QSAR modeling and computer-aided design of antimicrobial peptides.

QSAR modeling and computer-aided design of antimicrobial peptides. Research Abstract Details 

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QSAR modeling and computer-aided design of antimicrobial peptides. Abstract Text:

Jenssen,Christopher D Fjell,Artem Cherkasov,Robert E W Hancock,

The drastic increase in multi-drug-resistant bacteria has created an urgent need for new therapeutic interventions, including antimicrobial peptides, an interesting template for novel drug development. However, the process of optimizing peptide antimicrobial activity and specificity using large peptide libraries is both tedious and expensive. Here we confirm the use of a mathematical model for prediction, prior to synthesis, of peptide antibacterial activity toward the antibiotic resistant pathogen Pseudomonas aeruginosa. By the use of novel descriptors quantifying the contact energy between neighboring amino acids, as well as a set of inductive and conventional QSAR descriptors, we were able to model the antibacterial activity of peptides. Cross-correlation and optimization of the implemented descriptor values enabled us to build two models, using very limited sets of peptides, which were able to correctly predict the activity of 85 or 71% of the tested peptides, within a twofold deviation window of the corresponding previously assessed IC(50) values, measured earlier. Though these two models were significantly different in size, they demonstrated no significant difference in their predictive power, implying that it is possible to build powerful predictive models using even small sets of structurally different peptides, when using contact-energy descriptors and inductive and conventional QSAR descriptors in the model design. Copyright (c) 2007 European Peptide Society and John Wiley & Sons, Ltd.

QSAR modeling and computer-aided design of antimicrobial peptides. Publishing Authors By Initials

H Jenssen,CD Fjell,A Cherkasov,RE Hancock,

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QSAR modeling and computer-aided design of antimicrobial peptides. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Journal of peptide science : an official publicati

VOLUME: 14

Page Numbers: 110-4

Journal Abbreviation: J. Pept. Sci.

ISSN: 1075-2617

DAY: 24

MONTH: Jan

YEAR: 2008

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LANGUAGE: eng

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Grant and Affiliation Information for QSAR modeling and computer-aided design of antimicrobial peptides.

AFFILIATION: Centre for Microbial Diseases and Immunity Research, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.

Country: England

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MEDLINETA: J Pept Sci

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