Bioinformatics Tool to Identify Peptides Associated to Cancer Cells

We present a computational-mathematical algorithm that can identify peptides that are experimentally associated with their action against cancer cells and classified in the APD2 database. The algorithm, named polarity index method, showed an accuracy of 95% in a double-blind test applied to peptides from eight different databases. The method only requires the primary peptide structure, i.e. the amino acid sequence, to determine the polarity profile. Formerly, we have used this method to identify selective antibacterial peptides with a high efficiency. Our present study suggests that this computational method can also be used as a first filter in the analysis and identification of peptides and proteins that are related to cancer cells.