Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12859-015-0522-3
Title: EGFR Mutant Structural Database: Computationally predicted 3D structures and the corresponding binding free energies with gefitinib and erlotinib
Authors: Ma, L
Wang, D.D 
Huang, Y
Yan, H
Wong, M.P
Lee, V.H.F
Keywords: Amino acids
Biological organs
Database systems
Diseases
Drug therapy
Free energy
Binding free energy
Epidermal growth factor receptors
Erlotinib
Gefitinib
Non small cell lung cancer
Tyrosine kinase inhibitor
Binding energy
antineoplastic agent
epidermal growth factor receptor
erlotinib
gefitinib
protein binding
protein kinase inhibitor
quinazoline derivative
chemistry
exon
genetics
human
lung tumor
metabolism
molecular dynamics
mutation
non small cell lung cancer
protein conformation
protein database
Antineoplastic Agents
Carcinoma, Non-Small-Cell Lung
Databases, Protein
Erlotinib Hydrochloride
Exons
Humans
Lung Neoplasms
Molecular Dynamics Simulation
Mutation
Protein Binding
Protein Conformation
Protein Kinase Inhibitors
Quinazolines
Receptor, Epidermal Growth Factor
Issue Date: 2015
Citation: Ma, L, Wang, D.D, Huang, Y, Yan, H, Wong, M.P, Lee, V.H.F (2015). EGFR Mutant Structural Database: Computationally predicted 3D structures and the corresponding binding free energies with gefitinib and erlotinib. BMC Bioinformatics 16 (1) : 85. ScholarBank@NUS Repository. https://doi.org/10.1186/s12859-015-0522-3
Rights: Attribution 4.0 International
Abstract: 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. © 2015 Ma et al.; licensee BioMed Central.
Source Title: BMC Bioinformatics
URI: https://scholarbank.nus.edu.sg/handle/10635/181411
ISSN: 14712105
DOI: 10.1186/s12859-015-0522-3
Rights: Attribution 4.0 International
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