Drug resistance in HIV protease: Utilizing specificity

Univ Of Massachusetts Med Sch Worcester, Worcester MA

Investigators

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Abstract

Variations in HIV-1 protease that confer drug resistance threaten to weaken the effectiveness of protease inhibitors toward the most potent target of the current anti-viral HIV therapy. Through the efforts of this multi-disciplinary program project team the ensemble of HIV-1 protease variants becomes the therapeutic target rather than one wild-type protease clone, and therefore the ensemble must be characterized. The research in this component project is central to the goals of the entire effort because it involves determining and analyzing the structural, dynamic, thermodynamic and functional properties of key variant proteases and their complexes with existing and novel inhibitors, as well as exploiting residual protease activity as an avenue of drug design. We address the following hypotheses: (1) Protease inhibitors that deviate from the consensus volume occupied by substrates within the active site (as previously characterized in our laboratory) are particularly vulnerable to resistance mutations. (2) Secondary mutations outside the active site that contribute to drug resistance affect inhibitor binding by altering the ensemble of conformations that the protease accesses. (3) Through the use of novel structure based designed peptides the residual activity of HIV-1 protease in highly drug resistant cases where all protease inhibitors fail can be used to target infected cells (and will be tested in the Swanstrom lab). Overall, we will work with the Shafer and Swanstrom labs to identify key variant HIV-1 proteases to be characterized crystallographically and computationally. The new structures generated by this effort, combined with existing protease structures in the public domain, will provide the basis for ensemble-based inhibitor design by the Gilson and Tidor labs. We will then characterize the most promising lead compounds resulting from the Rana lab's screens and Swanstrom lab resistance profiles and return the data to the computational groups for further rounds of optimization.