Predicting Deleterious Non-Synonymous Single Nucleotide Polymorphisms (nsSNPs) of HRAS Gene and In Silico Evaluation of Their Structural and Functional Consequences towards Diagnosis and Prognosis of Cancer

The Harvey rat sarcoma (HRAS) proto-oncogene belongs to the RAS family and is one of the pathogenic genes that cause cancer. Deleterious nsSNPs might have adverse consequences at the protein level. This study aimed to investigate deleterious nsSNPs in the HRAS gene in predicting structural alterations associated with mutants that disrupt normal protein-protein interactions. Functional and structural analysis was employed in analyzing the HRAS nsSNPs. Putative post-translational modification sites and the changes in protein-protein interactions, which included a variety of signal cascades, were also investigated. Five different bioinformatics tools predicted 33 nsSNPs as "pathogenic" or "harmful". Stability analysis predicted rs1554885139, rs770492627, rs1589792804, rs730880460, rs104894227, rs104894227, and rs121917759 as unstable. Protein-protein interaction analysis revealed that HRAS has a hub connecting three clusters consisting of 11 proteins, and changes in HRAS might cause signal cascades to dissociate. Furthermore, Kaplan-Meier bioinformatics analyses indicated that the HRAS gene deregulation affected the overall survival rate of patients with breast cancer, leading to prognostic significance. Thus, based on these analyses, our study suggests that the reported nsSNPs of HRAS may serve as potential targets for different proteomic studies, diagnoses, and therapeutic interventions focusing on cancer.

In-silico analysis of nsSNPs in BCL-2 family proteins: Implications for colorectal cancer pathogenesis and therapeutics

Colorectal cancer (CRC) is a multifaceted disease characterized by abnormal cell proliferation in the colon and rectum. The BCL-2 family proteins are implicated in CRC pathogenesis, yet the impacts of genetic variations within these proteins remains elusive. This in-silico study employs diverse sequence- and structure-based bioinformatics tools to identify potentially pathogenic nonsynonymous single nucleotide polymorphisms (nsSNPs) in BCL-2 family proteins. Leveraging computational tools including SIFT, PolyPhen-2, SNPs&GO, PhD-SNP, PANTHER, and Condel, 94 nsSNPs were predicted as deleterious, damaging, and disease-associated by at least five tools. Stability analysis with I-Mutant2.0, MutPred, and PredictSNP further identified 31 nsSNPs that reduce protein stability. Conservation analysis highlighted highly functional, exposed variants (rs960653284, rs758817904, rs1466732626, rs569276903, rs746711568, rs764437421, rs779690846, and rs2038330314) and structural, buried variants (rs376149674, rs1375767408, rs1582066443, rs367558446, rs367558446, rs1319541919, and rs1370070128). To explore the functional effects of these mutations, molecular docking and molecular dynamics simulations were conducted. G233D (rs376149674) and R12G (rs960653284) mutations in the BCL2 protein exhibited the greatest differences in docking scores with d-α-Tocopherol and Tocotrienol, suggesting enhanced protein-ligand interactions. The simulations revealed that d-α-Tocopherol and Tocotrienol (strong binders) contributed to greater stability of BCL-2 family proteins, while Fluorouracil, though weaker, still demonstrated selective binding stability. This work represents the first comprehensive computational analysis of functional nsSNPs in BCL-2 family proteins, providing insights into their roles in CRC pathogenesis. While these findings demand experimental validation, they hold great promise for guiding future large-scale population studies, facilitating drug repurposing efforts, and advancing the development of targeted diagnostic and therapeutic modalities for CRC.