Computer-aided Discovery of Peptides that Specifically Attack Bacterial Biofilms View Full Text


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Article Info

DATE

2018-12

AUTHORS

Evan F. Haney, Yoan Brito-Sánchez, Michael J. Trimble, Sarah C. Mansour, Artem Cherkasov, Robert E. W. Hancock

ABSTRACT

Biofilms represent a multicellular growth state of bacteria that are intrinsically resistant to conventional antibiotics. It was recently shown that a synthetic immunomodulatory cationic peptide, 1018 (VRLIVAVRIWRR-NH2), exhibits broad-spectrum antibiofilm activity but the sequence determinants of antibiofilm peptides have not been systematically studied. In the present work, a peptide library consisting of 96 single amino acid substituted variants of 1018 was SPOT-synthesized on cellulose arrays and evaluated against methicillin resistant Staphylococcus aureus (MRSA) biofilms. This dataset was used to establish quantitative structure-activity relationship (QSAR) models relating the antibiofilm activity of these peptides to hundreds of molecular descriptors derived from their sequences. The developed 3D QSAR models then predicted the probability that a peptide would possess antibiofilm activity from a library of 100,000 virtual peptide sequences in silico. A subset of these variants were SPOT-synthesized and their activity assessed, revealing that the QSAR models resulted in ~85% prediction accuracy. Notably, peptide 3002 (ILVRWIRWRIQW-NH2) was identified that exhibited an 8-fold increased antibiofilm potency in vitro compared to 1018 and proved effective in vivo, significantly reducing abscess size in a chronic MRSA mouse infection model. This study demonstrates that QSAR modeling can successfully be used to identify antibiofilm specific peptides with therapeutic potential. More... »

PAGES

1871

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-19669-4

DOI

http://dx.doi.org/10.1038/s41598-018-19669-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100625205

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29382854


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