{"title":"In-silico profiling of SLC6A19, for identification of deleterious ns-SNPs to enhance the Hartnup disease diagnosis","authors":"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","doi":"10.1016/j.comtox.2022.100215","DOIUrl":null,"url":null,"abstract":"<div><p>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, <em>etc</em>. and also to determine effective lead compound for its treatment by docking. The SLC6A19 gene translates to B<sup>0</sup>AT1 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.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"22 ","pages":"Article 100215"},"PeriodicalIF":3.1000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468111322000032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TOXICOLOGY","Score":null,"Total":0}
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.
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