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Updated: Apr 14, 2026
Deciphering the Structural Effects of Activating EGFR Somatic Mutations with Molecular Dynamics Simulation
Published on: May 20, 2020
EGFR Mutant Structural Database: computationally predicted 3D structures and the corresponding binding free energies
Lichun Ma1, Debby D Wang2, Yiqing Huang3
1Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong. lichunma2-c@my.cityu.edu.hk.
Background:
Epidermal growth factor receptor (EGFR) mutation-induced drug resistance has caused great difficulties in the treatment of non-small-cell lung cancer (NSCLC). However, structural information is available for just a few EGFR mutants. In this study, we created an EGFR Mutant Structural Database (freely available at http://bcc.ee.cityu.edu.hk/data/EGFR.html ), including the 3D EGFR mutant structures and their corresponding binding free energies with two commonly used inhibitors (gefitinib and erlotinib).
Results:
We collected the information of 942 NSCLC patients belonging to 112 mutation types. These mutation types are divided into five groups (insertion, deletion, duplication, modification and substitution), and substitution accounts for 61.61% of the mutation types and 54.14% of all the patients. Among all the 942 patients, 388 cases experienced a mutation at residue site 858 with leucine replaced by arginine (L858R), making it the most common mutation type. Moreover, 36 (32.14%) mutation types occur at exon 19, and 419 (44.48%) patients carried a mutation at exon 21. In this study, we predicted the EGFR mutant structures using Rosetta with the collected mutation types. In addition, Amber was employed to refine the structures followed by calculating the binding free energies of mutant-drug complexes.
Conclusions:
The EGFR Mutant Structural Database provides resources of 3D structures and the binding affinity with inhibitors, which can be used by other researchers to study NSCLC further and by medical doctors as reference for NSCLC treatment.
Insights
A new database provides 3D structures and binding energies for epidermal growth factor receptor (EGFR) mutants, aiding non-small-cell lung cancer (NSCLC) treatment research and drug development.
Area of Science:
- Biochemistry
- Structural Biology
- Computational Biology
Background:
- Drug resistance in non-small-cell lung cancer (NSCLC) is a significant challenge, often driven by epidermal growth factor receptor (EGFR) mutations.
- Limited structural data exists for many EGFR mutants, hindering the development of targeted therapies.
- An EGFR Mutant Structural Database was developed to address this gap.
Purpose of the Study:
- To create a comprehensive database of 3D structures for EGFR mutants.
- To calculate binding free energies of EGFR mutants with common inhibitors.
- To provide a resource for further NSCLC research and clinical treatment strategies.
Main Methods:
- Collected data from 942 NSCLC patients across 112 mutation types.
- Predicted 3D EGFR mutant structures using Rosetta.
- Refined structures and calculated binding free energies using Amber.
Main Results:
- The database includes 112 mutation types, with substitutions being the most common (61.61%).
- The L858R mutation at residue site 858 was the most frequent (388 cases).
- Mutations were frequently observed in exon 19 (36 types) and exon 21 (44.48% of patients).
Conclusions:
- The EGFR Mutant Structural Database offers valuable 3D structural and binding affinity data.
- This resource can advance research into NSCLC mechanisms and drug resistance.
- It serves as a reference for clinicians in developing personalized NSCLC treatment plans.
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