Polanco Carlos, Samaniego Jose L
Instituto de Fisiología Celular, Universidad Nacional Autónoma de México, Circuito Exterior s/n Ciudad Universitaria Delegación Coyoacán, México.
Acta Biochim Pol. 2009;56(1):167-76. Epub 2009 Mar 18.
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.
抗菌肽的研究主要是因其在多种与社会相关的疾病中具有潜在益处，宿主利用它们来保护自身免受不同类型病原菌的侵害。我们使用了一种名为隐马尔可夫模型（HMMs）的数学计算方法来靶向一类抗菌肽，即选择性阳离子两亲抗菌肽（SCAAPs）。HMMs实施过程中的主要差异集中在使用每个候选SCAAPs的五种主要物理化学性质来检测SCAAP，而不是使用构成肽的氨基酸的统计信息。通过这种方法检测到了一组抗菌肽，结果发现了：9种SCAAPs、6种属于天蚕素A和蛙皮素2亚区域的合成抗菌肽以及19种来自天蚕素A家族的肽。开发了一种以HMMs为核心的评分函数，该函数独特地利用了数据库中可获取的信息。
