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Feature-similarity protein classifier as a ligand engineering tool.
Sridhar Maddipati1, Ariel Fernández
1School of Chemical Engineering, Purdue University, West Lafayette, IN 47906, USA.
Biomolecular Engineering
|November 18, 2006
Summary
Sticky packing defects in protein kinases offer a novel target for designing selective drugs. By clustering kinases based on packing differences, researchers can identify unique binding sites, enhancing drug specificity and reducing cross-reactivity in signaling pathway therapies.
Area of Science:
- Biochemistry
- Pharmacology
- Structural Biology
Background:
- Kinase inhibitors are crucial for blocking signaling pathways in drug therapy.
- Protein structure conservation across kinase homologs often results in undesirable cross-reactivity.
- Ligand-anchoring sites, such as sticky packing defects, are typically not conserved and can enhance drug selectivity.
Purpose of the Study:
- To introduce a novel strategy for designing selective kinase inhibitors.
- To partition the kinome based on packing differences for improved drug targeting.
- To investigate the relationship between packing sensitivity and drug promiscuity.
Main Methods:
- Hierarchical clustering of protein data bank (PDB)-reported kinases based on packing differences.
- Correlation analysis between kinome partitioning and pharmacological profiling.
- Assessment of drug sensitivity to packing variations.
Main Results:
- Kinome partitioning based on packing defects shows high correlation with pharmacological proximity.
- A variable packing sensitivity was observed for different drugs.
- Highly promiscuous ligands exhibited the lowest sensitivity to packing differences.
Conclusions:
- Sticky packing defects represent promising, non-conserved targets for enhancing kinase inhibitor selectivity.
- The developed classifier provides a strategic framework for designing more specific kinase-targeted therapies.
- Understanding packing variations is key to overcoming cross-reactivity issues in kinase drug discovery.
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