Title

Virtual screening of natural compounds as inhibitors of EGFR 696-1022 T790M associated with non-small cell lung cancer

Authors

Mahesha Nand1, Priyanka Maiti2, Ragini Pant1, Madhulata Kumari3, Subhash Chandra*,2, Veena Pande1

Affiliation

1Department of Biotechnology, Kumaun University, Bhimtal Campus Bhimtal, Uttarakhand, India; 2Department of Botany, Kumaun University, S.S.J Campus, Almora, Uttarakhand, India; 3Department of Information Technology, Kumaun University, SSJ Campus, Almora, Uttarakhand 263601, India.

Email

Mahesha Nand - E-mail: maheshlyf87@gmail.com, Priyanka Maiti – E-mail: priyankamaiti.06@gmail.com, Ragini Pant – E-mail: raginipant1@gmail.com, Subhash Chandra* – E-mail: scjnu@yahoo.co.in, Veena Pande – E-mail: - veena_kumaun@yahoo.co.in; Phone: +91-7351976362; *Corresponding author

Article Type

Hypothesis

Date

Received June 2, 2016; Revised July 8, 2016; Accepted July 9, 2016; Published October 10, 2016

Non-small cell lung cancer (NSCLC) is the most dominating and lethal type of lung cancer triggering more than 1.3 million deaths per year. The most effective line of treatment against NSCLC is to target epidermal growth factor receptor (EGFR) activating mutation. The present study aims to identify the novel anti-lung cancer compounds form nature against EGFR 696-1022 T790M by using in silico approaches. A library of 419 compounds from several natural resources was subjected to pre-screen through machine learning model using Random Forest classifier resulting 63 screened molecules with active potential. These molecules were further screened by molecular docking against the active site of EGFR 696-1022 T790M protein using AutoDock Vina followed by rescoring using X-Score. As a result 4 compounds were finally screened namely Granulatimide, Danorubicin, Penicinoline and Austocystin D with lowest binding energy which were -6.5 kcal/mol, -6.1 kcal/mol, -6.3 kcal/mol and -7.1 kcal/mol respectively. The drug likeness of the screened compounds was evaluated using FaF-Drug3 server. Finally toxicity of the hit compounds was predicted in cell line using the CLC-Pred server where their cytotoxic ability against various lung cancer cell lines was confirmed. We have shown 4 potential compounds, which could be further exploited as efficient drug candidates against lung cancer.

Keywords

lung cancer, natural compounds, EGFR, machine learning, molecular docking, and prediction

Citation

Nand et al. Bioinformation 12(6): 311-317 (2016)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.