EGFRIndb: Epidermal Growth Factor Receptor Inhibitor Database

Yadav, Inderjit Singh; Singh, Harinder; Khan, Mohd Imran; Chaudhury, Ashok; Raghava, Gajendra P. S.; Agarwal, Subhash Mohan

doi:10.2174/1871520614666140323203140

Cited by 19 publications

(23 citation statements)

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“…Several EGFR-related databases are available publicly, such as the EGFR Mutation Database ( http://www.cityofhope.org/egfr-mutation-database ) [ 9 ], the Catalogue of Somatic Mutations in Cancer ( http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/ ) [ 31 ], EGFR Inhibitor Database ( http://crdd.osdd.net/raghava/egfrindb/ ) [ 32 ] and the widely used PDB [ 11 ]. The EGFR Mutation Database contains the mutant position information as well as the response to inhibitors of the NSCLC patients.…”

Section: Resultsmentioning

confidence: 99%

EGFR Mutant Structural Database: computationally predicted 3D structures and the corresponding binding free energies with gefitinib and erlotinib

Wang

Huang

et al. 2015

BMC Bioinformatics

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BackgroundEpidermal 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).ResultsWe 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.ConclusionsThe 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.

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Section: Resultsmentioning

confidence: 99%

EGFR Mutant Structural Database: computationally predicted 3D structures and the corresponding binding free energies with gefitinib and erlotinib

Wang

Huang

et al. 2015

BMC Bioinformatics

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“…We obtained 3528 anti-EGFR compounds along with their inhibitory concentration ( IC 50 ) from database EGFRindb that covers around 350 research articles [ 22 ]. These compounds are diverse in nature and belong to various structural scaffolds.…”

Section: Methodsmentioning

confidence: 99%

QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest

Singh

Singla

et al. 2015

Biol Direct

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BackgroundEpidermal Growth Factor Receptor (EGFR) is a well-characterized cancer drug target. In the past, several QSAR models have been developed for predicting inhibition activity of molecules against EGFR. These models are useful to a limited set of molecules for a particular class like quinazoline-derivatives. In this study, an attempt has been made to develop prediction models on a large set of molecules (~3500 molecules) that include diverse scaffolds like quinazoline, pyrimidine, quinoline and indole.ResultsWe train, test and validate our classification models on a dataset called EGFR10 that contains 508 inhibitors (having inhibition activity IC50 less than 10 nM) and 2997 non-inhibitors. Our Random forest based model achieved maximum MCC 0.49 with accuracy 83.7% on a validation set using 881 PubChem fingerprints. In this study, frequency-based feature selection technique has been used to identify best fingerprints. It was observed that PubChem fingerprints FP380 (C(~O) (~O)), FP579 (O = C-C-C-C), FP388 (C(:C) (:N) (:N)) and FP 816 (ClC1CC(Br)CCC1) are more frequent in the inhibitors in comparison to non-inhibitors. In addition, we created different datasets namely EGFR100 containing inhibitors having IC50 < 100 nM and EGFR1000 containing inhibitors having IC50 < 1000 nM. We trained, test and validate our models on datasets EGFR100 and EGFR1000 datasets and achieved and maximum MCC 0.58 and 0.71 respectively. In addition, models were developed for predicting quinazoline and pyrimidine based EGFR inhibitors.ConclusionsIn summary, models have been developed on a large set of molecules of various classes for discriminating EGFR inhibitors and non-inhibitors. These highly accurate prediction models can be used to design and discover novel EGFR inhibitors. In order to provide service to the scientific community, a web server/standalone EGFRpred also has been developed (http://crdd.osdd.net/oscadd/egfrpred/).ReviewersThis article was reviewed by Dr Murphy, Prof Wang and Dr. Eisenhaber.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-015-0046-9) contains supplementary material, which is available to authorized users.

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“…BindingDB [61,200] collects measured binding affinities of proteins and small molecules. EGFRIndb [201] is a literature-curated database of 4581 small, synthetic molecular inhibitors of EGFR in vitro.…”

Section: Databasesmentioning

confidence: 99%

Many InChIs and quite some feat

Warr

2015

J Comput Aided Mol Des

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EGFRIndb: Epidermal Growth Factor Receptor Inhibitor Database

Cited by 19 publications

References 0 publications

EGFR Mutant Structural Database: computationally predicted 3D structures and the corresponding binding free energies with gefitinib and erlotinib

EGFR Mutant Structural Database: computationally predicted 3D structures and the corresponding binding free energies with gefitinib and erlotinib

QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest

Many InChIs and quite some feat

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