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Sci Transl Med. Author manuscript; available in PMC 2012 November 3.

Published in final edited form as:

PMCID: PMC3488347

NIHMSID: NIHMS389833

Lin Shen,^{1,}^{2} S. Alireza Rabi,^{1} Ahmad R. Sedaghat,^{1,}^{*} Liang Shan,^{1,}^{2} Jun Lai,^{1} Sifei Xing,^{1,}^{2} and Robert F. Siliciano^{1,}^{3,}^{†}

The publisher's final edited version of this article is available at Sci Transl Med

See other articles in PMC that cite the published article.

Control of HIV-1 replication was first achieved with regimens that included a nonnucleoside reverse transcriptase inhibitor (NNRTI) or a protease inhibitor (PI); however, an explanation for the high antiviral activity of these drugs has been lacking. Indeed, conventional pharmacodynamic measures like *IC _{50}* (drug concentration causing 50% inhibition) do not differentiate NNRTIs and PIs from less active nucleoside reverse transcriptase inhibitors (NRTIs). Drug inhibitory potential depends on the slope of the dose-response curve (

In 1997, regimens were developed that suppressed HIV-1 viremia to below the detection limit in most treated patients. These regimens combined two nucleoside analogue reverse transcriptase inhibitors (NRTIs) with an HIV-1 protease inhibitor (PI) (1–3). Combinations of two NRTIs and a non-nucleoside reverse transcriptase inhibitor (NNRTI) also proved effective (4,5). Collectively, these regimens, known as highly active antiretroviral therapy (HAART), transformed a previously fatal disease into a chronic condition that is well controlled in adherent patients. Despite HAART’s success, a fundamental theory explaining its effectiveness is lacking.

Drug resistance, which results both from the high error rate of reverse transcriptase and the dynamic nature of HIV-1 infection, is a major cause of treatment failure (6–10). The low probability of multiple simultaneous resistance mutations on the same genome clearly contributes to the success of triple combination therapy (11). However, suppression of HIV-1 replication is not simply the result of using three drugs; some triple NRTI combinations had suboptimal responses (5). Thus, in early treatment efforts, inclusion of a PI or NNRTI appeared essential for control of viral replication. Although the use of drugs acting through different mechanisms also contributes to the effectiveness of combination therapy, PIs and NNRTIs appeared to have greater antiviral activity than most NRTIs. Therefore, treatment guidelines recommend inclusion of a PI or NNRTI in most initial HAART regimens (5). Standard pharmacologic measures such as *IC _{50}* and inhibitory quotient do not distinguish PIs and NNRTIs from less active NRTIs (12). Thus, the fundamental pharmacodynamic principles underlying this successful treatment remain unclear. Instead, progress has depended on comparative clinical trials, which have recently established a role for newer drugs as well, such as integrase strand transfer inhibitors (InSTIs) and CCR5 antagonists (5).

We recently showed that the superior antiviral activity of PIs and NNRTIs relative to most NRTIs can be partially explained by the dose-response curve slope (*m*) (12). This parameter is included in all fundamental pharmacodynamic equations including the Hill equation (13), the sigmoidal *E _{max}* model (14), and the Chou-Talalay median effect equation (15). The slope parameter is related to the Hill coefficient describing intramolecular cooperativity in the binding of ligands to a multivalent receptor (13, 16). Positive cooperativity (

Since *m*>1 for NNRTIs and PIs cannot be explained by intramolecular cooperativity, a mechanistic explanation requires reconsidering the concept of cooperativity in drug action. Here we describe a critical subset model that explains steep slopes for these drugs in terms of intermolecular cooperativity, and we provide experimental validation for this model.

The simplest model describing dose-response relationships is the median effect equation (15). It is derived from the law of mass action, which governs dynamic equilibria in chemical reactions. The median effect equation can be written as:

$$log({f}_{a}/{f}_{u})=mlog\left([D]/{IC}_{50}\right)$$

(1)

$$\text{or},\phantom{\rule{0.38889em}{0ex}}\phantom{\rule{0.38889em}{0ex}}\phantom{\rule{0.38889em}{0ex}}{f}_{a}=\frac{1}{1+{\left(\frac{{IC}_{50}}{[D]}\right)}^{m}}$$

(2)

In the context of HIV-1 infection, *f _{a}* and

Some anti-HIV drugs have *m*>1 even though their target enzymes are univalent. To explain this observation, we developed a critical subset model based on mass action. The model envisions independent binding of multiple drug molecules to a set of identical targets, viral proteins, within each virus or virus-infected cell. Within a given virion or infected cell, the targeted proteins behave as a set of linked receptor sites that can be occupied to variable extents (Fig. 1A), depending on [*D*] and the affinity of individual drug-target interactions (*K _{d}*). Drug binding involves a series of equilibrium reactions mediating conversions between all possible partially occupied states (16). Assuming that replication requires some critical threshold number (

$${f}_{a}({n}_{T})=\frac{{\displaystyle \sum _{i={n}_{T}-c+1}^{{n}_{T}}}\left(\begin{array}{c}{n}_{T}\\ i\end{array}\right)[V]{\left(\frac{[D]}{{K}_{d}}\right)}^{i}}{{\displaystyle \sum _{i=0}^{{n}_{T}}}\left(\begin{array}{c}{n}_{T}\\ i\end{array}\right)[V]{\left(\frac{[D]}{{K}_{d}}\right)}^{i}}=\frac{{\displaystyle \sum _{i={n}_{T}-c+1}^{{n}_{T}}}\left(\begin{array}{c}{n}_{T}\\ i\end{array}\right){\left(\frac{[D]}{{K}_{d}}\right)}^{i}}{{\displaystyle \sum _{i=0}^{{n}_{T}}}\left(\begin{array}{c}{n}_{T}\\ i\end{array}\right){\left(\frac{[D]}{{K}_{d}}\right)}^{i}}$$

(3)

The critical subset model. **(A)** A virion (or virally infected cell) can be viewed as a multivalent receptor containing *n* identical copies of a given drug target such as HIV-1 protease. Although not physically linked to one another, these copies are spatially **...**

Consider for example HIV-1 protease. Multiple copies of protease participate in the maturation of each virion. Although not physically linked, they are constrained spatially within the virion. Thus, the virion can be considered a multivalent receptor with *n _{T}* binding sites, where

This model predicts the shapes of dose-response curves. To illustrate these predictions, we plotted hypothetical dose-response curves using median effect plots based on Equation 1 (Fig. 2). The median effect plot linearizes the dose-response curve for most drugs, with the slope of the resulting line equal to the Hill coefficient (Fig. 2A–B). We obtained complex dose-response curves with various *m* depending on *n _{T}* and

Steep dose-response curves predicted by the critical subset model. **(A)** Hypothetical log-log plot of *f*_{u} vs. drug concentration based on the median-effect model for drugs with different *m* values. The strong impact of slope on the suppression of infectivity **...**

Interestingly, the model predicts complex dose-response curves with non-linear median effect plots. In other words, *m* is different for different segments of the curve. As shown below, the dose-response curves of some antiretroviral drugs are similarly complex. According to the model, slope values are determined by *n _{T}* and

The model also predicts the relationship between *IC _{50}* and

A second factor affecting slope is heterogeneity in *n _{T}*. In other biological systems with all-or-none outcomes, heterogeneity in the number of drug targets flattens dose-response curves (20). For individual virions, infection is all-or-none, and thus heterogeneity arguments apply. When heterogeneity is limited, dose-response curves are steeper. Intuitively, this may be understood by considering that a homogeneous virus population will be inhibited over a narrow range of drug concentrations, whereas a more heterogeneous population will contain viruses that are easier or harder to inhibit, and therefore inhibition will be observed over a wider concentration range.

If *n _{T}* follows an integer-valued normal distribution with a standard deviation σ, then dose-response curves follow Equation 4, which gives

$${f}_{a}({n}_{T},\sigma )=\frac{{\displaystyle \sum _{{n}_{i}=c}^{{n}_{max}}}[{f}_{a}({n}_{i})P({n}_{i}|{n}_{T},\sigma )]}{{\displaystyle \sum _{{n}_{i}=c}^{{n}_{max}}}P({n}_{i}|{n}_{T},\sigma )}$$

(4)

where

$$P({n}_{i}\mid {n}_{T},\sigma )=\frac{1}{\sigma \sqrt{2\pi}}{e}^{\frac{-{({n}_{i}-{n}_{T})}^{2}}{2{\sigma}^{2}}}$$

(5)

and *f _{a}*(

The critical subset model makes testable predictions about how variation in *n _{T}* and

Effect of reductions in *n*_{F} on the dose-response curves for antiviral drugs. **(A)** Relative infectivity (*RI*) of phenotypically-mixed virions. Consider a population of virions with the same *n*_{T} but various numbers of active (*n*_{F}, blue crescents) and inactive **...**

The introduction of mutant enzyme subunits reduces infectivity, as some viruses will no longer contain a critical number of functional enzymes (Fig. 3A). The enzyme molecules targeted by RT and protease inhibitors are heterodimers and homodimers, respectively. If formation of mixed dimers from wild-type and mutant monomers follows random mixing, then the virions produced should contain reduced *n _{F}* following a binomial distribution. The

$$RI({n}_{T})=\sum _{{n}_{F}=c}^{{n}_{T}}P({n}_{F})=\sum _{{n}_{F}=c}^{{n}_{T}}\left(\begin{array}{c}{n}_{T}\\ {n}_{F}\end{array}\right){{P}_{f}}^{{n}_{F}}{(1-{P}_{f})}^{{n}_{T}-{n}_{F}}$$

(6)

*P(n _{F})* is the probability of getting

To test the critical subset model, phenotypically-mixed viruses can be assayed for susceptibility to drug inhibition. Dose-response curves for inhibition of viruses with various *f _{m}* can be predicted by Equation 4, where the calculation of

$${f}_{a}({n}_{i})=\frac{{\displaystyle \sum _{{n}_{F}=c}^{{n}_{i}}}\left[{f}_{a}({n}_{F})P({n}_{F})\right]}{RI({n}_{i})}$$

(7)

According to this model, the change in *IC _{50}* depends on the fractional occupancy of enzyme molecules required for inhibiting replication. Two contrasting situations with the same value of

Before testing the critical subset model, we validated several assumptions. One is that wild-type and mutant enzymes can be expressed in cells and incorporated into virions with similar efficiency. We chose well-characterized point mutations in the active sites of RT (D185N), protease (D25N), and integrase (D64E) that abolish enzyme activity without effects on multimer formation or virus assembly (18, 23, 24). Transfection efficiency was similar for the wild-type plasmid and mutant plasmids by flow cytometry (Fig. S2A). Wild-type and mutant RT and integrase were incorporated into the virions with similar efficiency (Fig. S2B–C). We also tested whether formation of mixed dimers from wild-type and mutant monomers follows random mixing and hence the binomial distribution. To obtain direct biochemical evidence for mixed dimer formation in cotransfected cells, we performed co-immunoprecipitations (Fig. S3). We introduced either an HA or a Flag tag into the N terminus of the wild-type and mutant enzymes. Viral proteins immunoprecipitated by either anti-HA or anti-Flag antibodies from cell lysates were immunoblotted with anti-Flag or anti-HA antibodies, respectively. Our results suggest that for both RT and protease, dimers with all of the four possible combinations – wild-type-wild-type, wild-type-mutant, mutant-wild-type, mutant-mutant – can be formed with approximately equal probability (Fig. S3A–B).

According to the model, drugs targeting steps involving multiple copies of the drug target should show steeper dose-response curves and dramatically better inhibition at drug concentrations above the *IC _{50}*. NNRTIs can interact with all RT molecules in the preintegration complex, and PIs can interact with all protease molecules in the maturing virion. These drug classes exhibit slopes >1.7 and high inhibitory potential (12). For NNRTIs and PIs, the model predicts that

We tested these predictions using the algorithm shown in Figure 4A. We first determined the apparent *n _{T}* and

Experimental validation of the critical subset model. **(A)** Algorithm for model validation from experimental data. Curves describing the dependence of the relative infectivity (*RI*) of phenotypically-mixed virions on *f*_{m} in the absence of drug were first **...**

To refine values for *n _{T}* and

Effect of phenotypic mixing on dose-response curves for NNRTIs and PIs. **(A)** Median effect plots of theoretical (dashed lines) and experimental (solid markers) dose-response curves for the NNRTIs EFV, NVP, and ETR with wild-type and phenotypically mixed **...**

Since *c*=2 for protease predicts a slope approaching 2 at high [*D*], the model does not explain *m*>2 for some PIs or the sharp upward inflection at high [*D*]. Therefore, we tested the model using PIs with *m*~2. For the wild-type dose-response curves, we considered only the initial linear part as described previously (12) and excluded the two data points at high [*D*]. *n _{T}*=9–15,

Having established values of *n _{T}* and

Taken together, these results suggest that the participation of multiple copies of RT and protease in the processes of reverse transcription and virion maturation, respectively, results in a form of intermolecular cooperativity that is manifested as steeper dose-response curves for the NNRTIs and PIs. The cooperative curves can be fit using the critical subset model without the need to postulate interactions between the active sites of individual enzyme molecules.

The InSTIs and the NRTIs exhibit slopes close to 1 (12). Unlike NNRTIs and PIs, these drugs target reactions in which a *single* molecular complex of one enzyme with viral nucleic acid mediates a critical step. For the InSTIs, the inhibited process is performed by precisely one molecular complex per infected cell, consisting of an integrase tetramer bound to the ends of the viral genome (26, 27). There is no possibility for intermolecular cooperativity because only a single drug target per virus mediates the targeted reaction. Other integrase molecules present in the preintegration complex are not targeted by InSTIs and are irrelevant. Similar reasoning can be applied to RT by breaking reverse transcription into individual steps, each consisting of attachment of one nucleotide. The NRTIs target a single enzyme:nucleic acid complex per infected cell, namely the RT molecule that is adding the next nucleotide to the viral cDNA (17). Other RT molecules in the reverse transcription complex are irrelevant and are not targeted. When a single target mediates the critical reaction (*n _{T}*=c=1), Equation 3 reduces to the median effect equation (Equation 2) with

To test these predictions, we generated phenotypically mixed virions in which a fraction of the integrase or RT molecules had the inactivating catalytic site mutations D64E or D185N, respectively (23, 24). Drug susceptibility was measured using the single round infectivity assay. The slopes were close to 1 and exhibited minimal change against modulated viruses as predicted by the model (Fig. 6, S4). The *IC _{50}* values for the InSTIs raltegravir (RAL) and elvitegravir (ELV) against phenotypically-mixed virions were unchanged (Fig. 6A). For all NRTIs except for zidovudine, there was no decrease in

Steep dose-response curves generally reflect positive cooperativity. In the classical Hill model, a slope >1 reflects cooperative binding of multiple ligands to a multivalent receptor. However, RT and protease have only one binding site for NNRTIs and PIs, respectively, and extensive kinetics studies with purified RT and protease have not provided evidence for cooperativity. Rather, the high slopes of NNRTIs and PIs in infectivity assays reflect unique aspects of the inhibition of viral replication that are not apparent in the behavior of the isolated enzymes.

Here we present a critical subset model that provides a molecular explanation for these steep dose-response curves. We hypothesized that within a given infected cell or virus particle, the viral proteins targeted by a drug behave as a set of linked receptor sites whose fractional occupancy determines whether the relevant step in the virus life cycle is completed before irreversible decay processes intervene. When multiple copies of a target are involved in the inhibited reaction, there exists a form of intermolecular cooperativity that does not depend on alterations in the binding properties of individual sites. This model predicts slopes >1 for NNRTIs and PIs because multiple copies of RT and protease are involved in the processes inhibited by these drugs, and a critical subset of these molecules are required for viral replication. The model successfully predicted shifts in the dose-response curves against viruses with decreased *n _{F}*. For NRTIs and InSTIs, only one copy of RT or integrase is relevant to the inhibited reaction. In this situation,

According to the model, slope is determined by *n _{T}* and

Since *n _{T}* and

The concepts developed here can be applied to other drug classes and other viruses. However, additional factors may complicate the analysis. For drugs affecting HIV-1 entry, the targets are trimeric, and interactions within and between trimers must be considered. One particularly unique aspect of HIV-1 RT, protease, and integrase inhibitors is that they target enzymes that are present within the virion or that are assembled from precursor polyproteins present within the virion. In this situation, constraints inherent in virion assembly limit heterogeneity (σ) in *n _{T}*. As shown here, heterogeneity flattens dose-response curves. Thus in the case of viruses for which the drug targets are expressed and act within infected cells, heterogeneity in the number of enzymes per cell may obscure the cooperativity that is expected when multiple copies of a drug target act together to complete a step in the life cycle.

A variety of elegant models describing steep dose-response curves for ion channels and other multivalent receptors have been developed (16, 33–35), but models to explain cooperative dose-response curves for antiviral drugs have not been described. It is possible that other models will be developed to explain the complex dose-effect relationships for antiretroviral drugs. For example, saturable drug binding to alternative intracellular sites could result in an upward inflection within a small concentration range above the concentration that saturates the alternative sites. However, a model such as the one described here would still be needed to explain slopes >1 in other regions of the dose-response curve. Although the overall slopes for NRTIs are close to 1, there are subtle inflections in the dose-response curves for some NRTIs (Fig. 6B, S4). These may in part reflect binding to alternative intracellular sites or the reactions needed to produce the active triphosphate forms of the NRTIs. Nevertheless, the fact that slope varies in a class-specific way despite large intraclass differences in structure suggests that the slope parameter is a direct reflection of the mechanism of inhibition, as in the model described here.

The critical subset model clearly represents an oversimplification of a very complex problem. Nevertheless, it explains the steep dose-response curves of NNRTIs and PIs without assuming that the binding of drug to one site affects the *K _{d}* of binding to other sites. A steep slope means that small increases in drug concentration above the

Detailed descriptions of experimental Materials and Methods are available in the Supplementary Material.

The *RI* values of recombinant viruses generated with different *f _{m}* were obtained by normalizing to the infectivity of the wild-type virus. Theoretical

Dose-response curves for virus stocks with various *f _{m}* were obtained by normalizing to the %GFP

All theoretical curve simulations, calculations, curve fitting and statistical analyses were performed using Matlab version 7.1 (The MathWorks) or Microsoft Office Excel 2003.

Fig. S1. Effect of heterogeneity in the total number of enzyme molecules (*n _{T}*) on dose-response curves for anti-HIV drugs.

Fig. S2. Virus production and levels of reverse transcriptase (RT) and integrase (IN) associated with virions produced by cells transfected with wild type-or mutant proviral constructs.

Fig. S3. Mixed dimer formation detected by coimmunoprecipitation (co-IP) of viral protein variants.

Fig. S4. Effect of phenotypic mixing on dose-response curves for NRTIs.

Table S1. *r*^{2} values for the fitting of wt dose-response curves with and without the inclusion of σ.

Table S2. Experimental and theoretical values for log (*f _{a}*/

Table S3. Predicted and experimental values for log (*f _{a}*/

Table S4. Experimental and theoretical values of log (*f _{a}*/

Table S5. Predicted and experimental values for log (*f _{a}*/

Supplementary Note 1. Calculation of slopes for complex dose-response curves generated by the critical subset model.

Supplementary Note 2. Detailed description of model predictions and experimental confirmation for EFV and LPV.

Supplementary Note 3. Effect of drug binding to inactive enzymes on *f _{a}*.

Click here to view.^{(2.1M, doc)}

We thank the AIDS Research and Reference Reagent Program of the US National Institutes of Health and Merck for providing anti-HIV-1 drugs. We thank Drs. J. Liu, E. Freire, and X. Yu for helpful discussions. We thank Drs. J. Siliciano, J. Blankson, and C. Durand for helpful review of the manuscript.

**Funding:** This work was supported by the US National Institutes of Health grant AI081600 and by the Howard Hughes Medical Institute.

^{**}This manuscript has been accepted for publication in *Science Translational Medicine*. This version has not undergone final editing. Please refer to the complete version of record at http://stm.sciencemag.org/content/3/91/91ra63.full.html. The manuscript may not be reproduced or used in any manner that does not fall within the fair use provisions of the Copyright Act without the prior, written permission of AAAS.

**Author contributions:** L.Shen and R.F.S. designed the experiments. L.Shen, J.L., and S.X. performed the experiments. L.Shen, S.A.R., A.R.S., L.Shan, and R.F.S. analyzed data. All of the authors contributed to the manuscript preparation.

**Competing interests:** The authors declare that they have no competing interests.

1. Gulick RM, Mellors JW, Havlir D, Eron JJ, Gonzalez C, McMahon D, Richman DD, Valentine FT, Jonas L, Meibohm A, Emini EA, Chodakewitz JA. Treatment with Indinavir, Zidovudine, and Lamivudine in Adults with Human Immunodeficiency Virus Infection and Prior Antiretroviral Therapy. N Engl J Med. 1997;337:734–739. [PubMed]

2. Hammer SM, Squires KE, Hughes MD, Grimes JM, Demeter LM, Currier JS, Eron JJ, Jr, Feinberg JE, Balfour HH, Jr, Deyton LR, Chodakewitz JA, Fischl MA. A Controlled Trial of Two Nucleoside Analogues Plus Indinavir in Persons with Human Immunodeficiency Virus Infection and CD4 Cell Counts of 200 Per Cubic Millimeter Or Less. AIDS Clinical Trials Group 320 Study Team. N Engl J Med. 1997;337:725–733. [PubMed]

3. Perelson AS, Essunger P, Cao Y, Vesanen M, Hurley A, Saksela K, Markowitz M, Ho DD. Decay Characteristics of HIV-1-Infected Compartments during Combination Therapy. Nature. 1997;387:188–191. [PubMed]

4. Staszewski S, Morales-Ramirez J, Tashima KT, Rachlis A, Skiest D, Stanford J, Stryker R, Johnson P, Labriola DF, Farina D, Manion DJ, Ruiz NM. Efavirenz Plus Zidovudine and Lamivudine, Efavirenz Plus Indinavir, and Indinavir Plus Zidovudine and Lamivudine in the Treatment of HIV-1 Infection in Adults. Study 006 Team. N Engl J Med. 1999;341:1865–1873. [PubMed]

5. Panel on Antiretroviral Guidelines for Adult and Adolescents, Guidelines for the use of Antiretroviral Agents in HIV-1-Infected Adults and Adolescents. DHHS. 2011 Jan;:1–166.

6. Larder BA, Darby G, Richman DD. HIV with Reduced Sensitivity to Zidovudine (AZT) Isolated during Prolonged Therapy. Science. 1989;243:1731–1734. [PubMed]

7. Coffin JM. HIV Population Dynamics in Vivo: Implications for Genetic Variation, Pathogenesis, and Therapy. Science. 1995;267:483–489. [PubMed]

8. Wei X, Ghosh SK, Taylor ME, Johnson VA, Emini EA, Deutsch P, Lifson JD, Bonhoeffer S, Nowak MA, Hahn BH, Saag MS, Shaw GM. Viral Dynamics in Human Immunodeficiency Virus Type 1 Infection. Nature. 1995;373:117–122. [PubMed]

9. Ho DD, Neumann AU, Perelson AS, Chen W, Leonard JM, Markowitz M. Rapid Turnover of Plasma Virions and CD4 Lymphocytes in HIV-1 Infection. Nature. 1995;373:123–126. [PubMed]

10. Wodarz D, Nowak MA. Mathematical Models of HIV Pathogenesis and Treatment. Bioessays. 2002;24:1178–1187. [PubMed]

11. Ribeiro RM, Bonhoeffer S, Nowak MA. The Frequency of Resistant Mutant Virus before Antiviral Therapy. AIDS. 1998;12:461–465. [PubMed]

12. Shen L, Peterson S, Sedaghat AR, McMahon MA, Callender M, Zhang H, Zhou Y, Pitt E, Anderson KS, Acosta EP, Siliciano RF. Dose-Response Curve Slope Sets Class-Specific Limits on Inhibitory Potential of Anti-HIV Drugs. Nat Med. 2008;14:762–766. [PMC free article] [PubMed]

13. Hill AV. The Possible Effects of the Aggregation of the Molecules of Haemoglobin on its Dissociation Curves. J Physiol. 1910;40:iv–vii.

14. Holpord NHG, Scheiner LB. Understanding the Dose-effect Relationship: Clinical Applications of Pharmacokinetic-pharmacodynamic Models. Clin Pharmacokinet. 1981;6:429–453. [PubMed]

15. Chou TC. Theoretical Basis, Experimental Design, and Computerized Simulation of Synergism and Antagonism in Drug Combination Studies. Pharmacol Rev. 2006;58:621–681. [PubMed]

16. Weiss JN. The Hill Equation Revisited: Uses and Misuses. FASEB J. 1997;11:835–841. [PubMed]

17. Kohlstaedt LA, Wang J, Friedman JM, Rice PA, Steitz TA. Crystal Structure at 3.5 A Resolution of HIV-1 Reverse Transcriptase Complexed with an Inhibitor. Science. 1992;256:1783–1790. [PubMed]

18. Miller M, Schneider J, Sathyanarayana BK, Toth MV, Marshall GR, Clawson L, Selk L, Kent SB, Wlodawer A. Structure of Complex of Synthetic HIV-1 Protease with a Substrate-Based Inhibitor at 2.3 A Resolution. Science. 1989;246:1149–1152. [PubMed]

19. Mitsuya H, Maeda K, Das D, Ghosh AK. Development of Protease Inhibitors and the Fight with Drug-Resistant HIV-1 Variants. Adv Pharmacol. 2008;56:169–197. [PubMed]

20. Hoffman A, Goldberg A. The Relationship between Receptor-Effector Unit Heterogeneity and the Shape of the Concentration-Effect Profile: Pharmacodynamic Implications. J Pharmacokinet Biopharm. 1994;22:449–468. [PubMed]

21. Ambrose Z, Julias JG, Boyer PL, Kewalramani VN, Hughes SH. The Level of Reverse Transcriptase (RT) in Human Immunodeficiency Virus Type 1 Particles Affects Susceptibility to Nonnucleoside RT Inhibitors but Not to Lamivudine. J Virol. 2006;80:2578–2581. [PMC free article] [PubMed]

22. Yang X, Kurteva S, Ren X, Lee S, Sodroski J. Stoichiometry of Envelope Glycoprotein Trimers in the Entry of Human Immunodeficiency Virus Type 1. J Virol. 2005;79:12132–12147. [PMC free article] [PubMed]

23. Le Grice SF, Naas T, Wohlgensinger B, Schatz O. Subunit-Selective Mutagenesis Indicates Minimal Polymerase Activity in Heterodimer-Associated p51 HIV-1 Reverse Transcriptase. EMBO J. 1991;10:3905–3911. [PubMed]

24. Hazuda DJ, Felock P, Witmer M, Wolfe A, Stillmock K, Grobler JA, Espeseth A, Gabryelski L, Schleif W, Blau C, Miller MD. Inhibitors of Strand Transfer that Prevent Integration and Inhibit HIV-1 Replication in Cells. Science. 2000;287:646–650. [PubMed]

25. Rose JR, Babe LM, Craik CS. Defining the Level of Human Immunodeficiency Virus Type 1 (HIV-1) Protease Activity Required for HIV-1 Particle Maturation and Infectivity. J Virol. 1995;69:2751–2758. [PMC free article] [PubMed]

26. Pandey KK, Bera S, Zahm J, Vora A, Stillmock K, Hazuda D, Grandgenett DP. Inhibition of Human Immunodeficiency Virus Type 1 Concerted Integration by Strand Transfer Inhibitors which Recognize a Transient Structural Intermediate. J Virol. 2007;81:12189–12199. [PMC free article] [PubMed]

27. Hare S, Gupta SS, Valkov E, Engelman A, Cherepanov P. Retroviral Intasome Assembly and Inhibition of DNA Strand Transfer. Nature. 2010;464:232–236. [PMC free article] [PubMed]

28. Briggs JA, Simon MN, Gross I, Krausslich HG, Fuller SD, Vogt VM, Johnson MC. The Stoichiometry of Gag Protein in HIV-1. Nat Struct Mol Biol. 2004;11:672–675. [PubMed]

29. Jacks T, Power MD, Masiarz FR, Luciw PA, Barr PJ, Varmus HE. Characterization of Ribosomal Frameshifting in HIV-1 Gag-Pol Expression. Nature. 1988;331:280–283. [PubMed]

30. Pettit SC, Clemente JC, Jeung JA, Dunn BM, Kaplan AH. Ordered Processing of the Human Immunodeficiency Virus Type 1 GagPol Precursor is Influenced by the Context of the Embedded Viral Protease. J Virol. 2005;79:10601–10607. [PMC free article] [PubMed]

31. Pettit SC, Everitt LE, Choudhury S, Dunn BM, Kaplan AH. Initial Cleavage of the Human Immunodeficiency Virus Type 1 GagPol Precursor by its Activated Protease Occurs by an Intramolecular Mechanism. J Virol. 2004;78:8477–8485. [PMC free article] [PubMed]

32. Muller B, Anders M, Akiyama H, Welsch S, Glass B, Nikovics K, Clavel F, Tervo HM, Keppler OT, Krausslich HG. HIV-1 Gag Processing Intermediates Trans-Dominantly Interfere with HIV-1 Infectivity. J Biol Chem. 2009;284:29692–29703. [PMC free article] [PubMed]

33. Agneter E, Singer EA, Sauermann W, Feuerstein TJ. The Slope Parameter of Concentration-Response Curves used as a Touchstone for the Existence of Spare Receptors. Naunyn Schmiedebergs Arch Pharmacol. 1997;356:283–292. [PubMed]

34. Castrignano T, Aluffi-Pentini F, Parisi V. A Novel Parameter to Estimate the Minimum Number of Bound Ligands Needed to Activate an Ion Channel. J Theor Biol. 1999;199:97–103. [PubMed]

35. Michel D. Cooperative Equilibrium Curves Generated by Ordered Ligand Binding to Multi-Site Molecules. Biophys Chem. 2007;129:284–288. [PubMed]

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