INTRODUCTION

Resistance to HIV-1 infection is possible when there is a mutation of a 32-base pair deletion (∆32) in the CCR5 (R5) protein and when there is an absence of CCR5 on the cell surface [1-3]. Heterozygotes for the (∆32) mutation also have delayed the development of the HIV illness with less rapid reductions in CD4 cell counts and reduced average viral loads [1-3]. Consequently, CCR5 is an attractive target for pharmaceutical therapies to combat HIV-1 infection [1]. When it was found that HIV enters cells by binding to the CD4 receptor [1-3], research studies focused more on forming inhibitors that could inhibit this initial binding stage of entry [1]. However, it was later discovered that the CD4 receptor was not the complete piece for the binding required for HIV entry into the host cell. It was found that a coreceptor was needed for HIV entry as well. The coreceptors CC chemokine receptor 5 (CCR5) [1] and CXC chemokine receptor 4 (CXCR4) [1-3] were revealed a few years after the discovery of CD4. CCR5 is a G protein-coupled receptor with seven transmembrane domains, in which the gp120 envelope protein from HIV binds to the N-terminus and to the extracellular loops of CCR5 to gain entry into a T cell (Figure 1a).  

The innate ligands of CCR5 include Regulated upon Activation, Normal T-cell Expressed, and Secreted [RANTES], macrophage inflammatory protein-1 alpha [MIP-1α], and macrophage inflammatory protein-1 beta [MIP-1β]) and these CCR5 ligands were found to be strong inhibitors of HIV entry [2, 3]. This initiated research studies to find synthetic compounds to inhibit CCR5 and block HIV entry into host cells [1]. CCR5 has surfaced on many cells including T lymphocytes, dendritic cells, and macrophages [4], in which CCR5-tropic HIV-1 strains cause the transmission of the virus [5]. Multiple products to target CCR5 inhibitors have been formed. There are multiple CCR5 antagonists, including maraviroc, aplaviroc, vicriviroc, INCB009471, and TBR 652, which have been developed. This first class of anti-HIV drugs emphasizes the host cellular pathways and notthe inhibition of viral enzyme processes [1]. Other CCR5 inhibitors in development are antibodies called PRO 140 and HGS004. PRO 140 is a humanized monoclonal antibody that can bind to CCR5 and block CCR5-tropic HIV-1 viral entry [6]. HGS004 is a human immunoglobulin G4 monoclonal antibody that is a CCR5 inhibitor [1].

Monoclonal antibodies (mAbs) that act as antagonists of CCR5 have been increasingly examined [7] and have led to many potential therapeutic solutions [3, 8-20]. However, currently, there is information lacking about the structural interaction between mAbs and CCR5 [21]. Thus, the understanding of the structural CCR5 blockade via mAbs is lacking and limited. Additionally, there is a need to improve the molecular aspects of the maraviroc drug [22]. However, there is a dualism between small chemical compounds and protein-based CCR5 antagonists, in which an interaction between each may advance the improvement of the drug design for both classes of molecular CCR5 antagonist types [23]. Anti-CCR5 mAbs isotypes can target epitopes located on the amino-terminal domain, the second extracellular loops (ECL2), and other multiple extracellular domains, including the first extracellular loop (ECL1) of CCR5 [24] (Figures 1b and 1d). These anti-R5 mAbs isotypes that bind to the N-terminus and the extracellular loops of R5 would directly block the binding of HIV viruses to CCR5 as a competitive inhibitor (Figures 1b and 1d). An anti-R5 mAb that binds to the transmembrane domains of CCR5 is not as well-known, but this anti-R5 mAb would act more as a non-competitive inhibitor type that is allosteric (Figure 1c). An anti-R5 mAb that binds to the transmembrane domains of CCR5 would be a non-competitive inhibitor because these anti-R5 mAbs do not bind to the epitopes of CCR5 that HIV-1 envelope proteins directly bind to for host cell entry (Figure 1c). Another type of CCR5 inhibition could include an uncompetitive type of inhibition, in which an anti-R5 mAb binds both to an extracellular loop and to the gp120 HIV-1 envelope protein by mimicking the CD4 receptor (Figure 1d).

For these reasons, the present study attempts to address the research question of which molecular and structural isotype of an anti-CCR5 mAb would be the most potent for inhibiting HIV envelope protein binding to lymphocytes. Thus, this study produced a predictive model, utilizing Michaelis-Menten enzyme kinetics, to assess which type of anti-R5 mAb inhibition, either competitive, non-competitive, or uncompetitive, would be the most effective form of anti-R5 mAb inhibition. In this study, the reaction rates of the CCR5 to HIV-1 gp120 relative to the types of anti-R5 inhibition (i.e. competitive, non-competitive, and uncompetitive) were predicted by forming a non-linear regression analysis of enzyme kinetic data extrapolated from the known molarities of CCR5 as the enzyme (R5E), HIV-1gp120 envelope proteins as the substrate (gp120S), from the concentrations of anti-R5 mAb, and existing reaction rate constants specific for the R5-to-gp120 binding interactions. 

MATERIALS AND METHODS

Experimental Design

This study included a sample size of 160 observations (N=160), which were processed as inputs into two data sets. The study applied four conditions to the extrapolated data, which included controls (without R5 inhibitors) with three additional R5 inhibitory conditions (Competitive, Non-competitive, and Uncompetitive). A data set contained the molarities of the substrates, such as the gp120 (gp120S), and the second data set contained the velocities (uM/min-1) of the R5 (R5E), acting as the enzymatic active site for gp120S. Michaelis-Menten Enzyme Kinetic calculations were performed to derive the inputs of each data set using the molarities of R5E, gp120S, the reaction rate constants, the Km, the Ki,and the Vmax for each reaction.

Data Extraction and Extrapolation

The known protein sizes of R5 and 120, which are 40.6 kDa and 120 KDa, respectively, were used for deriving their molarities. The R5 protein acted as the enzyme, and gp120 was used as the substrate for performing the Michaelis-Menten Enzyme Kinetic analysis. The R5E and gp120S molarities were estimated according to the number of R5 molecules required for 1 T cell infection with HIV, which is 10,000 molecules of R5. The molarities of R5 (R5E) and gp120 (gp120S) were approximated close to a range of 1B to 1T molecules per T cell, which is indicative of a high viral load. Four conditions were applied to each set of data extrapolated including, without (control) an R5 inhibitor (I), with a competitive inhibitor (CI), with an allosteric non-competitive inhibitor (NI), and with an uncompetitive inhibitor (UI).  The initial molarities of data extrapolated for R5 (E) and 120 (S) substrates in the enzymatic reactions (N=200) were 30 nM and 20 nM, respectively.

Michaelis-Menten Enzyme Kinetics

The molarities of R5E, gp120S, and of the inhibitors (CI, NI, and UI), were used to calculate the Km and Ki for each enzymatic reaction. The kcat or the constant rate of the catalytic reactions (60 minutes) with the molarities of R5E, gp120S, and the inhibitors, was used to compute the Vmax for each reaction. The Michaelis-Menten equations (1-4) were used to calculate the velocities (µM/s) for the enzymatic reactions of the four conditions of no inhibitor, competitive, non-competitive, and uncompetitive inhibitors. Michaelis-Menten graphs were generated using Excel for each of the four conditions. The concentrations of the inhibitors, for the Michaelis Menten kinetic computations, were 200 μM, 500 μM, 2000 μM, and 10,000 μM. The molarities of the gp120S substrates (nM) were plotted against the velocities (μM/min) for each predictive reaction between the R5E, 120S, and the inhibitors (CI, NI, and UI).

Statistical Analysis

Descriptive statistics and unpaired t-tests were performed, using GraphPad prism, to statistically analyze all data sets of the Michaelis-Menten enzyme kinetic computations.  The p-values were calculated with a statistical significance of P<0.05. A non-linear regression model, performed using Excel, of the enzyme kinetic data, was used to predict which type of anti-R5 mAb inhibitor (CI, NI, or UI) would more effectively block the interaction between R5 and gp120.

RESULTS AND DISCUSSION

Data were extracted and extrapolated from the known molecular weights and molarities of CCR5 and the gp120 envelope protein. The reaction rate constants were extrapolated from the known rates of time for the gp120 attachment to CCR5. Using the known molarities and distributions of both R5 and gp120 on a T cell, Michaelis-Menten enzyme kinetics was performed to find the initial velocities and the predicted subsequent velocities. The CCR5 was labeled as the enzyme (R5E) and the gp120 viral envelope protein was typed as the substrate (gp120S) while the anti-R5 mAb inhibitors were classified as either competitive (CI), non-competitive (NI), or uncompetitive inhibitors (UI). The Michaelis-Menten enzyme kinetic data extrapolated was then analyzed and used as a predictive model via non-linear regressions, in which the Vmax for the control was greater than 1 billion μMmin^-1. The competitive mAb inhibitor lowered the Vmax of the control to 2E+7 μmin^-1, and the uncompetitive inhibitor lowered the Vmax to 2 million μMmin^-1. However, the non-competitive mAb inhibitor showed the most significant effectiveness as an R5 inhibitor by lowering the Vmax to 20 μMmin^-1 (Figures 2a-5a). The Km for the control, CI, NI, and UI were 9.5 nM, 5 nM, 7 nM, and 8 nM, respectively (Figures 2a-5a).

At 200 μM of the inhibitors, the enzyme kinetic data was found to have p-values of P=0.047 for CI, P=0.046972 for NI, and P=0.046978 for the UI, using a one-tail T-test (Figure 2b). At 500 μM of the inhibitors, the Enzyme Kinetic data was found to have p-values of P=0.049 for CI, P=0.04697 for NI, and P=0.04698 for the UI (Figure 3b). At 2000 μM of the inhibitors, the enzyme kinetic data was found to have P-values of P=0.051 for CI, P=0.046970 for NI, and P=0.047 for the UI (Figure 4b). At 10000 μM of the inhibitors, the enzyme kinetic data was found to have p-values of P=0.0786 for CI, P=0.04697 for NI, and P=0.0471 for the UI (Figure 5b). Overall, a non-competitive mAb inhibitor of CCR5 was confirmed and forecasted by the predictive model, using non-linear regressions, to be more effective for lowering the reaction rate of R5 at a P<0.05, requiring a lesser potent dosage of a mAb as a non-competitive and allosteric inhibitor.