title: Identification of antimicrobial peptides by using eigenvectors.
abstract: Antibacterial peptides are subject to broad research due to their potential application and the benefit they can provide for a wide range of diseases. In this work, a mathematical-computational method, called the Polarity Vector Method, is introduced that has a high discriminative level (>70%) to identify peptides associated with Gram (-) bacteria, Gram (+) bacteria, cancer cells, fungi, insects, mammalian cells, parasites, and viruses, taken from the Antimicrobial Peptides Database. This supervised method uses only eigenvectors from the incident polar matrix of the group studied. It was verified with a comparative study with another extensively verified method developed previously by our team, the Polarity Index Method. The number of positive hits of both methods was up to 98% in all the tests conducted.
authors.affiliation: Department of Mathematics, Faculty of Sciences, Universidad Nacional Autónoma de México, México.
authors.forename: Carlos
authors.initials: C
authors.lastname: Polanco
mesh_terms: D023181:Antimicrobial Cationic Peptides / Q000737:chemistry*; D001426:Bacterial Proteins / Q000737:chemistry*; D019295:Computational Biology; D057927:Hydrophobic and Hydrophilic Interactions; D020539:Sequence Analysis, Protein
chemical_list: D023181:Antimicrobial Cationic Peptides; D001426:Bacterial Proteins