Novel method for probing the specificity binding profile of ligands: applications to HIV protease

Abstract

A detailed understanding of factors influencing the binding specificity of a ligand to a set of desirable targets and undesirable decoys is a key step in the design of potent and selective therapeutics. We have developed a general method for optimizing binding specificity in ligand-receptor complexes based on the theory of electrostatic charge optimization. This methodology can be used to tune the binding of a ligand to a panel of potential targets and decoys, along the continuum from narrow binding to only one partner to broad binding to the entire panel. Using HIV-1 protease as a model system, we probe specificity in three distinct ways. First, we probe interactions that could make the promiscuous protease inhibitor pepstatin more selective toward HIV-1 protease. Next, we study clinically approved HIV-1 protease inhibitors and probe ways to broaden the binding profiles toward both wild-type HIV-1 protease and drug-resistant mutants. Finally, we study a conformational ensemble of wild-type HIV-1 protease to 'design in' broad specificity to known drugs before resistance mutations arise. The results from this conformational ensemble were similar to those from the drug-resistant ensemble, suggesting the use of a conformational wild-type ensemble as a tool to develop escape-mutant-resistant inhibitors.

Figure 1

HIV-1 protease in complex with pepstatin. Top: Ribbon representation of HIV-1 protease colored by chain, pepstatin shown in van der Waals spheres, and the catalytic aspartates shown in licorice. Bottom: LigPlot [78] representation of the HIV protease active site. Hydrogen bonds shown with green dotted lines and van der Waals contacts are shown by red hatched circles. The following two figures show the decoys used in this study, human pepsin and human cathepsin D.

Figure 2

Human pepsin complexed with pepstatin. Refer to Figure 1 for description.

Figure 3

Human cathepsin D complexed with pepstatin. Refer to Figure 1 for description.

Figure 4

Narrow specificity-optimized charges of pepstatin. Top: Pepstatin atom names used in this study. Middle: Starting pepstatin charges are shown in black. Bottom: Optimized charges are shown with coloring such that red denotes charges that became more negative by at least a value l, where l=0.2e+0.2|q| and q is the initial charge of that atom. Blue charges are those that became more positive by at least l and green color represents atoms that changed by less than the tolerance l. The following figures follow this same convention.

Figure 5

Broad specificity-optimized charges for amprenavir binding to a target ensemble comprised of three classes: wild-type, V82A mutant (1X), and I63P/V82T/I84V mutant (3X). Colors are as described in Figure 4. The net specificity gain is computed to be 9.3 kcal/mol for this charge distribution.

Figure 6

Broad specificity-optimized charges for indinavir binding to a target ensemble comprised of three classes: wild-type, V82A mutant (1X), and I63P/V82T/I84V mutant (3X). Colors are as described in Figure 4. The net computed specificity gain is computed to be 11.8 kcal/mol for this charge distribution.

Figure 7

Broad specificity-optimized charges for nelfinavir binding to a target ensemble comprised of three classes: wild-type, V82A mutant (1X), and I63P/V82T/I84V mutant (3X). Colors are as described in Figure 4. The net specificity gain is computed to be 8.7 kcal/mol for this charge distribution.

Figure 8

Broad specificity-optimized charges for ritonavir binding to a target ensemble comprised of three classes: wild-type, V82A mutant (1X), and I63P/V82T/I84V mutant (3X). Colors are as described in Figure 4. The net specificity gain is computed to be 17.0 kcal/mol for this charge distribution.

Figure 9

Broad specificity-optimized charges for saquinavir binding to a target ensemble comprised of three classes: wild-type, V82A mutant (1X), and I63P/V82T/I84V mutant (3X). Colors are as described in Figure 4. The net specificity gain is computed to be 15.1 kcal/mol for this charge distribution.

Figure 10

Broad specificity-optimized charges for tipranavir binding to a target ensemble comprised of three classes: wild-type, V82A mutant (1X), and I63P/V82T/I84V mutant (3X). Colors are as described in Figure 4. The net specificity gain is computed to be 7.6 kcal/mol for this charge distribution.

Figure 11

Broad specificity-optimized charges for tipranavir binding to a target ensemble comprised of six classes, each containing different conformational states of the wild-type HIV protease. The optimized charge distribution is similar to the explicit mutant charge distribution in (Figure 10).