Charge optimization of the interface between protein kinases and their ligands

Abstract

Examining the potential for electrostatic complementarity between a ligand and a receptor is a useful technique for rational drug design, and can demonstrate how a system prioritizes interactions when allowed to optimize its charge distribution. In this computational study, we implemented the previously developed, continuum solvent-based charge optimization theory with a simple, quadratic programming algorithm and the UHBD Poisson-Boltzmann solver. This method allows one to compute the best set of point charges for a ligand or ligand region based on the ligand and receptor shape, and the receptor partial charges, by optimizing the binding free energy obtained from a continuum-solvent model. We applied charge optimization to a fragment of the heat-stable protein kinase inhibitor (PKI) of protein kinase A (PKA), to three flavopiridol inhibitors of CDK2, and to cyclin A which interacts with CDK2 to regulate the cell cycle. We found that a combination of global (involving every charge) and local (involving only charges in a local region) optimization can give useful hints for designing better inhibitors. Although some parts of an inhibitor may already contribute significantly to binding, we found that they could still be the most important targets for modifications to obtain stronger binders. In studying the binding of flavopiridol inhibitors to CDK2, comparable binding affinity could be obtained regardless of whether the net charges of the inhibitors were constrained to -2, -1, 0, 1, or 2 during the optimization. This provides flexibility in inhibitor design when a certain net charge of the inhibitor is desired in addition to strong binding affinity. For the study of the PKA-PKI and CDK2-cyclin A interfaces, we identified residues whose charge distributions are already close to optimal and those whose charge distributions could be refined to further improve binding.

Accurate solution of multi‐region continuum biomolecule electrostatic problems using the linearized Poisson–Boltzmann equation with curved boundary elements

“…One such example is charge optimization, 72,73,80,[105][106][107] which determines the optimal partial atomic charges for a ligand that minimize the electrostatic component of its binding free energy with a receptor molecule. In charge optimization, two geometries for the ligand are considered: the bound state, where it is complexed with the receptor molecule, and the unbound state, where it is isolated in solution.…”

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

“…6,7,11,14 When constraints are applied during charge optimization, a small number of partial charges q i (particularly among those with small A ii self-terms) are capped at constraint values corresponding to one of the endpoints of the interval of feasible partial charges, [−q max ; +q max ]. For these atomic centers, Sq i represents the energy cost incurred by altering their constrained optimal charge, q opt,c i , by 1e in the direction toward the inside of the interval.…”

“…[2][3][4][5][6][7] Using electrostatic charge optimization as a tool for both analyzing and enhancing binding electrostatics has become an attractive approach over the past few years. Charge optimization calculations have been applied to a number of natural complexes including barstar binding to barnnse, 6,8 glutaminyl-tRNA synthetase binding to its cognate substrates, 9 and the Arc repressor-DNA complex, 10 Charge optimization has also been carried out on the interfaces between protein kinases and small-molecule inhibitors, 11 showing that optimizing electrostatic complementarity between smallmolecule ligands and a given protein receptor can be a powerful technique for rational drug design, Similarly, the charge optimization method was used to analyze the electrostatic interaction between the active site of chorismate mutase and an endo-oxabicylic dicarboxylate transition state analog, 12 The suggested modification to the ligand was later verified experimentally, resulting in an improved inhibition of the enzyme. 13 One of the attractive facets of charge optimization calculations for molecular design is not only being able to obtain the optimal charge magnitudes, but also the selectivity of a particular atomic center for its optimal charge.…”

Charge Optimization Theory for Induced-Fit Ligands

“…Mandal and Hilvert synthesized the new compound and found, experimentally, a 1.7-kcal/mol improvement in binding affinity in a context corresponding to the calculational study, thus identifying the most potent known inhibitor of this enzyme. 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.…”