MD SIMULATIONS OF DESIGNED HIV INHIBITORS

这个子项目是众多研究子项目之一

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. The HIV-1 protease is a basket-shaped viral enzyme that participates in the maturation of the virus. Inhibition of this enzyme reduces the viral load in infected individuals. While several drugs are currently available that inhibit the function of the HIV protease, the need to design and develop new, novel drugs is imperative as the protease continues to rapidly mutate into drug-resistant forms. In the early 1990s Friedman and co-workers published two reports describing fullerene and fullerene derivatives (buckyballs) as inhibitors of HIV-1 protease as they are precisely the right size and shape to fit into and inhibit the active site. Unfortunately, this work did not lead to viable drugs as the aqueous solubility characteristics of the hydrocarbon- based buckyballs proved to be an insurmountable challenge. Recently we have published a computational study of the molecular behavior of an important class of new polyhedral molecules with different hydrophobic/hydrophilic properties. While studying these molecules we realized that their size and shape is quite comparable to C60 while the nature of their bonds allows them to be much more water soluble. We are interested in performing a computational investigation of the feasibility of using these molecules as HIV-1 protease inhibitors. Previously, we have performed MD simulations of the HIV protease sans any ligands; with C60; and in complexation with our molecules, using stochastic dynamics as implemented in MacroModel (serial) on the OPLS2005/GBSA(water) surface. We would like to verify our results using a different force field and sampling algorithm and so we have been teaching ourselves to properly use the AMBER and Desmond simulation packages. We are requesting 30,0000 units in order to run AMBER and Desmond on multiple processors shortening the testing and learning times.