NDLI: Computational prediction of anti HIV-1 peptides and in vitro evaluation of anti HIV-1 activity of HIV-1 P24-derived peptides.

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Computational prediction of anti HIV-1 peptides and in vitro evaluation of anti HIV-1 activity of HIV-1 P24-derived peptides.

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Computational prediction of anti HIV-1 peptides and in vitro evaluation of anti HIV-1 activity of HIV-1 P24-derived peptides.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Poorinmohammad, Naghmeh Mohabatkar, Hassan Behbahani, Mandana Biria, Davood
Description	Country affiliation: Iran Author Affiliation: Poorinmohammad N ( Department of Biotechnology, Faculty of Advanced Sciences and Technologies, University of Isfahan, Isfahan, Iran.)
Abstract	The world is entering the third decade of the acquired immunodeficiency syndrome (AIDS) pandemic. The primary cause of the disease has known to be human immunodeficiency virus type I (HIV-1). Recently, peptides are shown to have high potency as drugs in the treatment of AIDS. Therefore, in the present study, we have developed a method to predict anti-HIV-1 peptides using support vector machine (SVM) as a powerful machine learning algorithm. Peptide descriptors were represented based on the concept of Chou's pseudo-amino acid composition (PseAAC). HIV-1 P24-derived peptides were examined to predict anti-HIV-1 activity among them. The efficacy of the prediction was then validated in vitro. The mutagenic effect of validated anti-HIV-1 peptides was further investigated by the Ames test. Computational classification using SVM showed the accuracy and sensitivity of 96.76% and 98.1%, respectively. Based on SVM classification algorithm, 3 out of 22 P24-derived peptides were predicted to be anti-HIV-1, while the rest were estimated to be inactive. HIV-1 replication was inhibited by the three predicted anti-HIV-1 peptides as revealed in vitro, while the results of the same test on two of non-anti-HIV-1 peptides showed complete inactivity. The three anti-HIV-1 peptides were shown to be not mutagenic because of the Ames test results. These data suggest that the proposed computational method is highly efficient for predicting the anti-HIV-1 activity of any unknown peptide having only its amino acid sequence. Moreover, further experimental studies can be performed on the mentioned peptides, which may lead to new anti-HIV-1 peptide therapeutics candidates.
File Format	HTM / HTML
ISSN	10752617
Issue Number	1
Volume Number	21
e-ISSN	10991387
Journal	Journal of Peptide Science
Language	English
Publisher	Wiley
Publisher Date	2015-01-01
Publisher Place	Great Britain (UK)
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Biochemistry Anti-hiv Agents Chemistry Hiv Core Protein P24 Hiv-1 Drug Effects Peptide Fragments Amino Acid Sequence Pharmacology Cells, Cultured Computational Biology Computer Simulation Humans Molecular Sequence Data Mutagens Salmonella Typhimurium Genetics Journal Article Research Support, Non-u.s. Gov't
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Structural Biology Medicine Drug Discovery Biochemistry Molecular Biology Pharmacology Molecular Medicine

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