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Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation
Published on: May 20, 2020
Towards predictive inhibitor design for the EGFR autophosphorylation activity
1Life Science Division, Korea Institute of Science and Technology, P.O. Box 131, Cheongryang, Seoul 130-650, South Korea. amor.san_juan@up.edu.ph
Abstract:
Inhibition of the epidermal growth factor receptor (EGFR) tyrosine kinase is one among the pivotal targets for the treatment of cancer. The structural investigation directly halting the EGFR autophosphorylation is expected to give insights into alternatively blocking the aberrant activity of EGFR. The three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed from the systematic search conformer-based alignment method. Models derived from the training set of 95 compounds showed superior CoMFA as compared with CoMSIA (CoMFA: q(2)=0.50, r(2)=0.74, N=5, F=48.83, r(2)(pred)=0.56 while CoMSIA: q(2)=0.48, r(2)=0.62, N=2, F=72.70, r(2)(pred)=0.51). Validation of the models by test set prediction of 26 compounds was in good agreement with the experimental results. Further validation by molecular docking superimposition into the 3D-QSAR contour maps was found in agreement with each other. We identified that the structural modification of compound 19 by attachment of a bulky group on pyrrole ring along with an electronegative group on quinazoline ring and a hydrogen-bond donor on methyl formate opens a new avenue towards the optimization of novel chemical entities to develop potent inhibitors for EGFR autophosphorylation.
Insights
This study developed 3D-QSAR models to inhibit epidermal growth factor receptor (EGFR) tyrosine kinase, a key cancer target. Structural modifications suggest new strategies for designing potent EGFR autophosphorylation inhibitors.
Area of Science:
- Medicinal Chemistry
- Computational Drug Design
- Oncology
Background:
- Epidermal growth factor receptor (EGFR) tyrosine kinase inhibition is crucial for cancer treatment.
- Understanding EGFR autophosphorylation is key to blocking aberrant signaling.
- Targeting EGFR offers a significant therapeutic strategy in oncology.
Purpose of the Study:
- To develop and validate three-dimensional quantitative structure-activity relationship (3D-QSAR) models for EGFR inhibitors.
- To gain structural insights into the inhibition of EGFR autophosphorylation.
- To guide the optimization of novel chemical entities targeting EGFR.
Main Methods:
- Systematic search conformer-based alignment for 3D-QSAR model development.
- Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were employed.
- Molecular docking and experimental validation were used to assess model accuracy.
Main Results:
- Developed robust 3D-QSAR models with superior performance for CoMFA (q(2)=0.50, r(2)=0.74) over CoMSIA (q(2)=0.48, r(2)=0.62).
- Models demonstrated good predictive power for a test set of 26 compounds.
- Molecular docking results corroborated the 3D-QSAR contour maps, confirming structural insights.
Conclusions:
- Structural modifications, including adding bulky and electronegative groups and hydrogen-bond donors, are proposed for compound optimization.
- These findings provide a new direction for designing potent EGFR autophosphorylation inhibitors.
- The developed models serve as a valuable tool for future drug discovery efforts targeting EGFR.
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