COMPUTATIONAL AND BIOINFORMATIC MODELING AND ANALYSIS OF HIV DRUG RESISTANCE

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The long term goal of this research is to develop an effective and extensible computational model of HIV drug therapy to help in fighting the increasingly prevalent problem of drug resistance in. Utilizing a hierarchy of methods that range from atomic level molecular co-evolution to bioinformatics analysis of patient data, we will characterize the mutational landscape available to the virus under drug selection pressure and develop structure-based design methods that can produce inhibitors and therapeutic strategies to efficiently defeat escape. Our work will focus primarily on the well-characterized HIV protease, but will extend to other HIV drug targets to test the generality of our methods. In this research we will evaluate two basic hypotheses: 1) There exists a relatively small number of definable classes of HIV protease mutations, such that mutants within a given class will show strong cross resistance to a given inhibitor, and mutants in different classes wil not show cross-resistance. 2) Fragment-based design combined with computational modeling of protein flexibility will identify inhibitors that expand the protease "target space" including identification of exosites and design of flexibility wedges that block critical functional motions of HIV protease. Working in conjunction with the fragment-based crystallographic studies undertaken in Project 2 and the combinatorial chemical syntheses in Project 3 we will develop and apply computational methods for fragment-based drug design. Utilizing dynamic models of HIV protease wild type and mutants our FightAIDS@Home Internet distributed computing network will provide the needed computational power to screen large fragment libraries and evaluate dockings of promising linked fragments. Based on combination of experimental results on ex vivo resistance evolution from Project 4 and time course patient treatment data from Core C and by running and analyzing massive computational coevolution experiments, we will characterize the space of possible mutants, separating the range of possible viable mutants into discrete structural classes and identifying key mutant structures within each class for use in drug design. We will develop models of viral fitness that incorporate these multiple sources of data, and then use these models within larger simulations of viral evolution during the course of drug therapy.