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When Does Chemical Elaboration Induce a Ligand To Change Its Binding Mode?
Shipra Malhotra1, John Karanicolas1
1Program in Molecular Therapeutics, Fox Chase Cancer Center , 333 Cottman Avenue, Philadelphia, Pennsylvania 19111, United States.
Journal of Medicinal Chemistry
|December 17, 2016
Summary
Ligand binding modes can shift during drug development. A study found 14% of related ligand pairs changed binding poses, highlighting the need for structure-based modeling in hit-to-lead optimization.
Area of Science:
- Medicinal Chemistry
- Structural Biology
- Computational Chemistry
Background:
- Drug discovery often assumes ligands maintain their binding pose as they are optimized.
- Understanding ligand-receptor interactions is crucial for developing effective therapeutics.
Purpose of the Study:
- To investigate the frequency and drivers of binding mode changes during ligand elaboration.
- To evaluate the utility of structure-based modeling in predicting these changes.
Main Methods:
- Compilation of a large dataset of related ligand pairs bound to the same protein.
- Analysis of structural data to identify shifts in ligand binding modes.
- Correlation of binding mode changes with ligand physiochemical properties and computational modeling predictions.
Main Results:
- 14% (41 of 297) of ligand pairs exhibited altered binding modes upon structural elaboration.
- Specific physiochemical properties, characteristic of fragments, were associated with binding mode shifts.
- Structure-based modeling was more effective than physiochemical properties in identifying substitutions that alter binding modes.
- Observed changes were attributed to steric clashes or the emergence of new favorable interactions in alternative poses.
Conclusions:
- Ligand binding modes are not always conserved during hit-to-lead optimization.
- Structure-based modeling is a valuable tool for predicting and guiding optimization strategies that involve binding mode alterations.
- Scaffolds capable of adopting multiple binding poses offer opportunities for divergent drug optimization pathways.
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