doi:10.1016/j.bbamem.2006.03.010 
Copyright © 2006 Elsevier B.V. All rights reserved.
Driving engineering of novel antimicrobial peptides from simulations of peptide–micelle interactions
aDepartment of Chemical Engineering and Materials Science, University of Minnesota, 421, Washington Avenue SE, Minneapolis, MN 55455, USA
bThe Digital Technology Center, University of Minnesota, 421, Washington Avenue SE, Minneapolis, MN 55455, USA
Received 2 December 2005;
revised 6 March 2006;
accepted 8 March 2006.
Available online 15 May 2006.
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Abstract
Simulations of antimicrobial peptides in membrane mimics can provide the high resolution, atomistic picture that is necessary to decipher which sequence and structure components are responsible for activity and toxicity. With such detailed insight, engineering new sequences that are active but non-toxic can, in principle, be rationalized. Armed with supercomputers and accurate force fields for biomolecular interactions, we can now investigate phenomena that span hundreds of nanoseconds. Although the phenomena involved in antimicrobial activity, (i.e., diffusion of peptides, interaction with lipid layers, secondary structure attainment, possible surface aggregation, possible formation of pores, and destruction of the lipid layer integrity) collectively span time scales still prohibitively long for classical mechanics simulations, it is now feasible to investigate the initial approach of single peptides and their interaction with membrane mimics. In this article, we discuss the promise and the challenges of widely used models and detail our recent work on peptide–micelle simulations as an attractive alternative to peptide–bilayer simulations. We detail our results with two large structural classes of peptides, helical and beta-sheet and demonstrate how simulations can assist in engineering of novel antimicrobials with therapeutic potential.
Keywords: Antimicrobial peptide; Peptide–micelle simulation; Molecular dynamics; Mechanism of action
Fig. 1. Three structural classes of AMPs that we have investigated. (A) The helical peptide OVIS and its analogues. (B) Protegrin-1, a β-hairpin peptide and (C) Indolicidin, unstructured (the relative dimensions of the peptides are not to scale).
Fig. 2. Schematic representation of the simulation of an amphipathic helix in bilayers and micelles. (A) The final conformations A1 and A2 in the bilayer simulations are independent, and depend on the initial conformation of the peptide. (B) Because of the spherical symmetry of the micelle, conformations B1 and B2 are equivalent. Thus, the initial conformation of the peptide is unimportant, precluding the need to start run multiple simulations from many different starting conformations.
Fig. 3. Initial (left) and the final (right) conformations of the OVIS in DPC. A and B are side views, while C and D are top views. Snapshots were taken at the t = 0 and t = 39 ns. T7 and G10 also have similar final conformations. The same binding orientations are obtained for each of the three peptides when the simulations are started with the peptide placed in the aqueous phase.
Fig. 4. (a) Time profile of the distance between the center of mass of the peptide and the micelle. (b) Time profile of the angle between the peptide helical axis and the vector from the micelle center of mass to the peptide center of mass. The data for both plots are from the OVIS in DPC CONF2 simulations.
Fig. 5. The electron densities of the three peptides relative to the average electron densities of the phosphates in DPC. The abscissa is the distance from the center of mass of the micelle. The plot reflects the relative binding depths of the three peptides in DPC. These data are from the CONF2 simulations.
Fig. 6. The number of clusters of peptide dihedral angles. Each data point at time t = tini corresponds to the number of clusters of peptide dihedral angles calculated by a clustering of the time series of dihedral angles. The time series is obtained from a trajectory window from time t = tini till the end of the simulation. Each cluster of peptide dihedral angles represents a unique peptide conformation.
Fig. 7. Initial and final secondary structures of the peptides in DPC micelles in the CONF2 simulations.
Fig. 8. Radial distribution functions drawn between the positively charged amino acid side chains and the phosphate groups (in DPC) and the sulfate groups (in SDS). The functions were normalized by an arbitrary density of 0.01 and by the number of atoms in the first selection.
Fig. 9. Initial (A) and final (B) conformation of protegrins-1 in an SDS micelle.
Fig. 10. Representation of the location of the peptides in SDS micelles. Residues colored red are less than 17 Å from the center of the micelle; those more than 17 Å from the center but in the micelle core colored orange; residues in the head group region, 19–22 Å from the micelle center, colored yellow; and residues between 22 and 25 Å are colored green. Residues in the bulk water region are colored blue.
Fig. 11. Representation of the location of the peptides in DPC micelles. Residues colored red are less than 16 Å from the center of the micelle; those more than 16 Å from the center but in the micelle core colored orange; residues in the phosphate head group region; 18–21 Å from the micelle center colored yellow; and residues in the choline head group region, between 21 and 24 Å, are colored green. Residues in the bulk water region are colored blue.
Table 1.
Activity and toxicity of the protegrins discussed in the current paper
Hemolysis is given by the percent of red blood cells killed at 80 μg/mL of the peptide. Cytotoxicity is measured as the concentration of peptide required to kill 50% of the human epithelial cells.
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