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In order to understand the dimeric effects leading to membrane damage, extensive …


Biology Articles » Biochemistry » Lipid Biochemistry » Conformational study of the protegrin-1 (PG-1) dimer interaction with lipid bilayers and its effect » Methods

Methods
- Conformational study of the protegrin-1 (PG-1) dimer interaction with lipid bilayers and its effect

A unit cell containing two layers of lipids with almost 65,000 atoms is constructed. The β-sheet of the PG-1 dimer is initially located at the amphipathic region in the top leaflet of the lipid bilayer. Since our simulation method closely follows the previous method of the PG-1 monomer simulation, in this paper we only briefly describe key parameters used for the dimer simulations. The details of the simulation method are described elsewhere [22]. A lipid bilayer containing POPC (palmitoyl-oleyl-phosphatidylcholine) is constructed for the simulations. For the lipid bilayer, 160 POPCs (80 POPCs each side) constitute the lateral cell dimension of 71.3 Å × 71.3 Å. TIP3P waters were added and relaxed through a series of minimization and dynamics. Twelve counter ions (12 Cl-) were inserted to electrically neutralize the lipid bilayer system, since there are six positively charged amino acid residues in each monomer. To obtain a physiological salt concentration near 100 mM, additional 20 Na+ and 20 Cl- were added.

Our simulation employed the NPAT (constant number of atoms, pressure, surface area, and temperature) ensemble, an effective (time-averaged) surface tension, with a constant normal pressure applied in the direction perpendicular to the membrane. An alternative protocol would involve using variable surface area controlled by constant surface tension with the NPγT (constant number of atoms, pressure, surface tension, and temperature) ensemble, where γ is the applied surface tension. When γ = 0, NPγT is equivalent to NPT. Although experimentally measured macroscopic property of γ should be close to or zero for the non-stressed lipid bilayers [30], a nonzero surface tension must be employed in the NPγT simulations due to the presence of long-wavelength undulation for the microscopic membrane patch [31]. In the NPT ensemble, simulations with CHARMM27 [32] parameter sets reproduce incorrectly reduced surface area [33,34]. However a simulation with a correctly parameterized constant surface area with the NPAT ensemble can be directly comparable to an applied constant surface tension [35,36]. This has led us to use a constant surface area with the NPAT ensemble.

The CHARMM program [32] and Charmm 27 force field were used to construct the set of starting points and to relax the systems to a production-ready stage. In the pre-equilibrium stages, the initial configurations were gradually relaxed, with the peptide held rigid. A series of dynamic cycles were performed with the harmonically restrained peptides, and then the harmonic restraints were gradually diminished with the full Ewald electrostatics calculation and constant temperature (Nosé-Hoover) thermostat/barostat at 310 K. The entire pre-equilibration cycle took 5 ns to yield the starting point. For the production runs of 20 ns, any constraint applied to the peptides was removed, and the simulations were performed with the same parameter sets as used in the pre-equilibrium simulations. The system reached to the equilibration after initial 3–4 ns. The NAMD code [37] on a Biowulf cluster at the NIH was used for the starting point with the same Charmm 27 force field in the production simulations. Recent simulation studies suggest that ~10 ns duration simulations can reveal details of the interactions of lipid molecules with inner and outer membrane proteins [38,39].

Authors' contributions
HJ carried out the computational simulations, theoretical calculations, and analysis of the results and drafted the manuscript. BM and RN conceived the study, participated in its design and coordination, and helped to draft the manuscript. All authors read and approved the final manuscript.

Acknowledgements
This project has been funded in part by the US Army Medical Research Acquisition Activity under grant W81XWH-05-1-0002. This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under contract number NO1-CO-12400. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. This research was supported (in part) by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research. This study utilized the high-performance computational capabilities of the Biowulf PC/Linux cluster at the National Institutes of Health, Bethesda, MD.



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