Issue 33, 2022, Issue in Progress

Molecular docking and dynamics based approach for the identification of kinase inhibitors targeting PI3Kα against non-small cell lung cancer: a computational study

Abstract

Non-small cell lung cancer (NSCLC) is an obscure disease whose incidence is increasing worldwide day by day, and PI3Kα is one of the major targets for cell proliferation due to the mutation. Since PI3K is a class of kinase enzyme, and no in silico research has been performed on the inhibition of PI3Kα mutation by small molecules, we have selected the protein kinase inhibitor database and performed the energy minimization process by ligand preparation. The key objective of this research is to identify the potential hits from the protein kinase inhibitor library and further to perform lead optimization by a molecular docking and dynamics approach. And so, the protein was selected (PDB ID: 4JPS), having a unique inhibitor and a specific binding pocket with amino acid residue for the inhibition of kinase activity. After the docking protocol validation, structure-based virtual screening by molecular docking and MMGBSA binding affinity calculations were performed and a total of ten hits were reported. Detailed analysis of the best scoring molecules was performed with ADMET analysis, induced fit docking (IFD) and molecular dynamics (MD) simulation. Two molecules – 6943 and 34100 – were considered lead molecules and showed better results than the PI3K inhibitor Copanlisib in the docking assessment, ADMET analysis, and molecular dynamics simulation. Furthermore, the synthetic accessibility of the two compounds – 6943 and 34100 – was investigated using SwissADME, and the two lead molecules are easier to synthesize than the PI3K inhibitor Copanlisib. Computational drug discovery tools were used for identification of kinase inhibitors as anti-cancer agents for NSCLC in the present research.

Graphical abstract: Molecular docking and dynamics based approach for the identification of kinase inhibitors targeting PI3Kα against non-small cell lung cancer: a computational study

Supplementary files

Article information

Article type
Paper
Submitted
03 Jun 2022
Accepted
23 Jun 2022
First published
03 Aug 2022
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2022,12, 21452-21467

Molecular docking and dynamics based approach for the identification of kinase inhibitors targeting PI3Kα against non-small cell lung cancer: a computational study

D. Halder, S. Das, A. R. and J. R. S., RSC Adv., 2022, 12, 21452 DOI: 10.1039/D2RA03451D

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements