Antimicrobial peptides, also called body defense peptides, are used against a wide range of pathogens, such as negative- and positive-gram bacteria, mycobacteria, fungi, viruses, etc. Contrary to antibiotics, antimicrobial peptides do not develop resistance. Their wide antimicrobial spectrum situates them as important and attractive targets in research and pharmaceutical industry in order to obtain new structures using modern drug design techniques. We present here eleven QSAR models in which antimicrobial activity expressed as minimal inhibitory concentration values at Bacillus subtilis of 37 mastoparan analogs was correlated with different physicochemical parameters like: number of hydrophobic centers, molecular area and volume, internal dipole moment, refractivity, RPCG (relative positive charges) and number of donor and acceptor atoms generating by use of the computational software Sybyl. Significant R 2 (0.68-0.72) correlation coefficients and standard error of prediction SEE (0.199-0.230) were obtained, indicating that the established equations can be used. Thus, these linear models allowed us to create a library of 19 derivatives of mastoparan analogs obtained through computational mutagenesis. We propose this library of compounds as a source of possible derivatives with a more potent antimicrobial activity.

Evaluation of Antimicrobial Activity of New Mastoparan Derivatives Using QSAR and Computational Mutagenesis

Radu, Beatrice Mihaela;
2011-01-01

Abstract

Antimicrobial peptides, also called body defense peptides, are used against a wide range of pathogens, such as negative- and positive-gram bacteria, mycobacteria, fungi, viruses, etc. Contrary to antibiotics, antimicrobial peptides do not develop resistance. Their wide antimicrobial spectrum situates them as important and attractive targets in research and pharmaceutical industry in order to obtain new structures using modern drug design techniques. We present here eleven QSAR models in which antimicrobial activity expressed as minimal inhibitory concentration values at Bacillus subtilis of 37 mastoparan analogs was correlated with different physicochemical parameters like: number of hydrophobic centers, molecular area and volume, internal dipole moment, refractivity, RPCG (relative positive charges) and number of donor and acceptor atoms generating by use of the computational software Sybyl. Significant R 2 (0.68-0.72) correlation coefficients and standard error of prediction SEE (0.199-0.230) were obtained, indicating that the established equations can be used. Thus, these linear models allowed us to create a library of 19 derivatives of mastoparan analogs obtained through computational mutagenesis. We propose this library of compounds as a source of possible derivatives with a more potent antimicrobial activity.
Antimicrobial peptides, Computational mutagenesis, QSAR
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/936376
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