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

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.

Predicting the effects of rare genetic variants on oncogenic signaling pathways: A computational analysis of HRAS protein function

“…In a recent study by Behairy et al (2022), various SNPs of the HRAS gene were assessed to identify those potentially deleterious and associated with disease development using computational approaches. We searched for nsSNPs against HRAS in SNP databases and found 138 hits.…”

“…Thus, using multiple computational tools and integrating their results can provide more reliable predictions of functional nsSNPs, which can aid in understanding the molecular basis of diseases caused by nsSNPs. Overall, the study by Behairy et al (2022), highlights the importance of using computational approaches to identify potential deleterious nsSNPs in genes and their association with disease development. The study also underscores the significance of using multiple computational tools to validate the functional relevance of nsSNPs and the need for further research to establish their association with disease development.…”

Investigation of pathogenic germline variants in gastric cancer and development of “GasCanBase” database

“…So, the current study provides strong evidence that the selected nsSNPs might prompt deleterious effects as they have been computationally proven to alter the protein domains and motifs. They are also capable of changing structural conformations and interfering with drug binding 83 . Since these nsSNPs could have a damaging impact with respect to their protein structure, they might be used as potential genetic markers.…”

“…They are also capable of changing structural conformations and interfering with drug binding. 83 Since these nsSNPs could have a damaging impact with respect to their protein structure, they might be used as potential genetic markers.…”

Unraveling Extremely Damaging IRAK4 Variants and Their Potential Implications for IRAK4 Inhibitor Efficacy

“…Moreover, studying the association between genetic variants and different disorders has received significant attention in recent years [13][14][15]. Given the extensive reporting and deposition of missense single-nucleotide polymorphisms (SNPs) in databases, it has become imperative to employ a filtration process to distinguish SNPs with potential pathogenicity from the larger pool of neutral variants [16,17].…”