MOESM3 of Structural patterns of selection and diversity for Plasmodium vivax antigens DBP and AMA1

Additional file 3. Spatially-derived nucleotide diversity for PvAMA1 across multiple populations

MOESM2 of Structural patterns of selection and diversity for Plasmodium vivax antigens DBP and AMA1

Additional file 2. Domains/subdomains of PvDBP RII

MOESM5 of Structural patterns of selection and diversity for Plasmodium vivax antigens DBP and AMA1

Additional file 5. Spatially-derived nucleotide diversity for PvDBP across multiple populations

MOESM7 of Structural patterns of selection and diversity for Plasmodium vivax antigens DBP and AMA1

Additional file 7. Location of statistically significant (p < 0.05) Tajima’s D values on modelled PvAMA1 (a) and PvDBP (b) structures

MOESM11 of Structural patterns of selection and diversity for Plasmodium vivax antigens DBP and AMA1

Additional file 11. Table of highly polymorphic residues within PvAMA1

MOESM1 of Structural patterns of selection and diversity for Plasmodium vivax antigens DBP and AMA1

Additional file 1. Domains/subdomains of PvAMA1

MOESM4 of Structural patterns of selection and diversity for Plasmodium vivax antigens DBP and AMA1

Additional file 4. Spatially-derived Tajima’s D for PvAMA1 across multiple populations

Intrinsically disordered protein domains contain a biased amino acid composition.

<p>The relative proportional change in amino acid frequency within disordered regions was calculated for the entire <i>P</i>. <i>falciparum</i> proteome, with comparison made to all predicted ordered regions within the proteome.</p

Computational workflow used for analysis of the proteome of <i>Plasmodium</i> spp.

<p>Protein coding sequences were obtained from PlasmoDB, and submitted to predictors of protein disorder, MHC binding, linear B-cell epitopes and tandem repeats. Protein localisation data for <i>P</i>. <i>falciparum</i> was obtained from ApiLoc and non-synonymous single nucleotide polymorphisms (SNPs) were obtained from PlasmoDB. All data were stored in a local PostgreSQL database and queried using custom Python scripts. Statistical analysis and data visualisation were performed using the R statistical computing package.</p

The proportion of predicted linear B-cell epitopes was higher in IDPs for all <i>Plasmodium</i> spp.

<p>Classification of disorder was achieved using DISOPRED3. BepiPred was used for prediction of linear B-cell epitopes. The number of predicted linear B-cell epitopes as a percentage of all residues is shown across a range of BepiPred output thresholds. The corresponding sensitivity/specificity for each output threshold is given at <a href="http://www.cbs.dtu.dk/services/BepiPred/output.php" target="_blank">http://www.cbs.dtu.dk/services/BepiPred/output.php</a>. Thresholds range from -0.2 (sensitivity = 0.75, specificity = 0.5) to 1.3 (sensitivity = 0.13, specificity = 0.96).</p