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In Silico Molecular Modeling and Docking Analysis in Lung Cancer Cell Proteins

Оглавление

Manisha Sritharan1 and Asita Elengoe2*

1Department of Science and Biotechnology, Faculty of Engineering and Life Sciences, University of Selangor, Bestari Jaya, Selangor, Malaysia

2Department of Biotechnology, Faculty of Science, Lincoln University College, Petaling Jaya, Selangor, Malaysia

Abstract

In this study, the three-dimensional (3D) models of lung cancer cell line proteins [epidermal growth factor (EGFR), K-ras oncogene protein, and tumor suppressor (TP53)] were generated and their binding affinities with curcumin, ellagic acid, and quercetin through local docking were assessed. Firstly, Swiss model was used to build lung cancer cell line proteins and then visualized by the PyMol software. Next, ExPASy ProtParam Proteomics server was used to evaluate the physical and chemical parameters of the protein structures. Furthermore, the protein models were validated using PROCHECK, ProQ, ERRAT, and Verify3D programs. Lastly, the protein models were docked with curcumin, ellagic acid, and quercetin by using BSP-Slim server. All three protein models were adequate and in exceptional standard. The curcumin showed binding energy with EGFR, K-ras oncogene protein, and TP53 at 5.320, 2.730, and 1.633, kcal/mol, respectively. Besides that, the ellagic acid showed binding energy of EGFR, K-ras oncogene protein, and TP53 at –2.892, 0.921, and 0.054 kcal/mol, respectively. Moreover, the quercetin showed binding energy of EGFR, K-ras oncogene protein, and TP53 at –1.249, –1.154, and –0.809 kcal/mol, respectively. The EGFR had the strongest bond with ellagic acid while K-ras oncogene protein and TP53 had the strongest interaction with quercetin. In order to identify the appropriate function, all these potential drug candidates can be further assessed through laboratory experiments.

Keywords: EGFR, K-ras, TP53, curcumin, ellagic acid, quercetin, docking

The Internet of Medical Things (IoMT)

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