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Identifying EGFR mutation-induced drug resistance based on alpha shape model analysis of the dynamics

View Article: PubMed Central - PubMed

ABSTRACT

Background: Epidermal growth factor receptor (EGFR) mutation-induced drug resistance is a difficult problem in lung cancer treatment. Studying the molecular mechanisms of drug resistance can help to develop corresponding treatment strategies and benefit new drug design.

Methods: In this study, Rosetta was employed to model the EGFR mutant structures. Then Amber was carried out to conduct molecular dynamics (MD) simulation. Afterwards, we used Computational Geometry Algorithms Library (CGAL) to compute the alpha shape model of the mutants.

Results: We analyzed the EGFR mutation-induced drug resistance based on the motion trajectories obtained from MD simulation. We computed alpha shape model of all the trajectory frames for each mutation type. Solid angle was used to characterize the curvature of the atoms at the drug binding site. We measured the knob level of the drug binding pocket of each mutant from two ways and analyzed its relationship with the drug response level. Results show that 90 % of the mutants can be grouped correctly by setting a certain knob level threshold.

Conclusions: There is a strong correlation between the geometric properties of the drug binding pocket of the EGFR mutants and the corresponding drug responses, which can be used to predict the response of a new EGFR mutant to a drug molecule.

No MeSH data available.


a and b show the comparison of the crystal structures of WT EGFR and the mutant delE746_A750insAP. c and d are the alpha shape models of the drug binding pocket of (a) and (b), respectively. The original site is colored blue while the corresponding mutant site is shown in magenta. The drug molecule (gefitinib) is displayed in purple
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Fig1: a and b show the comparison of the crystal structures of WT EGFR and the mutant delE746_A750insAP. c and d are the alpha shape models of the drug binding pocket of (a) and (b), respectively. The original site is colored blue while the corresponding mutant site is shown in magenta. The drug molecule (gefitinib) is displayed in purple

Mentions: Only several EGFR mutant crystal structures are available from the Protein Data Bank (PDB) [41], due to the cost and complexity in structure determination by experiments. We adopted the released mutant structures from PDB, such as L858R (PDB: 2ITZ), while modeled most of them using Rosetta [36]. We carried out Rosetta high-resolution ddg_monomer (HRDM) protocol and comparative modeling (CM) protocol to generate the EGFR mutants based on the crystal structure of WT EGFR (PDB: 2ITY) [42, 43]. Rosetta ddg_monomer was applied to predict the point mutation, such as L861Q and G719C_S768I. Other mutation types such as delE746_A750 (deletion), dulN771_H773 (duplication) and delE746_A750insAP (modification) were modeled by using the CM protocol. We further refined the predicted mutant structures using Amber [37], where 1000 steps of minimization were conducted to optimize the structures. FiguresĀ 1a and b show the comparison of the WT EGFR and the mutant delE746_A750insAP modeled using Rosetta.Fig. 1


Identifying EGFR mutation-induced drug resistance based on alpha shape model analysis of the dynamics
a and b show the comparison of the crystal structures of WT EGFR and the mutant delE746_A750insAP. c and d are the alpha shape models of the drug binding pocket of (a) and (b), respectively. The original site is colored blue while the corresponding mutant site is shown in magenta. The drug molecule (gefitinib) is displayed in purple
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5015241&req=5

Fig1: a and b show the comparison of the crystal structures of WT EGFR and the mutant delE746_A750insAP. c and d are the alpha shape models of the drug binding pocket of (a) and (b), respectively. The original site is colored blue while the corresponding mutant site is shown in magenta. The drug molecule (gefitinib) is displayed in purple
Mentions: Only several EGFR mutant crystal structures are available from the Protein Data Bank (PDB) [41], due to the cost and complexity in structure determination by experiments. We adopted the released mutant structures from PDB, such as L858R (PDB: 2ITZ), while modeled most of them using Rosetta [36]. We carried out Rosetta high-resolution ddg_monomer (HRDM) protocol and comparative modeling (CM) protocol to generate the EGFR mutants based on the crystal structure of WT EGFR (PDB: 2ITY) [42, 43]. Rosetta ddg_monomer was applied to predict the point mutation, such as L861Q and G719C_S768I. Other mutation types such as delE746_A750 (deletion), dulN771_H773 (duplication) and delE746_A750insAP (modification) were modeled by using the CM protocol. We further refined the predicted mutant structures using Amber [37], where 1000 steps of minimization were conducted to optimize the structures. FiguresĀ 1a and b show the comparison of the WT EGFR and the mutant delE746_A750insAP modeled using Rosetta.Fig. 1

View Article: PubMed Central - PubMed

ABSTRACT

Background: Epidermal growth factor receptor (EGFR) mutation-induced drug resistance is a difficult problem in lung cancer treatment. Studying the molecular mechanisms of drug resistance can help to develop corresponding treatment strategies and benefit new drug design.

Methods: In this study, Rosetta was employed to model the EGFR mutant structures. Then Amber was carried out to conduct molecular dynamics (MD) simulation. Afterwards, we used Computational Geometry Algorithms Library (CGAL) to compute the alpha shape model of the mutants.

Results: We analyzed the EGFR mutation-induced drug resistance based on the motion trajectories obtained from MD simulation. We computed alpha shape model of all the trajectory frames for each mutation type. Solid angle was used to characterize the curvature of the atoms at the drug binding site. We measured the knob level of the drug binding pocket of each mutant from two ways and analyzed its relationship with the drug response level. Results show that 90 % of the mutants can be grouped correctly by setting a certain knob level threshold.

Conclusions: There is a strong correlation between the geometric properties of the drug binding pocket of the EGFR mutants and the corresponding drug responses, which can be used to predict the response of a new EGFR mutant to a drug molecule.

No MeSH data available.