This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Motivation: Understanding the mechanisms through which molecules penetrate cell membranes is important for gaining insight into many biological processes such as drug delivery and toxicity. While permeation through lipid bilayers, which model cell membranes, has been studied extensively, the underlying thermodynamic contributions to penetration are not well understood1,2. The goal of this project is to gain detailed knowledge of the biophysics between a permeant and lipid bilayer using advanced molecular modeling methods and high performance computing. Proposed Reasearch: Polycyclic Aromatic Hydrocarbons (PAH) are widespread in the environment and are well known to be toxic and carcinogenic but mechanisms for their toxicity are not clear3,4. It is known that PAH interact with biological membranes, and it is suspected that resulting changes to the structure and functioning of the membrane impair cell growth and activity3. In this project we will study the transport of a series of PAH containing from six to twenty carbon atoms, in lipid bilayers to gain using molecular dynamic (MD) approaches. The main objective of the study is to gain insights into effects of size and shape on permeation of a lipid bilayer in a systematic way that has not been adequately shown in literature. Free Energy Methods: A number of methods for calculating free energies from MD simulations have been developed and used in literature. Some of the most used of these methods include free energy perturbation (FEP), thermodynamic integration (TI), umbrella sampling with the weighted histogram analysis method (US-WHAM), and constraint force (CF)5. Due to the inhomogeneity of the membrane, the sampling will be carried out along a reaction coordinate parallel to the bilayer normal6. Both CF and US-WHAM methods have been successfully used in literature for determining a potential of mean force (PMF) through a bilayer. One of the major differences between these methods is that US-WHAM counts the density of states in a particular phase space window whereas CMF averages forces on a particle from the surrounding system. Force averaging generally provides better convergence density of states therefore I will use the CF method to find the PMF6. CF also more easily facilitates parallel simulations. Together with the CF method, the TI approach will be used to calculate the free energy differences between a few discrete points in the bilayer as well as at various temperatures. The results from the TI method will be then used to validate the CF simulations and determine components of the free energy7. High-performance Computing: This project is ideal for utilizing high-performance computational resources. Each of these simulations would take months on a single processor but, when performed in parallel, the wall clock time could be shortened to weeks. In addition to the computational time required for the simulations, large amounts of data are needed to calculate a meaningful PMF. For a system composed of hexane permeating a dioleoyl phosphatidylcholine model membrane, a total of 500ns of simulation time was required for generating the PMF8. On a single processor that simulation could take over a year to complete and with a two femtosecond time step, would require storage of data for up to twenty-five million time steps. These requirement are for generating the PMF of one molecule only. Timeline CF simulations: Months 1-4 calculating PMF: Months 4-6 TI simulations: Months 6-8 TI at various temperatures: Months 9-12 References: 1) Ke P.C., Qiao R. J. Phys. Condensed Matter. 19: (2007) 2) Ginzberg V.V., Balijepalli S. Nano Letters. 0 (0): (2007);3) Plant A.L., Knapp R.D., Smith L.C. J. Biol. Chem. 262 (6): 2514 (1986);4) Bemporad D., Luttmann C., Essex J.W. Biophys. J. 87: 1 (2004);5) Ghoufi A., Malfreyt P. Molec. Phys. 104 (22-24): 3787 (2006);6) Trzesniak D., Kunz A.E., van Gunsteren W.F. ChemPhysChem. 8: 162 (2007);7) Peter C., Oostenbrink C., van Dorp A., van Gunsteren W.F. J. Chem. Phys. 120 (6): 2653 (2004);8) MacCallum J.L., Tieleman D.P. J. Am. Chem. Soc. 128: 125 (2005)
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