Semnan University of Medical Sciences

Research and Technology Vice Chancellor

Madanchi, H. and Akbari, S. and Shabani, A.A. and Sardari, S. and Farmahini Farahani, Y. and Ghavami, G. and Ebrahimi Kiasari, R. (2019) Alignment-based design and synthesis of new antimicrobial Aurein-derived peptides with improved activity against Gram-negative bacteria and evaluation of their toxicity on human cells. Drug Development Research, 80. pp. 162-170. ISSN 02724391

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    Abstract

    Considering the worldwide increasing prevalence of resistance to traditional antibiotics, it is necessary to find new antibiotics to deal with this issue. Recently, antimicrobial peptides (AMPs) have been proposed as new antimicrobial agents. Aureins are a family of AMPs that are isolated from Green and Golden Bell Frogs. These peptides have a favorable antibacterial activity against Gram-positive bacteria. We designed two peptides derived from natural Aurein enjoying alignment-based design method. After synthesis of the peptides, their secondary structure was checked by circular dichroism. Consequently, the antibacterial effects of these peptides were investigated by determining the minimum inhibitory concentration (MIC) and bactericidal concentration. Eventually, the toxicity of these peptides was determined by MTT (3-[4, 5-dimethylthiazol-2-yl]-2, 5-diphenyltetrazolium bromide) assay on normal human skin cells (Hu02 cell line). Natural Aurein1.2 was used as a natural control to compare the properties in all stages. The results indicated that these new peptides had medium-upward antimicrobial activity against Escherichia coli, Pseudomonas aeruginosa, and Bacillus subtilis (MIC of 8–64 μg/mL) and weak bactericidal activity against Staphylococcus aureus (MIC of 128–256 μg/mL). Also, MTT assays results showed that AureinN2 is less toxic than AureinN1 and Aurein1.2. Toxicity of AureinN2 for Hu02 cell lines was between 20 and 40% at the concentration of 8–500 μg/mL. In this study, we were able to improve antimicrobial activity of two synthetic derivatives of the Aurein family against Gram-negative bacteria by using machine-learning algorithm and other in silico methods. © 2018 Wiley Periodicals, Inc.

    Item Type: Article
    Subjects: R Medicine > R Medicine (General)
    Divisions: Faculty of Medical Sciences > School of Medicine
    Depositing User: Admin User
    Date Deposited: 27 Mar 2019 00:12
    Last Modified: 27 Mar 2019 00:12
    URI: http://eprints.semums.ac.ir/id/eprint/1561

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