Detection of selective cationic amphipatic antibacterial peptides by Hidden Markov models
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...
| Autores: | , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2009 |
| País: | México |
| Recursos: | Universidad Nacional Autónoma de México |
| Repositório: | Sistema de Información de la Facultad de Ciencias, UNAM |
| OAI Identifier: | oai:repositorio.fciencias.unam.mx:11154/3152 |
| Acesso em linha: | http://hdl.handle.net/11154/3152 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Biochemistry & Molecular Biology antibacterial peptides Hidden Markov models |
| Resumo: | 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. |
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