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利用计算方法鉴定 NF1 的致病性错义突变。

Identification of Pathogenic Missense Mutations of NF1 Using Computational Approaches.

机构信息

Department of Respiratory, Hangzhou Children's Hospital, Hangzhou, 310014, Zhejiang Province, China.

Department of Pharmacogenomics, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang Province, China.

出版信息

J Mol Neurosci. 2024 Oct 7;74(4):94. doi: 10.1007/s12031-024-02271-x.

Abstract

Neurofibromatosis type 1 (NF1) is a prevalent autosomal dominant disorder caused by mutations in the NF1 gene, leading to multisystem disorders. Given the critical role of cysteine residues in protein stability and function, we aimed to identify key NF1 mutations affecting cysteine residues that significantly contribute to neurofibromatosis pathology. To identify the most critical mutations in the NF1 gene that contribute to the pathology of neurofibromatosis, we employed a sophisticated computational pipeline specifically designed to detect significant mutations affecting the NF1 gene. Our approach involved an exhaustive search of databases such as the Human Gene Mutation Database (HGMD), UniProt, and ClinVar for information on missense mutations associated with NF1. Our search yielded a total of 204 unique cysteine missense mutations. We then employed in silico prediction tools, including PredictSNP, iStable, and Align GVGD, to assess the impact of these mutations. Among the mutations, C379R, R1000C, and C1016Y stood out due to their deleterious effects on the biophysical properties of the neurofibromin protein, significantly destabilizing its structure. These mutations were subjected to further phenotyping analysis using SNPeffect 4.0, which predicted disturbances in the protein's chaperone binding sites and overall structural stability. Furthermore, to directly visualize the impact of these mutations on protein structure, we utilized AlphaFold3 to simulate both the wild-type and mutant NF1 structures, revealing the significant effects of the R1000C mutation on the protein's conformation. In conclusion, the identification of these mutations can play a pivotal role in advancing the field of precision medicine and aid in the development of effective drugs for associated diseases.

摘要

神经纤维瘤病 1 型(NF1)是一种常见的常染色体显性遗传病,由 NF1 基因突变引起,导致多系统疾病。鉴于半胱氨酸残基在蛋白质稳定性和功能中的关键作用,我们旨在确定影响半胱氨酸残基的关键 NF1 突变,这些突变对神经纤维瘤病病理学有重要贡献。为了确定 NF1 基因中导致神经纤维瘤病病理学的最关键突变,我们采用了一种专门设计的复杂计算管道,用于检测影响 NF1 基因的显著突变。我们的方法涉及对数据库(如人类基因突变数据库(HGMD)、UniProt 和 ClinVar)进行详尽搜索,以获取与 NF1 相关的错义突变信息。我们的搜索共产生了 204 个独特的半胱氨酸错义突变。然后,我们使用了计算预测工具,包括 PredictSNP、iStable 和 Align GVGD,来评估这些突变的影响。在这些突变中,C379R、R1000C 和 C1016Y 由于它们对半胱氨酸残基的生物物理性质的有害影响而引人注目,显著破坏了神经纤维瘤蛋白的结构。这些突变进一步通过 SNPeffect 4.0 进行表型分析,该分析预测了蛋白质伴侣结合位点和整体结构稳定性的干扰。此外,为了直接观察这些突变对蛋白质结构的影响,我们利用 AlphaFold3 模拟了野生型和突变型 NF1 结构,揭示了 R1000C 突变对蛋白质构象的显著影响。总之,这些突变的鉴定可以在推进精准医学领域和开发相关疾病的有效药物方面发挥关键作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/02e4/11458684/b504e61fa83e/12031_2024_2271_Fig1_HTML.jpg

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