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Optimal charges in lead progression: a structure-based neuraminidase case study.
Kathryn A Armstrong1, Bruce Tidor, Alan C Cheng
1Biological Engineering Division, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Journal of Medicinal Chemistry
|April 14, 2006
Summary
Optimizing electrostatic interactions in drug design is challenging due to desolvation effects. This study shows that aligning a ligand's charge distribution with its optimal electrostatic state improves binding affinity for neuraminidase inhibitors.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Drug Design
Background:
- Optimizing electrostatic interactions in structure-based drug design is complex.
- The net electrostatic contribution is influenced by nonintuitive desolvation effects.
- Current methods struggle with intuitive design of electrostatic interactions.
Purpose of the Study:
- To investigate if knowledge of a ligand's optimal charge distribution can aid in designing electrostatic interactions.
- To assess the utility of charge distribution optimization as a design goal for drug leads.
Main Methods:
- Calculated the difference between optimal and actual charge distributions for small-molecule influenza neuraminidase inhibitors.
- Utilized known protein-ligand cocrystal structures.
- Correlated charge distribution differences with calculated electrostatic binding contributions.
Main Results:
- A strong correlation (r² = 0.94) was observed between the difference from the electrostatic optimum and the calculated electrostatic binding contribution.
- The optimal charge distribution proved to be a useful design goal, even with minor binding mode changes.
- Generated chemical modification suggestions aligned with observed binding affinity improvements.
Conclusions:
- Charge optimization is a valuable strategy for generating compound ideas during lead optimization.
- This approach provides insights into the design of neuraminidase inhibitors.
- The method demonstrates utility despite discrete chemical constraints.