In-silico profiling of SLC6A19, for identification of deleterious ns-SNPs to enhance the Hartnup disease diagnosis

IF 3.1 Q2 TOXICOLOGY
Wahidah H. Al-Qahtani , Dinakarkumar Yuvaraj , Anjaneyulu Sai Ramesh , Haryni Jayaradhika Raghuraman Rengarajan , Muthusamy Karnan , Jothiramalingam Rajabathar , Arokiyaraj Charumathi , Sayali Harishchandra Pangam , Priyanka Kameswari Devarakonda , Gouthami Nadiminti , Prikshit Sharma
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引用次数: 0

Abstract

The mutation in the solute carrier 6 (SLC6A19) gene causes the Hartnup disorder, affecting the absorption of non-polar amino acids. Recent DNA sequencing advances have increased the identification of single nucleotide polymorphisms (SNPs) in the SLC6A19 gene, but no further information regarding their deleterious probability is available. Hence, this study aims to comprehensively analyze and identify the potentially deleterious non-synonymous-SNPs of the SLC6A19 gene with a computational approach using openly accessible online software tools including SIFT, PolyPhen2, SAVES 5.0, SPIDER, etc. and also to determine effective lead compound for its treatment by docking. The SLC6A19 gene translates to B0AT1 tetramer protein, amongst chain A was taken into consideration. The analysis revealed mutation G490S (chain A) of the said protein as the candidate ns-SNP among the screened 539 missense mutations, retrieved from the National Centre for Biotechnology Information (NCBI). Moreover, the binding energy of the candidate ns-SNP had a higher affinity for benztropine over conventional drugs such as nicotinamide and niacin. Yet, clinical validation is required to support the above findings.

SLC6A19基因的芯片分析:识别有害的nsnps以提高哈特纳普病的诊断
溶质载体6 (SLC6A19)基因突变导致哈特纳普病,影响非极性氨基酸的吸收。最近的DNA测序进展增加了SLC6A19基因单核苷酸多态性(snp)的鉴定,但没有关于其有害概率的进一步信息。因此,本研究旨在利用SIFT、PolyPhen2、SAVES 5.0、SPIDER等开放的在线软件工具,采用计算方法综合分析和鉴定SLC6A19基因的潜在有害非同名snp,并通过对接确定有效的先导化合物进行治疗。SLC6A19基因翻译成B0AT1四聚体蛋白,其中A链被考虑。分析结果显示,从国家生物技术信息中心(NCBI)检索到的539个错义突变中,该蛋白的突变G490S (A链)是候选的ns-SNP。此外,候选ns-SNP的结合能对苯托品的亲和力高于烟酰胺和烟酸等常规药物。然而,需要临床验证来支持上述发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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