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Predicting Antibacterial Peptides by the Concept of Chou's Pseudo-amino Acid Composition and Machine Learning Methods

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Microbial resistance to antibiotics is a rising concern among health care professionals, driving them to search for alternative therapies. In the past few years, antimicrobial peptides (AMPs) have attracted a lot of attention as a substitute for conventional antibiotics. Antimicrobial peptides have a broad spectrum of activity and can act as antibacterial, antifungal, antiviral and sometimes even as anticancer drugs. The antibacterial peptides have little sequence homology, despite common properties. Since there is a need to develop a computational method for predicting the antibacterial peptides, in the present study, we have applied the concept of Chou's pseudo-amino acid composition (PseAAC) and machine learning methods for their classification. Our results demonstrate that using the concept of PseAAC and applying Support Vector Machine (SVM) can provide useful information to predict antibacterial peptides.
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Keywords: Antibacterial peptides; Chou's pseudo amino acid composition; bioinformatics; clustering; fivefold cross-validation; machine learning methods

Document Type: Research Article

Publication date: February 1, 2013

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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