title: Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test.
abstract: Cancer is an important reason of death worldwide. Traditional cytotoxic therapies, such as radiation and chemotherapy, are expensive and cause severe side effects. Currently, design of anticancer peptides is a more effective way for cancer treatment. So there is a need to develop a computational method for predicting the anticancer peptides. In the present study, two methods have been developed to predict these peptides using support vector machine (SVM) as a powerful machine learning algorithm. Classifiers have been applied based on the concept of Chou's pseudo-amino acid composition (PseAAC) and local alignment kernel. Since a number of HIV-1 proteins have cytotoxic effect, therefore we predicted the anticancer effect of HIV-1 p24 protein with these methods. After the prediction, mutagenicity of 2 anticancer peptides and 2 non-anticancer peptides was investigated by Ames test. Our results show that, the accuracy and the specificity of local alignment kernel based method are 89.7% and 92.68%, respectively. The accuracy and specificity of PseAAC-based method are 83.82% and 85.36%, respectively. By computational analysis, out of 22 peptides of p24 protein, 4 peptides are anticancer and 18 are non-anticancer. In the Ames test results, it is clear that anticancer peptides (ARP788.8 and ARP788.21) are not mutagenic. Therefore the results demonstrate that the described computation methods are useful to identify potential anticancer peptides, which are worthy of further experimental validation and 2 peptides (ARP788.8 and ARP788.21) of HIV-1 p24 protein can be used as new anticancer candidates without mutagenicity.
authors.affiliation: Department of Biotechnology, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan, Iran.
authors.forename: Zohre
authors.initials: Z
authors.lastname: Hajisharifi
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Iran.
authors.forename: Moien
authors.initials: M
authors.lastname: Piryaiee
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Iran.
authors.forename: Majid
authors.initials: M
authors.lastname: Mohammad Beigi
authors.affiliation: Department of Biotechnology, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan, Iran.
authors.forename: Mandana
authors.initials: M
authors.lastname: Behbahani
authors.affiliation: Department of Biotechnology, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan, Iran. Electronic address: h.mohabatkar@ast.ui.ac.ir.
authors.forename: Hassan
authors.initials: H
authors.lastname: Mohabatkar
mesh_terms: D000596:Amino Acids / Q000737:chemistry; D000970:Antineoplastic Agents / Q000737:chemistry / Q000494:pharmacology* / Q000633:toxicity; D019295:Computational Biology / Q000379:methods; D015195:Drug Design; D004354:Drug Screening Assays, Antitumor / Q000379:methods*; D016655:HIV Core Protein p24 / Q000737:chemistry / Q000494:pharmacology* / Q000633:toxicity; D006801:Humans; D009152:Mutagenicity Tests / Q000379:methods*; D010446:Peptide Fragments / Q000737:chemistry / Q000494:pharmacology / Q000633:toxicity; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D000970:Antineoplastic Agents; D016655:HIV Core Protein p24; D010446:Peptide Fragments
keywords: HIV-1 p24 protein; Machine learning methods; SVM