Letters pubs.acs.org/acschemicalbiology
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,⊥ Benoît Kornmann,⊥ Paul Wrede,§ Karl-Heinz Altmann,† Silja Wessler,‡ and Gisbert Schneider*,† †
Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH), Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland ‡ Department of Molecular Biology, Division of Microbiology, Paris-Lodron University of Salzburg, 5020 Salzburg, Austria § Institute of Chemistry and Biochemistry, Freie Universität Berlin, 14195 Berlin, Germany ⊥ Institute of Biochemistry, Swiss Federal Institute of Technology (ETH), Otto-Stern-Weg-3, 8093 Zurich, Switzerland S Supporting Information *
ABSTRACT: Certain cationic peptides interact with biological 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 membraneactive 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 nonmembranolytic 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 peptide−membrane interaction.
P
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 and uses the distance of the mutants to the attractor sequence
recisely controlled peptide-membrane interactions are essential for a cell’s survival, and for its proper biochemical function. While targeting peptides code for the respective cellular compartment of a native protein and typically interact with the target membranes either directly or via receptorbinding without affecting their structural integrity,1−3 the class of membranolytic peptides destroys 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-defense peptides, including certain antimicrobial and anticancer 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 toward a better understanding of the underlying structure−activity relationships of peptide−membrane 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 © XXXX American Chemical Society
Received: June 18, 2017 Accepted: August 1, 2017 Published: August 1, 2017 A
DOI: 10.1021/acschembio.7b00504 ACS Chem. Biol. XXXX, XXX, XXX−XXX
Letters
ACS Chemical Biology Table 1. Activities of the Peptides Generateda vesicle (LUV) typeb ID
peptide sequence
1 2 3 4 5 6 7 8 9
GLFDIVKKVVGALG QQKTRPWHEFVPQS TYTPFEHWQSHLQN WYERYSHPTSRLQD WYFRFSHVESRLQD WYHRQSHVHSRLQD WYHRFSHFHSRLQD WYHRLSHIHSRLQD WYHRLSHLHSRLQD
D 388 346 204 121 114 117 31 5 0
POPC
POPCECL5
POPCECL20
POPEG
POPGCL40
S.aureus inhibition
++
++
++
++
++
++
+ + +
+
+ (+)
mito. colocalization (Pearson’s r) −0.1 0.0 0.2 0.5 0.8 −0.1 0.6 0.7 0.8
± ± ± ± ± ± ± ± ±
0.1 0.0 0.1 0.2 0.0 0.2 0.4 0.1 0.1
fold helix induction 3.1 1.2 2.1 1.0 2.1 1.5 2.5 2.3 2.5
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 SH1000GFP strain) after 20 h. +: LUV lytic activity 51−74%. (+): LUV lytic activity 10−50%. Empty fields: LUV lytic activity
