reacted strongly with 3D10, yet the folded protein was recognized by the antibody in a conformation-dependent manner. With this approach, peptide array data can be mined to extract physicochemical properties of epitopes recognized by cross-reactive antibodies.