title: Detection of selective cationic amphipatic antibacterial peptides by Hidden Markov models.
abstract: Antibacterial peptides are researched mainly for the potential benefit they have in a variety of socially relevant diseases, used by the host to protect itself from different types of pathogenic bacteria. We used the mathematical-computational method known as Hidden Markov models (HMMs) in targeting a subset of antibacterial peptides named Selective Cationic Amphipatic Antibacterial Peptides (SCAAPs). The main difference in the implementation of HMMs was focused on the detection of SCAAP using principally five physical-chemical properties for each candidate SCAAPs, instead of using the statistical information about the amino acids which form a peptide. By this method a cluster of antibacterial peptides was detected and as a result the following were found: 9 SCAAPs, 6 synthetic antibacterial peptides that belong to a subregion of Cecropin A and Magainin 2, and 19 peptides from the Cecropin A family. A scoring function was developed using HMMs as its core, uniquely employing information accessible from the databases.
authors.affiliation: Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Circuito Exterior s/n Ciudad Universitaria Delegación Coyoacán, México. polanco@unam.mx
authors.forename: Carlos
authors.initials: C
authors.lastname: Polanco
authors.forename: Jose L
authors.initials: JL
authors.lastname: Samaniego
mesh_terms: D000595:Amino Acid Sequence; D000900:Anti-Bacterial Agents / Q000032:analysis* / Q000737:chemistry; D002412:Cations; D016000:Cluster Analysis; D008390:Markov Chains; D008969:Molecular Sequence Data; D010455:Peptides / Q000032:analysis* / Q000737:chemistry
chemical_list: D000900:Anti-Bacterial Agents; D002412:Cations; D010455:Peptides