ArticleProteome science2016
Identifying EGFR mutation-induced drug resistance based on alpha shape model analysis of the dynamics.
Article in Proteome science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 17 citations in OpenAlex.
- Machine learning based personalized drug response prediction for lung cancer patients.Scientific reports · 2022Article
- Genotype-determined EGFR-RTK heterodimerization and its effects on drug resistance in lung Cancer treatment revealed by molecular dynamics simulations.BMC molecular and cell biology · 2021Article
- Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods.Computational and structural biotechnology journal · 2020Article
- Prediction of sensitivity to gefitinib/erlotinib for EGFR mutations in NSCLC based on structural interaction fingerprints and multilinear principal component analysis.BMC bioinformatics · 2018Article
- EGFR and Cortactin: Markers for potential double target therapy in oral squamous cell carcinoma.Experimental and therapeutic medicine · 2017Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEpidermal 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.
methodsIn 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.
resultsWe 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.
conclusionsThere 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.
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