In addition, other properties like molecular weight, content of bulky residues, content of small residues, average hydrophobicity, average charge a.s.o. and the content of selected dipeptide-groups are calculated from the sequence as well. 144 such properties are weighted individually and are used as query vector. The weights have been trained on a set of protein families with known structures, using a genetic algorithm. Sequences in the database are transformed into vectors as well, and the euclidian distance between the query and database sequences is calculated.
Distances are rank ordered, and sequences with lowest distance are reported on top.
We appreciate feedback about correct protein identification. Table 1 was derived doing this for all 1300 sequences. Compare secondary structure predictions between query and hit. I.e, not all hits are printed. The top scoring protein is the top candidate for identification of the protein.