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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
Published on: July 8, 2025
Ligand-based autotaxin pharmacophore models reflect structure-based docking results
Catrina D Mize1, Ashley M Abbott, Samantha B Gacasan
1Department of Chemistry, The University of Memphis, Memphis, TN 38152, USA.
Journal of Molecular Graphics & Modelling
|October 5, 2011
Summary
Developing effective autotaxin (ATX) inhibitors is crucial for treating diseases like cancer and atherosclerosis. Pharmacophore models, when aligned with docking results, significantly improve the identification of potent ATX inhibitors.
Area of Science:
- Biochemistry and Medicinal Chemistry
- Enzyme Inhibition and Drug Discovery
Background:
- Autotaxin (ATX) is an enzyme with lysophospholipase D activity, converting lysophosphatidyl choline to lysophosphatidic acid (LPA).
- Both ATX and LPA are implicated in the pathogenesis of atherosclerosis, cancer invasiveness, and neuropathic pain.
- Targeting ATX presents a promising therapeutic strategy with significant research interest in developing novel inhibitors.
Purpose of the Study:
- To compare the performance of ligand-based pharmacophores for autotaxin (ATX) inhibitor discovery.
- To evaluate pharmacophore models against a compound database and docking results using a recent ATX crystal structure.
- To identify optimal pharmacophore modeling strategies for identifying potent ATX inhibitors.
Main Methods:
- Development of ligand-based pharmacophores using diverse sets of ATX inhibitors.
- Performance evaluation of pharmacophore models against a large database of compounds with known ATX inhibitory activity.
- Comparison of ligand-based pharmacophore models with molecular docking simulations against the ATX crystal structure.
Main Results:
- Pharmacophore models demonstrated superior performance when ligand-based superposition aligned well with docking-based superposition of active inhibitors.
- Two specific pharmacophore models, integrating competitive inhibitors and the sole crystallized inhibitor, achieved over 40% identification rate of active ATX inhibitors.
- This represents a substantial improvement compared to the <10% rate for active site-directed inhibitors in the test database.
Conclusions:
- Ligand-based pharmacophore models are effective tools for identifying autotaxin (ATX) inhibitors.
- Integrating docking results with pharmacophore modeling enhances the accuracy and efficiency of inhibitor discovery.
- Optimized pharmacophore strategies offer a significant advancement in the development of therapeutic ATX inhibitors.
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