Computational methodology promises to revolutionize the process of designing drugs and antiviral agents. It has the potential to significantly reduce the time and costs associated with 1) the identification and ranking of potential lead compounds; and 2) the enhancement of their specificity and potency. the retroviral proteases present an important and suitable challenge to develop and improve our ability to use computer based techniques in designing appropriate inhibitors. The objectives of this research are to advance our procedures for the automated docking of substrates to proteins into a mature tool with which to aid in the drug design cycle of anti-retroviral agents. Initially we will extend the capabilities and efficiency of our docking algorithm. We will explore methods of incorporating limited protein mobility in the flaps of the aspartic proteases in order to better model the interaction of these target proteins with candidate inhibitors. We will also optimize the force field used in the docking so that the computed energetics of the simulation will accurately represent experimentally observed inhibitor interactions. Our goal in this phase is to allow the ranking of the inhibitory efficacy of trial drugs based on calculated energies of binding. In close collaboration with the other members of this program project, we will test and rank trial drugs using our docking protocol. Our initial targets will be the HIV protease and models for the FIV protease. Candidate drugs will be tested against both of these protease models in order to identify leads which may have more viability against mutational drift. Differential binding of drugs to retroviral proteases and to host aspartic proteases will be compared to enhance drug specificity. We will develop and test novel rational drug design strategies that will use recent computational algorithms to suggest novel drug candidates. Two different applications incorporating the Metropolis and the genetic algorithms will yield entirely new avenues of drug morphology. In our first approach we will use these algorithms to mutate drugs during the docking simulation. In the second approach we will grow and recombine fragments to yield new combinations. The tools that will be developed, tested and applied in this project should serve as a critical component in a state-of-the-art drug design cycle and will be made available to the scientific community.
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