ArticleScientific reports2021
Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 23 citations in OpenAlex.
- Primer-walking sequencing identifies predominantly noncoding TP53 variants in canine cancers.Journal of veterinary science · 2026Article
- Genetic background shapes AI-predicted variant effects.bioRxiv : the preprint server for biology · 2026Article
- Computational analysis and validation of UGT1A1/4 missense variants impacting tecovirimat metabolism in monkeypox patients.Frontiers in systems biology · 2026Article
- Profiling crRNA architectures for enhanced Cas12 biosensing.Communications biology · 2025Article
- Molecular targets and therapies associated with poor prognosis of triple‑negative breast cancer (Review).International journal of oncology · 2025Review
- ACMG/AMP interpretation of BRCA1 missense variants: Structure-informed scores add evidence strength granularity to the PP3/BP4 computational evidence.American journal of human genetics · 2025Article
- In-silico screening and analysis of missense SNPs in human CYP3A4/5 affecting drug-enzyme interactions of FDA-approved COVID-19 antiviral drugs.Scientific reports · 2025Article
- A computational and structural approach to identify malignant non-synonymous FOXM1 single nucleotide polymorphisms in triple-negative breast cancer.Scientific reports · 2025Article
- Association of Titin Polymorphisms with the Progression of Oral Squamous Cell Carcinoma and Its Clinicopathological Characteristics.International journal of molecular sciences · 2024Article
- Review
- Molecular and immunohistochemical study ofHeliyon · 2024Article
- Computational Exploration of Single-Nucleotide Polymorphisms in the Human hRAS Gene: Implications and Insights.Cureus · 2024Article
- Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists.PloS one · 2023Article
- Impact of highly deleterious non-synonymous polymorphisms on GRIN2A protein's structure and function.PloS one · 2023Article
- Article
- Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset.Briefings in bioinformatics · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genetic polymorphisms in DNA damage repair and tumor suppressor genes have been associated with increasing the risk of several types of cancer. Analyses of putative functional single nucleotide polymorphisms (SNP) in such genes can greatly improve human health by guiding choice of therapeutics. In this study, we selected nine genes responsible for various cancer types for gene enrichment analysis and found that BRCA1, ATM, and TP53 were more enriched in connectivity. Therefore, we used different computational algorithms to classify the nonsynonymous SNPs which are deleterious to the structure and/or function of these three proteins. The present study showed that the major pathogenic variants (V1687G and V1736G of BRCA1, I2865T and V2906A of ATM, V216G and L194H of TP53) might have a greater impact on the destabilization of the proteins. To stabilize the high-risk SNPs, we performed mutation site-specific molecular docking analysis and validated using molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) studies. Additionally, SNPs of untranslated regions of these genes affecting miRNA binding were characterized. Hence, this study will assist in developing precision medicines for cancer types related to these polymorphisms.
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