 Antimicrobial Peptides Design by Evolutionary Multiobjective OptimizationGiuseppe Maccari, Mariagrazia Di Luca, Riccardo Nifosí, Francesco Cardarelli, Giovanni Signore, Claudia Boccardi and Angelo Bifone PLOS Computational Biology, 2013, vol. 9, issue 9, 1-12 Abstract: Antimicrobial peptides (AMPs) are an abundant and wide class of molecules produced by many tissues and cell types in a variety of mammals, plant and animal species. Linear alpha-helical antimicrobial peptides are among the most widespread membrane-disruptive AMPs in nature, representing a particularly successful structural arrangement in innate defense. Recently, AMPs have received increasing attention as potential therapeutic agents, owing to their broad activity spectrum and their reduced tendency to induce resistance. The introduction of non-natural amino acids will be a key requisite in order to contrast host resistance and increase compound's life. In this work, the possibility to design novel AMP sequences with non-natural amino acids was achieved through a flexible computational approach, based on chemophysical profiles of peptide sequences. Quantitative structure-activity relationship (QSAR) descriptors were employed to code each peptide and train two statistical models in order to account for structural and functional properties of alpha-helical amphipathic AMPs. These models were then used as fitness functions for a multi-objective evolutional algorithm, together with a set of constraints for the design of a series of candidate AMPs. Two ab-initio natural peptides were synthesized and experimentally validated for antimicrobial activity, together with a series of control peptides. Furthermore, a well-known Cecropin-Mellitin alpha helical antimicrobial hybrid (CM18) was optimized by shortening its amino acid sequence while maintaining its activity and a peptide with non-natural amino acids was designed and tested, demonstrating the higher activity achievable with artificial residues.Author Summary: In recent years, the increasing and rapid spread of pathogenic microorganisms resistant to conventional antibiotics especially in hospital settings spurred research for the identification of novel molecules endowed with antimicrobial activities and new mechanisms of action. Antimicrobial peptides (AMPs) received an increasing attention as potential therapeutic agents because of their wide spectrum of activity and low rate in inducing bacterial resistance. Currently, research is focused on the design and optimization of novel AMPs to improve their antimicrobial activity, minimize the cytotoxicity and reduce the proteolytic degradation, also in biological fluids. To this end, the introduction of non-natural amino acids will be a key requisite in order to contrast host resistance and increase compound's life. However, the amino acidic alphabet extension to non-natural elements makes a systematic approach to AMPs design unfeasible. A rational in-silico approach can drastically reduce the number of testing compounds and consequently the production costs and the time required for evaluation of activity and toxicity. In this article, AMP in-silico design with non-natural amino acids was performed and a series of candidates were tested in order to demonstrate the potentiality of this approach. Date: 2013 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1003212 DOI: 10.1371/journal.pcbi.1003212 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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