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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.


The comparison of average convex degrees of the drug Response mutants delE746_T751insVA (RL = 1, shown in a, c and e) and delL747_P753insS (RL = 2, shown in b, d and f), and the No-response ones L861R (RL = 4), dulN771_H773 (RL = 4) and G724S_L861Q (RL = 4). The drug Response mutants are shown in blue, while the No-response ones are colored red
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Fig3: The comparison of average convex degrees of the drug Response mutants delE746_T751insVA (RL = 1, shown in a, c and e) and delL747_P753insS (RL = 2, shown in b, d and f), and the No-response ones L861R (RL = 4), dulN771_H773 (RL = 4) and G724S_L861Q (RL = 4). The drug Response mutants are shown in blue, while the No-response ones are colored red

Mentions: First, we computed the average convex degree of the drug binding site for all the motion trajectory frames of each mutant. For a specific trajectory frame, the average convex degree was calculated as the sum of all the solid angle values of convex atoms at the drug binding site divided by the total number of convex atoms in this area. Figure 3 shows the comparison of average convex degree of the drug Response mutants delE746_T751insVA (RL = 1) and delL747_P753insS (RL = 2), and the No-response ones L861R (RL = 4), dulN771_H773 (RL = 4) and G724S_L861Q (RL = 4). Although the two groups of mutants cannot be separated perfectly, the average convex degree of delE746_T751insVA and delL747_P753insS are generally lower than that of L861R, dulN771_H773 and G724S_L861Q.Fig. 3


Identifying EGFR mutation-induced drug resistance based on alpha shape model analysis of the dynamics
The comparison of average convex degrees of the drug Response mutants delE746_T751insVA (RL = 1, shown in a, c and e) and delL747_P753insS (RL = 2, shown in b, d and f), and the No-response ones L861R (RL = 4), dulN771_H773 (RL = 4) and G724S_L861Q (RL = 4). The drug Response mutants are shown in blue, while the No-response ones are colored red
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: The comparison of average convex degrees of the drug Response mutants delE746_T751insVA (RL = 1, shown in a, c and e) and delL747_P753insS (RL = 2, shown in b, d and f), and the No-response ones L861R (RL = 4), dulN771_H773 (RL = 4) and G724S_L861Q (RL = 4). The drug Response mutants are shown in blue, while the No-response ones are colored red
Mentions: First, we computed the average convex degree of the drug binding site for all the motion trajectory frames of each mutant. For a specific trajectory frame, the average convex degree was calculated as the sum of all the solid angle values of convex atoms at the drug binding site divided by the total number of convex atoms in this area. Figure 3 shows the comparison of average convex degree of the drug Response mutants delE746_T751insVA (RL = 1) and delL747_P753insS (RL = 2), and the No-response ones L861R (RL = 4), dulN771_H773 (RL = 4) and G724S_L861Q (RL = 4). Although the two groups of mutants cannot be separated perfectly, the average convex degree of delE746_T751insVA and delL747_P753insS are generally lower than that of L861R, dulN771_H773 and G724S_L861Q.Fig. 3

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.