Coupled atomic charge selectivity for optimal ligand‐charge distributions at protein binding sites

Abstract

Charge optimization as a tool for both analyzing and enhancing binding electrostatics has become an attractive approach over the past few years. An interesting feature of this method for molecular design is that it provides not only the optimal charge magnitudes, but also the selectivity of a particular atomic center for its optimal charge. The current approach to compute the charge selectivity at a given atomic center of a ligand in a particular binding process is based on the binding-energy cost incurred upon the perturbation of the optimal charge distribution by a unit charge at the given atomic center, while keeping the other atomic partial charges at their optimal values. A limitation of this method is that it does not take into account the possible concerted changes in the other atomic charges that may incur a lower energetic cost than perturbing a single charge. Here, we describe a novel approach for characterizing charge selectivity in a concerted manner, taking into account the coupling between the ligand charge centers in the binding process. We apply this novel charge selectivity measure to the celecoxib molecule, a nonsteroidal anti-inflammatory agent binding to cyclooxygenase 2 (COX2), which has been recently shown to also exhibit cross-reactivity toward carbonic anhydrase II (CAII), to which it binds with nanomolar affinity. The uncoupled and coupled charge selectivity profiles over the atomic centers of the celecoxib ligand, binding independently to COX2 and CAII, are analyzed comparatively and rationalized with respect to available experimental data. Very different charge selectivity profiles are obtained for the uncoupled versus coupled selectivity calculations.

Rational Approaches to Improving Selectivity in Drug Design

“…This concept was recently extended to predict a coupled charge selectivity (CSq), which is defined as the energetic cost of changing an atomic charge by one electron charge from its optimal value while allowing all other charges in the molecule to reoptimize. 69 The CSq method was applied to inhibitors of COX-2 such as celecoxib, which have nanomolar affinity for carbonic anhydrase II (CAII). The CSq analysis identified that the ionized sulfonamide group of celecoxib was well optimized to bind CAII and was highly charge selective whereas there was little charge selectivity of this group binding to COX2.…”

“…Experimentally, the binding affinity of P1-Met BPTI is 7.4 kcal/mol worse than P1-Lys BPTI, indicating the strong selectivity for a positively charged group in this site, in agreement with the charge optimization predictions. This concept was recently extended to predict a coupled charge selectivity (CSq), which is defined as the energetic cost of changing an atomic charge by one electron charge from its optimal value while allowing all other charges in the molecule to reoptimize . The CSq method was applied to inhibitors of COX-2 such as celecoxib, which have nanomolar affinity for carbonic anhydrase II (CAII).…”

Introductory Chapter: Some Quantitative Structure Activity Relationship Descriptor

“…They explained octanol-water partition coeicients of organic compounds with the atomic charges [16,21]. Bhat et al [22] reported optimal ligand-charge distribution at protein-binding sites with the help of atomic charge Highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) are very popular quantum chemical descriptors. The strongest Frontier orbitals (FO)…”

Charge Optimization Theory for Induced-Fit Ligands

“…3,4 Nonspherical geometries and alternative basis sets for ligand charge distributions were then addressed, 5,6 and the method was extended to exact molecular shapes. 7−12 A framework for optimizing electrostatic specificity has also been developed. 13,14 Calculation of the electrostatic potentials is generally done by solving the linearized Poisson–Boltzmann (LPB) equation, but other forms of linear response theory are applicable.…”

“…22 Other applications include E. coli glutaminyl-tRNA synthetase binding to its cognate substrates, 23 protein inhibitors of HIV-1 cell entry, 24 the interface between protein kinases and their ligands, 25 small-molecule influenza neuraminidase inhibitors, 26 and the celecoxib ligand binding independently to COX2 and CAII. 12 Recently, charge optimization and protein design together identified tighter binding peptides to HIV-1 protease that were studied experimentally. 27 Binding specificity optimization probed ligand binding in the model system of HIV protease, other proteases, and their inhibitors.…”