Toxoplasma gondii invade host cells using a multi-step process that depends on the regulated secretion of adhesions. To identify key primary sequence features of adhesins in this parasite, we analyze the relative frequency of individual amino acids, their dipeptide frequencies, and the polarity, polarizability and Van der Waals volume of the individual amino acids by using cluster analysis. This method identified cysteine as a key amino acid in the Toxoplasma adhesin group. The best vector algorithm of non-concatenated features was for 2 attributes: the single amino acid relative frequency and the dipeptide frequency. Polarity, polarizability and Van der Waals volume were not good classificatory attributes. Single amino acid attributes clustered unambiguously 67 apicomplexan hypothetical adhesins. This algorithm was also useful for clustering hypothetical Toxoplasma target host receptors. All of the cluster performances had over 70% sensitivity and 80% specificity. Compositional aminoacid data can be useful for improving machine learning-based prediction software when homology and structural data are not sufficient.