Descriptors for antimicrobial peptides

    Research output: Contribution to journalReviewResearchpeer-review

    Abstract

    Introduction: A frightening increase in the number of isolated multidrug resistant bacterial strains linked to the decline in novel antimicrobial drugs entering the market is a great cause for concern. Cationic antimicrobial peptides (AMPs) have lately been introduced as a potential new class of antimicrobial drugs, and computational methods utilizing molecular descriptors can significantly accelerate the development of new peptide drug candidates.

    Areas covered: This paper gives a broad overview of peptide and amino-acid scale descriptors available for AMP modeling and highlights which of these are currently being used in quantitative structure--activity relationship (QSAR) studies for AMP optimization. Additionally, some key commercial computational tools are discussed, and both successful and less successful studies are referenced, illustrating some of the challenges facing AMP scientists. Through examples of different peptide QSAR studies, this review highlights some of the missing links and illuminates some of the questions that would be interesting to challenge in a more systematic fashion.

    Expert opinion: Computer-aided peptide QSAR using molecular descriptors may provide the necessary edge to peptide drug discovery, enabling successful design of a new generation anti-infective drug molecules. However, if this wonderful scenario is to play out, computational chemists and peptide microbiologists would need to start playing together and not just side by side.
    Original languageEnglish
    JournalExpert Opinion on Drug Discovery
    Volume6
    Issue number2
    Pages (from-to)171-184
    Number of pages14
    ISSN1746-0441
    DOIs
    Publication statusPublished - 2011

    Keywords

    • amino-acid descriptor
    • antimicrobial peptides
    • peptide descriptors
    • peptide drug design
    • physicochemical peptide properties
    • quantitative structure--activity relationship

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