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Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation
Published on: May 20, 2020
Investigating mutation-specific biological activities of small molecules using quantitative structure-activity
P Anoosha1, R Sakthivel1, M Michael Gromiha1
1Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India.
Abstract:
Epidermal Growth Factor Receptor (EGFR) is a potential drug target in cancer therapy. Missense mutations play major roles in influencing the protein function, leading to abnormal cell proliferation and tumorigenesis. A number of EGFR inhibitor molecules targeting ATP binding domain were developed for the past two decades. Unfortunately, they become inactive due to resistance caused by new mutations in patients, and previous studies have also reported noticeable differences in inhibitor binding to distinct known driver mutants as well. Hence, there is a high demand for identification of EGFR mutation-specific inhibitors. In our present study, we derived a set of anti-cancer compounds with biological activities against eight typical EGFR known driver mutations and developed quantitative structure-activity relationship (QSAR) models for each separately. The compounds are grouped based on their functional scaffolds, which enhanced the correlation between compound features and respective biological activities. The models for different mutants performed well with a correlation coefficient, (r) in the range of 0.72-0.91 on jack-knife test. Further, we analyzed the selected features in different models and observed that hydrogen bond and aromaticity-related features play important roles in predicting the biological activity of a compound. This analysis is complimented with docking studies, which showed the binding patterns and interactions of ligands with EGFR mutants that could influence their activities.
Insights
Developing novel Epidermal Growth Factor Receptor (EGFR) mutation-specific inhibitors is crucial for cancer therapy. This study presents quantitative structure-activity relationship (QSAR) models for eight common EGFR mutations, identifying key compound features for improved drug design.
Area of Science:
- Oncology
- Medicinal Chemistry
- Computational Biology
Background:
- Epidermal Growth Factor Receptor (EGFR) is a key target in cancer therapy, but drug resistance arises from mutations.
- Existing EGFR inhibitors face challenges due to acquired resistance and varying efficacy against different mutants.
- There is a critical need for developing mutation-specific inhibitors to overcome resistance and improve treatment outcomes.
Purpose of the Study:
- To identify novel anti-cancer compounds targeting specific Epidermal Growth Factor Receptor (EGFR) mutations.
- To develop robust quantitative structure-activity relationship (QSAR) models for predicting the activity of compounds against eight common EGFR driver mutations.
- To elucidate the structural features that contribute to the efficacy of inhibitors against distinct EGFR mutants.
Main Methods:
- Derivation of anti-cancer compounds with biological activity against eight typical EGFR known driver mutations.
- Development of separate quantitative structure-activity relationship (QSAR) models for each EGFR mutant.
- Analysis of compound features, including functional scaffolds, hydrogen bonding, and aromaticity, using QSAR models.
- Complementary molecular docking studies to visualize ligand-protein interactions with EGFR mutants.
Main Results:
- Quantitative structure-activity relationship (QSAR) models demonstrated strong predictive performance with correlation coefficients (r) ranging from 0.72 to 0.91 in jack-knife tests.
- Grouping compounds by functional scaffolds improved the correlation between compound features and biological activities.
- Key features influencing biological activity were identified as hydrogen bonding and aromaticity.
- Docking studies provided insights into the binding patterns and interactions of ligands with EGFR mutants.
Conclusions:
- The developed QSAR models are effective in predicting the activity of anti-cancer compounds against specific EGFR mutations.
- Understanding the role of structural features like hydrogen bonding and aromaticity can guide the design of more potent and specific EGFR inhibitors.
- This research contributes to the development of next-generation EGFR-targeted cancer therapies by addressing drug resistance mechanisms.
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