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Letter
Peptide-membrane interaction between targeting and lysis Katharina Stutz, Alex T. Müller, Jan A. Hiss, Petra Schneider, Markus Blatter, Bernhard Pfeiffer, Gernot Posselt, Gil Kanfer, Benoit Kornmann, Paul Wrede, Karl-Heinz Altmann, Silja Wessler, and Gisbert Schneider ACS Chem. Biol., Just Accepted Manuscript • DOI: 10.1021/acschembio.7b00504 • Publication Date (Web): 01 Aug 2017 Downloaded from http://pubs.acs.org on August 3, 2017
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ACS Chemical Biology
Peptide-membrane interaction between targeting and lysis Katharina Stutza‡, Alex T. Müllera‡, Jan A. Hissa, Petra Schneidera, Markus Blattera, Bernhard Pfeiffera, Gernot Posseltb, Gil Kanfera, Benôit Kornmanna, Paul Wredec, Karl-Heinz Altmanna, Silja Wesslerb and Gisbert Schneidera,* a
Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland b Department of Molecular Biology, Division of Microbiology, Paris-Lodron University of Salzburg, 5020 Salzburg, Austria c Institute of Chemistry and Biochemistry, Freie Universität Berlin, 14195 Berlin, Germany
Supporting Information is available online ABSTRACT: Certain cationic peptides interact with bio-
logical membranes. These often-complex interactions can result in peptide targeting to the membrane, or in membrane permeation, rupture, and cell lysis. We investigated the relationship between the structural features of membrane-active peptides and these effects, to better understand these processes. To this end, we employed a computational method for morphing a membranolytic antimicrobial peptide into a non-membranolytic mitochondrial targeting peptide by “directed simulated evolution”. The results obtained demonstrate that superficially subtle sequence modifications can strongly affect the peptides’ membranolytic and membrane-targeting abilities. Spectroscopic and computational analyses suggest that N- and C-terminal structural flexibility plays a crucial role in determining the mode of peptidemembrane interaction.
Precisely controlled peptide-membrane interactions are essential for a cell’s survival, and for its proper biochemical function. Whilst targeting peptides code for the respective cellular compartment of a native protein and typically interact with the target membranes either directly or via receptor-binding without affecting their structural integrity,1-3 the class of membranolytic peptides destroy lipid membranes upon interaction.4-8 Both types of biologically-relevant effects can be achieved with small cationic peptides which adopt a helical structure in hydrophobic environments.9-11 An important class of host-defence peptides, including certain antimicrobial and anti-cancer peptides, belong to this group.12,13 Despite a rich history of research in this active field, the decisive structural features behind these effects remain unknown.
As a step towards a better understanding of the underlying structure-activity relationships of peptidemembrane interaction, we employed a directed evolutionary algorithm (MoPED, Morphing of Peptides by Evolutionary Design)14 to systematically convert (“morph”) a cationic, helical, membranolytic antimicrobial peptide (AMP) into a cationic, helical, nonmembranolytic mitochondrial transit peptide (mTP), and studied the activity and structure of the intermediate peptide sequences. As minimalist representatives of each peptide class, we introduce the shortened 14mer AMP Aurein 2.2d2 (peptide 1, a synthetic derivative of the natural AMP Aurein 2.2 from frog skin, lacking the two C-terminal residues of the natural AMP, the removal of which has no substantial effect on peptide function or structure)15 as the start sequence, and the 14mer mTP from human mitochondrial trimethyllysine dioxygenase precursor protein (peptide 9)16 as the end sequence (“attractor”) for peptide morphing. This choice was motivated by the idea to use a pair of smallest possible peptides with experimentally confirmed activities. In this present study, the sequence space contained all 1914 14-mers that could be formed from 19 amino acids (i.e., the genetically coded amino acid residues without Cys for ease of synthesis). By applying the algorithm, we obtained seven new peptides spanning the sequence space between the start and the attractor peptide. In contrast to single-residue mutation like the “alanine walk”, MoPED considers all residue positions at the same time. The software employs a physicochemical similarity metric based on the Grantham substitution matrix17,18 for generating mutations and calculating pairwise distances between the peptides in sequence space.19-22 Briefly, the algorithm iteratively generates a population of mutated sequences,
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Table 1 Activities of the peptides generated. Underlined residues indicate the sequence differences when morphing peptides 1→9. D: Distance to the mTP attractor peptide no. 9, according to the Grantham matrix. ++: vesicle (LUV) lytic activity ≥75% compared to Triton (≡100%) or bacterial growth inhibition (106 S. aureus SH1000-GFP strain) after 20 h. +: LUV lytic activity 51–74%; (+): LUV lytic activity 10–50%. Empty fields: LUV lytic activity
