Marilyn B. Kroeger Smith

Structure and mechanism of action of nonnucleoside inhibitors of HIV-1 reverse transcriptase: strategies to combat drug resistance

Abstract In the past few years, drug research has focused on three HIV-1 enzymes, reverse transcriptase (RT), protease, and integrase. In the case of RT, a number of potent inhibitors have been discovered. These can be classified into two distinct groups, nucleoside analogs and nonnucleoside inhibitors; however, mutations in RT have allowed the virus to develop resistance to all of the known drugs. In order to better understand the interactions between amino acid residues in the protein and nonnucleoside inhibitors, a computer model of the nonnucleoside inhibitor binding pocket of RT has been developed, using a subset of amino acid residues surrounding the pocket. The results of molecular mechanics minimizations of three RT/nonnucleoside inhibitor complexes showed that the resultant total energies of complexation (binding energies) correlated with EC 50 values if and only if the calculations were carried out using coordinates from the cognate complex while allowing for adjustments of the protein relative to the inhibitor. If a model was constructed using only the crystal data of one particular RT/inhibitor complex (RT/8-Cl TIBO), the calculations did not correctly order the other inhibitors. The difficulty in devising such a “generic” model nonnucleoside binding site in HIV-1 RT is likely due to the inherent flexibility of the enzyme. A comparison of the structure(s) of HIV-1 RT in complexes with different nonnucleoside inhibitors shows that the enzyme readily adapts to the shape of each inhibitor upon complexation. In contrast to the side-chain residues of HIV protease, the amino acid residues surrounding the binding pocket in RT adopt geometries that are unique to each bound inhibitor, adopting positions that make tight van der Waals contacts. Accompanying these changes at the site where the inhibitor binds are alterations in the geometry of the nearby polymerase active site. These changes can be conveniently monitored by measuring the increase in the distance between residue G231 in the RT primer grip region and aspartyl residues (D110, D185, and D186) in the polymerase active site. The magnitude of the change in this distance correlates inversely with inhibitor EC 50 , suggesting a possible mechanism of action of the drugs. Calculations using a site where various amino acids residues were changed to simulate mutations in RT that induce resistance to the nonnucleoside inhibitors revealed that a combination of less favorable inhibitor–protein interactions and slight geometry changes in the polymerase active site are responsible for the decreased effectiveness of the inhibitors against mutant RTs. The modeling results are discussed with regard to both the mechanism of inhibition as well as application of these insights to strategies for the design of better nonnucleoside inhibitors.

Energetic effects for observed and unobserved HIV-1 reverse transcriptase mutations of residues L100, V106, and Y181 in the presence of nevirapine and efavirenz.

The effect of mutations on amino acid residues L100, V106, and Y181 for unbound HIV-1 reverse transcriptase (RT) and RT bound to nevirapine and efavirenz was investigated using Monte Carlo/free energy perturbation calculations. Using both native and bound crystal structures of RT, mutation of the amino acid residues to both those observed and unobserved in patients was carried out. The results of the calculations revealed that the variant that survives in patients dosed with either nevirapine or efavirenz had a more positive Delta Delta G value than other variants that were not observed in patients. These data suggest that the mutation observed in patients is the most effective (the one that binds the drug most weakly) of all possible codon change mutations.

Assault on resistance : The use of computational chemistry in the development of anti-HIV drugs

While many inhibitors of the Human Immunodeficiency Virus (HIV), the causative agent of Acquired Immunodeficiency Syndrome (AIDS), have been developed, the problem of drug resistance has continued to plague the fight against the disease. The ability of computers to aid in the drug discovery process, and by default the resistance problem, has increased dramatically as the speed of computers and sophistication of associated calculation programs has grown. In particular, the capability of predicting a compounds ability to combat resistance prior to synthesis of drug candidates has proven particularly desirable. Since resistance can develop against a specific drug designed to inhibit only one stage of the viral cycle, combinations of drugs directed at more than one step have proven to be more effective than a single drug given alone. While the introduction of this combination therapy (termed highly active antiretroviral therapy (HAART)) has significantly decreased the death rate from HIV infections, resistance problems still arise. This paper will review previous approaches and address current and future computational strategies used in the design of second-generation and beyond drugs.

HIV-1 Reverse Transcriptase Variants: Molecular Modeling of Y181C, V106A, L100I, and K103N Mutations with Nonnucleoside Inhibitors Using Monte Carlo Simulations in Combination with a Linear Response Method

The energies and physical descriptors for the binding of 21 novel 1-(2,6-difluorobenzyl)-2-(2,6-difluorophenyl)-benzimidazole (BPBI) analogs to HIV-1 reverse transcriptase (RT) variants Y181C, L100I, V106A, and K103N have been determined using Monte Carlo (MC) simulations. The crystallographic structure of the lead compound, 4-methyl BPBI, was used as a starting point to model the inhibitors in both the mutant bound and the unbound states. The energy terms and physical descriptors obtained from the calculations were reasonably correlated with the respective experimental EC50 values for the inhibitors against the various mutant RTs. Using the linear response correlations from the calculations, 2 novel BPBI inhibitors have been designed and simulations have been carried out. The results show the computed deltaG(binding) values match the experimental data for the analogs. Given the ongoing problem with drug resistance, the ability to predict the activity of novel analogs against variants prior to synthesis is highly advantageous.