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Nucleocapsid Annealing-Mediated Electrophoresis NAME Assay Allows the Rapid Identification of HIV-1 Nucleocapsid Inhibitors
Published on: January 19, 2015
Nucleocapsid Protein: A Desirable Target for Future Therapies Against HIV-1
Mattia Mori1, Lesia Kovalenko, Sébastien Lyonnais
1Dipartimento di Biotecnologie Chimica e Farmacia, Università degli Studi di Siena, via A. Moro 2, 53100, Siena, Italy.
Abstract:
The currently available anti-HIV-1 therapeutics is highly beneficial to infected patients. However, clinical failures occur as a result of the ability of HIV-1 to rapidly mutate. One approach to overcome drug resistance is to target HIV-1 proteins that are highly conserved among phylogenetically distant viral strains and currently not targeted by available therapies. In this respect, the nucleocapsid (NC) protein, a zinc finger protein, is particularly attractive, as it is highly conserved and plays a central role in virus replication, mainly by interacting with nucleic acids. The compelling rationale for considering NC as a viable drug target is illustrated by the fact that point mutants of this protein lead to noninfectious viruses and by the inability to select viruses resistant to a first generation of anti-NC drugs. In our review, we discuss the most relevant properties and functions of NC, as well as recent developments of small molecules targeting NC. Zinc ejectors show strong antiviral activity, but are endowed with a low therapeutic index due to their lack of specificity, which has resulted in toxicity. Currently, they are mainly being investigated for use as topical microbicides. Greater specificity may be achieved by using non-covalent NC inhibitors (NCIs) targeting the hydrophobic platform at the top of the zinc fingers or key nucleic acid partners of NC. Within the last few years, innovative methodologies have been developed to identify NCIs. Though the antiviral activity of the identified NCIs needs still to be improved, these compounds strongly support the druggability of NC and pave the way for future structure-based design and optimization of efficient NCIs.
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