A Grazia Di

B Bjellqvist

CA Schramm

D Eisenberg

EA Barbosa

EP Brennan

G Zhao

GD Brand

GWJ Seto

J Garnier

JC Stewart

JM Conlon

JRSa Leite

JT Jarrett

K Pärn

M Dathe

M Jain

M Torrent

M Zasloff

MHS Ramada

NB Last

PEG Thorén

RM Epand

S Popovic

ST Henriques

TN Demidova-Rice

WC Wimley

YH Chen

Scientific Reports

AbstractMature proteins can act as potential sources of encrypted bioactive peptides that, once released from their parent proteins, might interact with diverse biomolecular targets. In recent work we introduced a systematic methodology to uncover encrypted intragenic antimicrobial peptides (IAPs) within large protein sequence libraries. Given that such peptides may interact with membranes in different ways, resulting in distinct observable outcomes, it is desirable to develop a predictive methodology to categorize membrane active peptides and establish a link to their physicochemical properties. Building upon previous work, we explored the interaction of a range of IAPs with model membranes probed by differential scanning calorimetry (DSC) and circular dichroism (CD) techniques. The biophysical data were submitted to multivariate statistical methods and resulting peptide clusters were correlated to peptide structure and to their antimicrobial activity. A re-evaluation of the physicochemical properties of the peptides was conducted based on peptide cluster memberships. Our data indicate that membranolytic peptides produce characteristic thermal transition (DSC) profiles in model vesicles and that this can be used to categorize novel molecules with unknown biological activity. Incremental expansion of the model presented here might result in a unified experimental framework for the prediction of novel classes of membrane active peptides.</jats:p

Towards an experimental classification system for membrane active peptides

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