Connecting peptide physicochemical and antimicrobial properties by a rational prediction model
| Autor: | Victòria M. Nogués, Ester Boix, Marc Torrent, David Andreu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Models
Molecular Chemical Phenomena Protein Conformation lcsh:Medicine Peptide Bioinformatics 01 natural sciences Biochemistry Computer Applications chemistry.chemical_compound Drug Discovery Peptide synthesis lcsh:Science Peptide sequence Immune Response chemistry.chemical_classification 0303 health sciences Multidisciplinary Chemistry Antimicrobial Innate Immunity Medical Microbiology Regression Analysis Computer-Aided Design Pèptids medicine.symptom Sequence Analysis Algorithms Research Article Antimicrobial peptides Immunology Context (language use) Biological Data Management Antibiòtics Computational biology Microbiology Biologia computacional 03 medical and health sciences medicine Amino Acid Sequence Biology Microbial Pathogens 030304 developmental biology Low toxicity 010405 organic chemistry lcsh:R Immunity Computational Biology Immune Defense 0104 chemical sciences Mechanism of action Antibiotic-resistant bacterial strains Computer Science lcsh:Q Neural Networks Computer Microorganismes Resistència als medicaments Medicinal Chemistry Antimicrobial Cationic Peptides |
| Zdroj: | Dipòsit Digital de Documents de la UAB Universitat Autònoma de Barcelona PLoS ONE, Vol 6, Iss 2, p e16968 (2011) PLoS ONE; Vol 6 PLoS ONE Recercat. Dipósit de la Recerca de Catalunya instname |
| Popis: | The increasing rate in antibiotic-resistant bacterial strains has become an imperative health issue. Thus, pharmaceutical industries have focussed their efforts to find new potent, non-toxic compounds to treat bacterial infections. Antimicrobial peptides (AMPs) are promising candidates in the fight against antibiotic-resistant pathogens due to their low toxicity, broad range of activity and unspecific mechanism of action. In this context, bioinformatics' strategies can inspire the design of new peptide leads with enhanced activity. Here, we describe an artificial neural network approach, based on the AMP's physicochemical characteristics, that is able not only to identify active peptides but also to assess its antimicrobial potency. The physicochemical properties considered are directly derived from the peptide sequence and comprise a complete set of parameters that accurately describe AMPs. Most interesting, the results obtained dovetail with a model for the AMP's mechanism of action that takes into account new concepts such as peptide aggregation. Moreover, this classification system displays high accuracy and is well correlated with the experimentally reported data. All together, these results suggest that the physicochemical properties of AMPs determine its action. In addition, we conclude that sequence derived parameters are enough to characterize antimicrobial peptides. M.T. is the recipient of a postdoctoral grant from Alianza Cuatro Universidades (Spain). Work supported by the European Union (HEALTH-F3-2008-223414), the Spanish Ministry of Science and Innovation (BIO2008-04487-CO3-02, BFU2009-09371) and the Generalitat de Catalunya (SGR2009-494, SG R2009-795). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. |
| Databáze: | OpenAIRE |
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