Personalized prediction of EGFR mutation-induced drug resistance in lung cancer.

ABSTRACT: EGFR mutation-induced drug resistance has significantly impaired the potency of small molecule tyrosine kinase inhibitors in lung cancer treatment. Computational approaches can provide powerful and efficient techniques in the investigation of drug resistance. In our work, the EGFR mutation feature is characterized by the energy components of binding free energy (concerning the mutant-inhibitor complex), and we combine it with specific personal features for 168 clinical subjects to construct a personalized drug resistance prediction model. The 3D structure of an EGFR mutant is computationally predicted from its protein sequence, after which the dynamics of the bound mutant-inhibitor complex is simulated via AMBER and the binding free energy of the complex is calculated based on the dynamics

SUBMITTER: Wang DD 

PROVIDER: S-EPMC3790204 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature