Our selection procedure for peptide antigens relied on the application of bioinformatic filters to collections of protein sequences, including conceptually translated nucleotide sequence data. The selection criteria aimed at proteins located to the hostparasite interface. Such proteins are potentially seen by the host immune system and may elicit an immune response and thus represent good candidates of diagnostic antigens. Our in silico analysis therefore prioritized proteins containing a sorting signal directing the protein to the extracellular space (PSORT II [17] ), transmembrane domains (TMHMM II [18] ), or a C-terminal signature sequence for addition of glycosyl phosphatidylinositol (GPI) anchor (GPI-som [19] ). These prediction algorithms require full length amino acid sequences with complete N-and C-termini, thus excluding the use of most EST libraries. The majority of proteins generally display well defined three dimensional structures, which is mostly globular. Our selection procedure preferred sequences in which the chemically synthesized peptide also adopts a natural conformation in vitro. There are two structural motifs meeting this claim. Firstly, the intrinsically unstructured regions (IUR) that lack a well defined three dimensional structure displaying an extended conformation that can be identified for example by IUPred [20] . Secondly the alpha-helical coiled-coil motif predicted for example by Paircoil2 [21] . Alpha-helical coiled-coils are believed to readily fold into a stable structure in aqueous solution [22] . A microarray platform served for testing the diagnostic potential of the peptides selected in silico. Using a highthroughput microarray format brings together the advantages of low reagent consumption and rapid multiplexed analysis. Particularly in diagnostics, the possibility of testing the reactivity of a given serum sample with multiple antigens simultaneously harbors great benefits. Promising candidates identified on microarray were further explored and validated for use in ELISA.