Suppr超能文献

一种计算计算方法,用于预测人类 PLCG1 基因的高风险编码和非编码 SNPs。

A computational in silico approach to predict high-risk coding and non-coding SNPs of human PLCG1 gene.

机构信息

Department of Biochemistry and Molecular Biology, Clinical Biochemistry and Translational Medicine Laboratory, University of Dhaka, Dhaka, Bangladesh.

出版信息

PLoS One. 2021 Nov 18;16(11):e0260054. doi: 10.1371/journal.pone.0260054. eCollection 2021.

Abstract

PLCG1 gene is responsible for many T-cell lymphoma subtypes, including peripheral T-cell lymphoma (PTCL), angioimmunoblastic T-cell lymphoma (AITL), cutaneous T-cell lymphoma (CTCL), adult T-cell leukemia/lymphoma along with other diseases. Missense mutations of this gene have already been found in patients of CTCL and AITL. The non-synonymous single nucleotide polymorphisms (nsSNPs) can alter the protein structure as well as its functions. In this study, probable deleterious and disease-related nsSNPs in PLCG1 were identified using SIFT, PROVEAN, PolyPhen-2, PhD-SNP, Pmut, and SNPS&GO tools. Further, their effect on protein stability was checked along with conservation and solvent accessibility analysis by I-mutant 2.0, MUpro, Consurf, and Netsurf 2.0 server. Some SNPs were finalized for structural analysis with PyMol and BIOVIA discovery studio visualizer. Out of the 16 nsSNPs which were found to be deleterious, ten nsSNPs had an effect on protein stability, and six mutations (L411P, R355C, G493D, R1158H, A401V and L455F) were predicted to be highly conserved. Among the six highly conserved mutations, four nsSNPs (R355C, A401V, L411P and L455F) were part of the catalytic domain. L411P, L455F and G493D made significant structural change in the protein structure. Two mutations-Y210C and R1158H had post-translational modification. In the 5' and 3' untranslated region, three SNPs, rs139043247, rs543804707, and rs62621919 showed possible miRNA target sites and DNA binding sites. This in silico analysis has provided a structured dataset of PLCG1 gene for further in vivo researches. With the limitation of computational study, it can still prove to be an asset for the identification and treatment of multiple diseases associated with the target gene.

摘要

PLCG1 基因与多种 T 细胞淋巴瘤亚型有关,包括外周 T 细胞淋巴瘤(PTCL)、血管免疫母细胞性 T 细胞淋巴瘤(AITL)、皮肤 T 细胞淋巴瘤(CTCL)、成人 T 细胞白血病/淋巴瘤以及其他疾病。该基因的错义突变已在 CTCL 和 AITL 患者中发现。非同义单核苷酸多态性(nsSNP)可改变蛋白质结构及其功能。在这项研究中,使用 SIFT、PROVEAN、PolyPhen-2、PhD-SNP、Pmut 和 SNPS&GO 工具,确定了 PLCG1 中可能具有破坏性和与疾病相关的 nsSNP。此外,通过 I-mutant 2.0、MUpro、Consurf 和 Netsurf 2.0 服务器检查了它们对蛋白质稳定性的影响,以及保守性和溶剂可及性分析。使用 PyMol 和 BIOVIA discovery studio visualizer 对一些 SNP 进行了结构分析。在发现的 16 个具有破坏性的 nsSNP 中,有 10 个对蛋白质稳定性有影响,有 6 个突变(L411P、R355C、G493D、R1158H、A401V 和 L455F)被预测为高度保守。在这 6 个高度保守的突变中,有 4 个 nsSNP(R355C、A401V、L411P 和 L455F)位于催化结构域。L411P、L455F 和 G493D 使蛋白质结构发生了显著的结构变化。两个突变-Y210C 和 R1158H 有翻译后修饰。在 5'和 3'非翻译区,三个 SNP,rs139043247、rs543804707 和 rs62621919 显示可能的 miRNA 靶位点和 DNA 结合位点。这项计算机分析为进一步的体内研究提供了 PLCG1 基因的结构化数据集。由于计算研究的局限性,它仍然可以成为鉴定和治疗与目标基因相关的多种疾病的资产。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4131/8601573/a90b954aa475/pone.0260054.g001.jpg

相似文献

1
A computational in silico approach to predict high-risk coding and non-coding SNPs of human PLCG1 gene.
PLoS One. 2021 Nov 18;16(11):e0260054. doi: 10.1371/journal.pone.0260054. eCollection 2021.
4
Predicting the functional and structural consequences of nsSNPs in human methionine synthase gene using computational tools.
Syst Biol Reprod Med. 2019 Aug;65(4):288-300. doi: 10.1080/19396368.2019.1568611. Epub 2019 Jan 24.
5
Computational analysis for the determination of deleterious nsSNPs in human MTHFR gene.
Comput Biol Chem. 2018 Jun;74:20-30. doi: 10.1016/j.compbiolchem.2018.02.022. Epub 2018 Feb 27.

本文引用的文献

1
Inferring the molecular and phenotypic impact of amino acid variants with MutPred2.
Nat Commun. 2020 Nov 20;11(1):5918. doi: 10.1038/s41467-020-19669-x.
2
The InterPro protein families and domains database: 20 years on.
Nucleic Acids Res. 2021 Jan 8;49(D1):D344-D354. doi: 10.1093/nar/gkaa977.
3
Insights Into the Molecular and Cellular Underpinnings of Cutaneous T Cell Lymphoma.
Yale J Biol Med. 2020 Mar 27;93(1):111-121. eCollection 2020 Mar.
8
NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning.
Proteins. 2019 Jun;87(6):520-527. doi: 10.1002/prot.25674. Epub 2019 Mar 9.
9
Ensembl variation resources.
Database (Oxford). 2018 Jan 1;2018:bay119. doi: 10.1093/database/bay119.
10
UniProt: a worldwide hub of protein knowledge.
Nucleic Acids Res. 2019 Jan 8;47(D1):D506-D515. doi: 10.1093/nar/gky1049.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验