Dataset Information

Identification of Pathogenic Missense Mutations in the CHRNA5 Gene: A Computational Approach.

ABSTRACT: Aim The CHRNA5/A3/B4 gene locus is closely related to nicotine dependence and other smoking-related disorders. Coupling genetic and clinical studies of nicotine dependence and smoking behaviors may open new avenues for medication development. The aim of this study is to investigate the functional missense mutations in the CHRNA5 gene. Methodology The Ensembl database was used to gather data on missense mutations of the human CHRNA5 gene. Computational tools viz. SIFT (Sorting Intolerant From Tolerant), PolyPhen (Polymorphism Phenotyping), PROVEAN (Protein Variation Effect Analyzer), I-Mutant, and MutPred were used to uncover the pathogenic mutations in the gene under investigation. Results Among 161 missense variants reported inthe CHRNA5 gene, 94 variants were found to be highly pathogenic. Moreover, 20 were pathogenic and 4 were not pathogenic. Conclusion The computational analysis disclosed harmful mutations in the CHRNA5 gene which could be potentially associated with smoking-related traits.

SUBMITTER: Kumaraguru M

PROVIDER: S-EPMC10663970 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Identification of Pathogenic Missense Mutations in the CHRNA5 Gene: A Computational Approach.

Kumaraguru Mahalakshmi M L Leelavathi L Priyadharsini Vijayashree J VJ I Meignana Arumugham MA S Rajeshkumar R

Cureus 20231023 10

Aim The CHRNA5/A3/B4 gene locus is closely related to nicotine dependence and other smoking-related disorders. Coupling genetic and clinical studies of nicotine dependence and smoking behaviors may open new avenues for medication development. The aim of this study is to investigate the functional missense mutations in the CHRNA5 gene. Methodology The Ensembl database was used to gather data on missense mutations of the human CHRNA5 gene. Computational tools viz. SIFT (Sorting Intolerant From Tol ...[more]

PMID: 38021533

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Project description:Traditional cancer treatment approaches are often hindered by the presence of toxic side effects and the high rate of relapse observed in treated organs. In contrast, novel immunotherapeutic strategies targeting immune checkpoint inhibitors, particularly PD-1, have demonstrated promising results with minimal adverse effects. However, the emergence of immunotherapeutic-resistant tumors, predominantly caused by intrinsic mutations, poses a significant obstacle to successful treatment outcomes. Consequently, the primary objective of this study was to screen for the most detrimental missense mutations in the PD-1 gene associated with immunotherapeutic resistance. To achieve this aim, a comprehensive screening process utilizing 20 web servers, incorporating both sequence- and structure-based methodologies, was undertaken. Through meticulous analysis and mutual disease association sorting, four specific missense mutations were successfully identified. These mutations, namely, R38C, D61V, R94C, and D117V, emerged as the leading contributors to genetic cancer progression and immunotherapeutic resistance against PD-1 blockers. The findings presented in this study are supported by multiple lines of evidence. A thorough examination of protein topology, structural alignment, docking interactions with PD-L1, and protein flexibility collectively confirmed the pathogenic nature of these sorted mutations. By considering these various aspects, we have gained a comprehensive understanding of the underlying mechanisms driving immunotherapeutic resistance. In conclusion, the comprehensive screening process undertaken in this study has successfully identified R38C, D61V, R94C, and D117V as the primary mutations contributing to genetic cancer progression and immunotherapeutic resistance against PD-1 blockers. The integration of protein topology analysis, structural alignment, docking studies with PD-L1, and assessment of protein flexibility have collectively provided robust evidence to support the pathogenic significance of these mutations.

Dataset Information

Identification of Pathogenic Missense Mutations in the CHRNA5 Gene: A Computational Approach.

Publications

Identification of Pathogenic Missense Mutations in the CHRNA5 Gene: A Computational Approach.

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