Computational Analysis of Deleterious SNPs in NRAS to Assess Their Potential Correlation With Carcinogenesis

Mohammed Y Behairy; Mohamed A Soltan; Mohamed S Adam; Ahmed M Refaat; Ehab M Ezz; Sarah Albogami; Eman Fayad; Fayez Althobaiti; Ahmed M Gouda; Ashraf E Sileem; Mahmoud A Elfaky; Khaled M Darwish; Muhammad Alaa Eldeen

doi:10.3389/fgene.2022.872845

Computational Analysis of Deleterious SNPs in NRAS to Assess Their Potential Correlation With Carcinogenesis

Front Genet. 2022 Aug 16:13:872845. doi: 10.3389/fgene.2022.872845. eCollection 2022.

Authors

Affiliations

¹ Department of Microbiology and Immunology, Faculty of Pharmacy, University of Sadat City, Sadat City, Egypt.
² Department of Microbiology and Immunology, Faculty of Pharmacy, Sinai University, Ismailia, Egypt.
³ Department of Pharmacology, Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt.
⁴ Zoology Departmen, Faculty of Science, Minia University, El-Minia, Egypt.
⁵ Department of Pharmacology, Faculty of Medicine, University of Khartoum, Khartoum, Sudan.
⁶ Department of Biotechnology, College of Sciences, Taif University, Taif, Saudi Arabia.
⁷ Department of Pharmacy Practice, Faculty of Pharmacy, Zagazig University, Zagazig, Egypt.
⁸ Department of Chest Diseases, Faculty of Medicine, Zagazig University, Zagazig, Egypt.
⁹ Department of Natural Products, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia.
¹⁰ Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia.
¹¹ Department of Medicinal Chemistry, Faculty of Pharmacy, Suez Canal University, Ismailia, Egypt.
¹² Cell Biology, Histology and Genetics Division, Zoology Department, Faculty of Science, Zagazig University, Zagazig, Egypt.

Abstract

The NRAS gene is a well-known oncogene that acts as a major player in carcinogenesis. Mutations in the NRAS gene have been linked to multiple types of human tumors. Therefore, the identification of the most deleterious single nucleotide polymorphisms (SNPs) in the NRAS gene is necessary to understand the key factors of tumor pathogenesis and therapy. We aimed to retrieve NRAS missense SNPs and analyze them comprehensively using sequence and structure approaches to determine the most deleterious SNPs that could increase the risk of carcinogenesis. We also adopted structural biology methods and docking tools to investigate the behavior of the filtered SNPs. After retrieving missense SNPs and analyzing them using six in silico tools, 17 mutations were found to be the most deleterious mutations in NRAS. All SNPs except S145L were found to decrease NRAS stability, and all SNPs were found on highly conserved residues and important functional domains, except R164C. In addition, all mutations except G60E and S145L showed a higher binding affinity to GTP, implicating an increase in malignancy tendency. As a consequence, all other 14 mutations were expected to increase the risk of carcinogenesis, with 5 mutations (G13R, G13C, G13V, P34R, and V152F) expected to have the highest risk. Thermodynamic stability was ensured for these SNP models through molecular dynamics simulation based on trajectory analysis. Free binding affinity toward the natural substrate, GTP, was higher for these models as compared to the native NRAS protein. The Gly13 SNP proteins depict a differential conformational state that could favor nucleotide exchange and catalytic potentiality. A further application of experimental methods with all these 14 mutations could reveal new insights into the pathogenesis and management of different types of tumors.

Keywords: NRAS gene; carcinogenesis; computational analysis; precision medicine; single nucleotide polymorphism.