1.HIV-1 Integrase Strand Transfer Inhibitors with Reduced Susceptibility to Drug Resistant Mutant Integrases.
Zhao XZ, Smith SJ, Maskell DP1, Metifiot M2, Pye VE1, Fesen K2, Marchand C2, Pommier Y2, Cherepanov P1,3, Hughes SH2, Burke TR Jr. ACS Chem Biol. 2016 Apr 15;11(4):1074-81. doi: 10.1021/acschembio.5b00948. Epub 2016 Feb 5.
HIV integrase (IN) strand transfer inhibitors (INSTIs) are among the newest anti-AIDS drugs; however, mutant forms of IN can confer resistance. We developed noncytotoxic naphthyridine-containing INSTIs that retain low nanomolar IC50 values against HIV-1 variants harboring all of the major INSTI-resistant mutations. We found by analyzing crystal structures of inhibitors bound to the IN from the prototype foamy virus (PFV) that the most successful inhibitors show striking mimicry of the bound viral DNA prior to 3'-processing and the bound host DNA prior to strand transfer. Using this concept of "bi-substrate mimicry," we developed a new broadly effective inhibitor that not only mimics aspects of both the bound target and viral DNA but also more completely fills the space they would normally occupy. Maximizing shape complementarity and recapitulating structural components encompassing both of the IN DNA substrates could serve as a guiding principle for the development of new INSTIs.
2.Discovery of Novel HIV-1 Integrase Inhibitors Using QSAR-Based Virtual Screening of the NCI Open Database.
Ko GM, Garg R1, Bailey BA, Kumar S. Curr Comput Aided Drug Des. 2016 Apr 13. [Epub ahead of print]
Quantitative structure-activity relationship (QSAR) models can be used as a predictive tool for virtual screening of chemical libraries to identify novel drug candidates. We report the results of a study performed for descriptor selection, QSAR model development, and virtual screening for identifying novel HIV-1 integrase inhibitor candidates. A comparison of the performances of three evolutionary algorithms: differential evolution-binary particle swarm optimization (DE-BPSO), binary particle swarm optimization (BPSO), and genetic algorithms (GA) for descriptor selection showed that DE-BPSO has a significant performance improvement over BPSO and GA. Three QSAR models were developed from an ensemble of multiple linear regression (MLR), partial least squares (PLS), and extremely randomized trees (ERT) based models. These models were then used in consensus as a predictive tool for virtual screening of the National Cancer Institute (NCI) Open Database containing 265,242 compounds, of which six compounds were identified to be highly active (pIC_50>6).
3.Methods for the Analyses of Inhibitor-Induced Aberrant Multimerization of HIV-1 Integrase.
Kessl JJ1, Sharma A2,3, Kvaratskhelia M2. Methods Mol Biol. 2016;1354:149-64. doi: 10.1007/978-1-4939-3046-3_10.
HIV-1 integrase (IN) is an important therapeutic target as its function is essential for the viral lifecycle. The discovery of multifunctional allosteric IN inhibitors or ALLINIs, which potently impair viral replication by promoting aberrant, higher order IN multimerization as well as inhibit IN interactions with its cellular cofactor, LEDGF/p75, has opened new venues to exploit IN multimerization as a therapeutic target. Furthermore, the recent discovery of multimerization selective IN inhibitors or MINIs, has provided new investigational probes to study the direct effects of aberrant IN multimerization in vitro and in infected cells. Here we describe three complementary methods designed to detect and quantify the effects of these new classes of inhibitors on IN multimerization. These methods include a homogenous time-resolved fluorescence-based assay which allows for measuring EC50 values for the inhibitor-induced aberrant IN multimerization, a dynamic light scattering-based assay which allows for monitoring the formation and sizes of oligomeric IN particles in a time-dependent manner, and a chemical cross-linking-based assay of interacting IN subunits which allows for the determination of IN oligomers in viral particles.
4.The Competitive Interplay between Allosteric HIV-1 Integrase Inhibitor BI/D and LEDGF/p75 during the Early Stage of HIV-1 Replication Adversely Affects Inhibitor Potency.
Feng L1, Dharmarajan V2, Serrao E3, Hoyte A1, Larue RC1, Slaughter A1, Sharma A1, Plumb MR1, Kessl JJ1, Fuchs JR4, Bushman FD5, Engelman AN3, Griffin PR2, Kvaratskhelia M1. ACS Chem Biol. 2016 Mar 2. [Epub ahead of print]
Allosteric HIV-1 integrase inhibitors (ALLINIs) have recently emerged as a promising class of antiretroviral agents and are currently in clinical trials. In infected cells, ALLINIs potently inhibit viral replication by impairing virus particle maturation but surprisingly exhibit a reduced EC50 for inhibiting HIV-1 integration in target cells. To better understand the reduced antiviral activity of ALLINIs during the early stage of HIV-1 replication, we investigated the competitive interplay between a potent representative ALLINI, BI/D, and LEDGF/p75 with HIV-1 integrase. While the principal binding sites of BI/D and LEDGF/p75 overlap at the integrase catalytic core domain dimer interface, we show that the inhibitor and the cellular cofactor induce markedly different multimerization patterns of full-length integrase. LEDGF/p75 stabilizes an integrase tetramer through the additional interactions with the integrase N-terminal domain, whereas BI/D induces protein-protein interactions in C-terminal segments that lead to aberrant, higher-order integrase multimerization.