Characterization of the Ligand Receptor Encounter Complex and Its Potential for in Silico Kinetics-Based Drug Development

Abstract

The study of drug-receptor interactions has largely been framed in terms of the equilibrium thermodynamic binding affinity, an in vitro measure of the stability of the drug-receptor complex that is commonly used as a proxy measure of in vivo biological activity. In response to the growing realization of the importance of binding kinetics to in vivo drug activity we present a computational methodology for the kinetic characterization of drug-receptor interactions in terms of the encounter complex. Using trajectory data from multiple Brownian dynamics simulations of ligand diffusion, we derive the spatial density of the ligand around the receptor and show how it can be quantitatively partitioned into different basins of attraction. Numerical integration of the ligand densities within the basins can be used to estimate the residence time of the ligand within these diffusive binding sites. Simulations of two structurally similar inhibitors of Hsp90 exhibit diffusive binding sites with similar spatial structure but with different ligand residence times. In contrast, a pair of structurally dissimilar inhibitors of MDM2, a peptide and a small molecule, exhibit spatially distinct basins of attraction around the receptor, which in turn reveal differences in ligand orientational order. Thus, our kinetic approach provides microscopic details of drug-receptor dynamics that provide novel insight into the observed differences in the thermodynamic binding affinities for the two inhibitors, such as the differences in the entropic contributions to binding. The characterization of the encounter complex, in terms of the structure, topology, and dynamics of diffusive binding sites, offers a new perspective on ligand-receptor interactions and the potential for greater insight into drug action. The method, which requires no prior knowledge of the bound state, is a first step toward the incorporation of ligand kinetics into in silico drug development protocols.

A spatiotemporal characterization of the effect of p53 phosphorylation on its interaction with MDM2

“…3; left). In previous work, 36,37 we showed that the interaction landscape of wild-type p53 with MDM2 ( Fig. 3a; left) is characterized by broad basins of attraction, with the 2 highest occupied basins, basins 1 and 2, lying close to the C-and N-terminal regions of the MDM2 protein surface respectively, away from the binding site, and a third basin of attraction, basin 3, lying further away from the MDM2 surface.…”

“…The encounter complex, a diffusively bound state that forms prior to any (nondiffusive, classical) bound complex, is characterized by regions of the proteinligand interaction landscape where associative interactions dominate over dissociative interactions. 36,[41][42][43][44][45][46] These regions of space, termed basins of attraction, correspond to molecular configurations whose lifetime (residence time) is prolonged relative to those in the bulk environment, but are not bound to the protein surface. 36 In the following sections, we use p53 to refer to a set of p53 peptides in their wild type and phosphorylated states and we use MDM2 to refer to the crystallized N-terminal domain of MDM2 (from residues 25-109).…”

“…In previous work, we showed that the residence time of any of the encounter complex basins can be computed as a time-weighted sum of the p53 peptide density in the volume enclosed by that basin. 36 Figure 4 shows the absolute residence time (t/ of each basin of attraction for wild-type and phosphorylated p53 peptides. It is clear that phosphorylation of Thr18 or Ser20 leads to a significant decrease in the residence time in all basins of attraction.…”

“…BD simulation details The details of the BD simulations along with the algorithm for the identification of the basins of attraction were described in a previous work 36 and we only describe it briefly here. The BD trajectories were propagated by solving the translational and the rotational diffusion equations, using the Ermak-McCammon algorithm 53 as implemented in the SDA package version 4.23 b.…”

“…[31][32][33][34][35][36] In previous work, [36][37][38] we showed, through Brownian dynamics analysis, that the residence time of the drug receptor encounter complex is an alternative and effective means for explaining the preferential binding of Nutlin and p53 to MDM2 36,37 and for understanding the dynamic determinants for the differential binding of Nutlin geometric isomers to MDM2. 38 In this work, we extend this approach to study the effect of p53-peptide phosphorylation on the dynamics of its interactions with the N-terminal domain of MDM2 (residues 25-109).…”