Optimal charges in lead progression: a structure-based neuraminidase case study.

Collective experience in structure-based lead progression has found electrostatic interactions to be more difficult to optimize than shape-based ones. A major reason for this is that the net electrostatic contribution observed includes a significant nonintuitive desolvation component in addition to the more intuitive intermolecular interaction component. To investigate whether knowledge of the ligand optimal charge distribution can facilitate more intuitive design of electrostatic interactions, we took a series of small-molecule influenza neuraminidase inhibitors with known protein cocrystal structures and calculated the difference between the optimal and actual charge distributions. This difference from the electrostatic optimum correlates with the calculated electrostatic contribution to binding (r(2) = 0.94) despite small changes in binding modes caused by chemical substitutions, suggesting that the optimal charge distribution is a useful design goal. Furthermore, detailed suggestions for chemical modification generated by this approach are in many cases consistent with observed improvements in binding affinity, and the method appears to be useful despite discrete chemical constraints. Taken together, these results suggest that charge optimization is useful in facilitating generation of compound ideas in lead optimization. Our results also provide insight into design of neuraminidase inhibitors.