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.
    LanguageEnglish
    JournalExpert Opinion on Drug Discovery
    Volume6
    Issue number2
    Pages171-184
    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

    Cite this

    @article{4ccf31718f6e445585e78762b4081fad,
    title = "Descriptors for antimicrobial peptides",
    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.",
    keywords = "amino-acid descriptor, antimicrobial peptides, peptide descriptors, peptide drug design, physicochemical peptide properties, quantitative structure--activity relationship",
    author = "H{\aa}vard Jenssen",
    year = "2011",
    doi = "10.1517/17460441.2011.545817",
    language = "English",
    volume = "6",
    pages = "171--184",
    journal = "Expert Opinion on Drug Discovery",
    issn = "1746-0441",
    publisher = "Taylor & Francis",
    number = "2",

    }

    Descriptors for antimicrobial peptides. / Jenssen, Håvard.

    In: Expert Opinion on Drug Discovery, Vol. 6, No. 2, 2011, p. 171-184.

    Research output: Contribution to journalReviewResearchpeer-review

    TY - JOUR

    T1 - Descriptors for antimicrobial peptides

    AU - Jenssen, Håvard

    PY - 2011

    Y1 - 2011

    N2 - 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.

    AB - 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.

    KW - amino-acid descriptor

    KW - antimicrobial peptides

    KW - peptide descriptors

    KW - peptide drug design

    KW - physicochemical peptide properties

    KW - quantitative structure--activity relationship

    U2 - 10.1517/17460441.2011.545817

    DO - 10.1517/17460441.2011.545817

    M3 - Review

    VL - 6

    SP - 171

    EP - 184

    JO - Expert Opinion on Drug Discovery

    T2 - Expert Opinion on Drug Discovery

    JF - Expert Opinion on Drug Discovery

    SN - 1746-0441

    IS - 2

    ER -