Parametric Models for Understanding Atomic Trajectories in Different Domains of Lung Cancer Causing Protein

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

6 Scopus Citations
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Detail(s)

Original languageEnglish
Article number8720156
Pages (from-to)67551-67563
Journal / PublicationIEEE Access
Volume7
Online published22 May 2019
Publication statusPublished - 2019

Abstract

Non-small cell lung cancer (NSCLC) is a major cause of death worldwide. About 80% to 85% of lung cancer cases are NSCLC. It is well known that mutation of the epidermal growth factor (EGFR) may lead to NSCLC. The first generation drugs are effective initially, but almost all patients develop drug resistance after about a year due to a secondary mutation. The computational methods are an efficient tool for investigating drug resistance, design and discovery. Moreover, Molecular Dynamics (MD) simulation enables us to study and analyze the behavior of proteins and molecules at the atomic level. MD simulations offer extraordinary insight about biomolecules and are a valuable tool for computer aided drug discovery. Earlier studies on EGFR only focused on the kinase domain. Because EGFR is a multi-domain protein, mutations in the kinase domain may affect the function in other domains. Therefore, it is important to investigate the complete structure of EGFR and its mutants. In this paper, we first generate the complete structure of EGFR and perform MD simulation for the wildtype EGFR, EGFR with L858R mutation and EGFR with L858R and T790M mutation. We divide the complete structure of EGFR and its mutants into 8 domains according to the reference crystal structure. We then consider atom trajectories as time series signals and estimate the power spectral densities using the auto-regressive integrated (ARI) model, which shows interesting insight. Dynamic time warping is used to analyze the similarity between each domain of the structures. Interesting patterns are observed which may be useful for investigating drug resistance and design. Furthermore, Pearson correlation coefficient, peaks and widths of the power spectral density are calculated for each domain. The simulation results provide useful insight about conformation dynamics of EGFR, such as atom motion and protein stability. The domains are less correlated in L858R type and even weaker when the second mutation occurs. The warping patterns are changed due to mutation and the movement of atoms is distorted. Hence, it is difficult for a drug to bind to the protein. These findings will be useful in understanding the characteristics of EGFR and for computer aided drug design process for NSCLC patients.

Research Area(s)

  • Autoregressive integrated model, drug resistance, dynamic time warping, epidermal growth factor receptor, molecular dynamics, non-small cell lung cancer