Computer-aided Discovery of Peptides that Specifically Attack Bacterial Biofilms

Abstract

AbstractBiofilms 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.