Electrostatic Influence on the Kinetics of Ligand Binding to Acetylcholinesterase

Abstract

To explore the role that surface and active center charges play in electrostatic attraction of ligands to the active center gorge of acetylcholinesterase (AChE), and the influence of charge on the reactive orientation of the ligand, we have studied the kinetics of association of cationic and neutral ligands with the active center and peripheral site of AChE. Electrostatic influences were reduced by sequential mutations of six surface anionic residues outside of the active center gorge (Glu-84, Glu-91, Asp-280, Asp-283, Glu-292, and Asp-372) and three residues within the active center gorge (Asp-74 at the rim and Glu-202 and Glu-450 at the base). The peripheral site ligand, fasciculin 2 (FAS2), a peptide of 6.5 kDa with a net charge of ؉4, shows a marked enhancement of rate of association with reduction in ionic strength, and this ionic strength dependence can be markedly reduced by progressive neutralization of surface and active center gorge anionic residues. By contrast, neutralization of surface residues only has a modest influence on the rate of cationic m-trimethylammoniotrifluoroacetophenone (TFK ؉ ) association with the active serine, whereas neutralization of residues in the active center gorge has a marked influence on the rate but with little change in the ionic strength dependence. Brownian dynamics calculations for approach of a small cationic ligand to the entrance of the gorge show the influence of individual charges to be in quantitative accord with that found for the surface residues. Anionic residues in the gorge may help to orient the ligand for reaction or to trap the ligand. Bound FAS2 on AChE not only reduces the rate of TFK ؉ reaction with the active center but inverts the ionic strength dependence for the cationic TFK ؉ association with AChE. Hence it appears that TFK ؉ must traverse an electrostatic barrier at the gorge entry imparted by the bound FAS2 with its net charge of ؉4.

Directed Ligand Passage over the Surface of Diffusion-Controlled Enzymes: A Cellular Automata Model

“…The directed passage of ligand toward the active site, as suggested by some of these models, enhances the rate of diffusion-controlled reactions even further. Some models suggest that the electrostatic field experienced by the ligand on the surface of protein may guide it toward the active site [14][15]. Some other models present the van der Waals interactions as the prominent force directing the ligand over the protein surfaces [10].…”

Computational Methods for Biomolecular Electrostatics

“…For example, by using Brownian dynamics (BD) simulations (Ermak and McCammon, 1978;Northrup et al, 1984) (Sec. IV.F) to calculate diffusional association rates, Gabdoulline and Wade demonstrated that, for fast-associating protein pairs, electrostatic interactions enhance association and are the dominant forces determining the rate of diffusional association (Radic et al, 1997;Gabdoulline and Wade, 2001). Using related methods, Sept et al demonstrated the role of electrostatic interactions in determining the rates and polarity of actin polymerization (Sept et al, 1999;Sept and McCammon, 2001).…”

“…The binding affinities, from an electrostatic point of view, are determined by balance of these two energetic contributions (Xu et al, 1997;Lee and Tidor, 2001;Sheinerman and Honig, 2002;Russell et al, 2004;del Álamo and Mateu, 2005). Systematic studies of protein pairs, such as barnase and barstar Fersht, 1993, 1995;Frisch et al, 1997;Dong et al, 2003), and fasciculin-2 (Radic et al, 1997), as well as protein kinase A and balanol (Wong et al, 2001), have shown that charged and polar residues at the protein-protein interfaces play important roles in binding energetics. Similarly, Sept et al have demonstrated an important role for electrostatics in determining microtubule structure and stability (Sept et al, 2003).…”

“…Given the importance of electrostatic interactions in biomolecular association, BD simulations are usually combined with continuum electrostatic calculations to provide the most accurate estimates of diffusion-limited encounter rates (Allison and McCammon, 1985;Davis and McCammon, 1990a;Ilin et al, 1995;Madura et al, 1995;Sept et al, 1999;Wade, 2001, 2002). Such calculations have been used in numerous diffusional encounter rate calculations, including simulations of small molecule interactions with enzymes (Allison and McCammon, 1985;Madura and McCammon, 1989;Davis et al, 1991;Sines et al, 1992;Luty et al, 1993;Tan et al, 1993;Elcock et al, 1996;Radic et al, 1997;Tara et al, 1998), simulations of protein-protein encounter (Gabdoulline and Wade, 1997;Elcock et al, 1999;Sept et al, 1999;Elcock et al, 2001;Gabdoulline and Wade, 2001;Spaar et al, 2006), as well as functional assessment of differences in protein electrostatics (Livesay et al, 2003).…”

Prediction of Salt and Mutational Effects on the Association Rate of U1A Protein and U1 Small Nuclear RNA Stem/Loop II

“…The disparate salt effects on k a and k d are observed on many protein-protein pairs. [33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] The binding of U1A with U1SLII shows exactly this salt behavior 7,14 and is thus expected to be modeled by a transient complex close to the native complex.…”