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Open Access Adaptive Peptide Design

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Computer algorithms help in the identification and optimization of peptides with desired structure and function. We provide an overview of the current focus of our research group in this field, highlighting innovative methods for peptide representation and de novo peptide generation. Our evolutionary molecular design cycle contains structure–activity relationship modeling by machine-learning methods, virtual peptide generation, activity prediction, peptide syntheses, as well as biophysical and biochemical activity determination. Such interplay between computer-assisted peptide generation and scoring with real laboratory experiments enables rapid feedback throughout the design cycle so that adaptive optimization can take place. Selected practical applications are reviewed including the design of new immunomodulatory MHC-I binding peptides and antimicrobial peptides.

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Keywords: ANTIMICROBIAL PEPTIDE; DE NOVO DESIGN; EPITOPE; MACHINE LEARNING; MEMBRANE INTERACTION; MHC-I; MOLECULAR SIMILARITY

Document Type: Research Article

Affiliations: Eidgenössische Technische Hochschule, Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, CH-8093 Zürich, Switzerland

Publication date: December 1, 2013

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  • International Journal for Chemistry and Official Membership Journal of the Swiss Chemical Society (SCS) and its Divisions

    CHIMIA, a scientific journal for chemistry in the broadest sense, is published 10 times a year and covers the interests of a wide and diverse readership. Contributions from all fields of chemistry and related areas are considered for publication in the form of Review Articles and Notes. A characteristic feature of CHIMIA are the thematic issues, each devoted to an area of great current significance.

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