Othman Houcemeddine, Jemimah Sherlyn, da Rocha Jorge Emanuel Batista
Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, 9 jubilee Road, Parktown, Johannesburg 2193, South Africa.
Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.
J Pers Med. 2022 Feb 11;12(2):263. doi: 10.3390/jpm12020263.
Recent genomic studies have revealed the critical impact of genetic diversity within small population groups in determining the way individuals respond to drugs. One of the biggest challenges is to accurately predict the effect of single nucleotide variants and to get the relevant information that allows for a better functional interpretation of genetic data. Different conformational scenarios upon the changing in amino acid sequences of pharmacologically important proteins might impact their stability and plasticity, which in turn might alter the interaction with the drug. Current sequence-based annotation methods have limited power to access this type of information. Motivated by these calls, we have developed the Structural Workflow for Annotating ADME Targets (SWAAT) that allows for the prediction of the variant effect based on structural properties. SWAAT annotates a panel of 36 ADME genes including 22 out of the 23 clinically important members identified by the PharmVar consortium. The workflow consists of a set of Python codes of which the execution is managed within Nextflow to annotate coding variants based on 37 criteria. SWAAT also includes an auxiliary workflow allowing a versatile use for genes other than ADME members. Our tool also includes a machine learning random forest binary classifier that showed an accuracy of 73%. Moreover, SWAAT outperformed six commonly used sequence-based variant prediction tools (PROVEAN, SIFT, PolyPhen-2, CADD, MetaSVM, and FATHMM) in terms of sensitivity and has comparable specificity. SWAAT is available as an open-source tool.
最近的基因组研究揭示了小群体内的遗传多样性在决定个体对药物反应方式方面的关键影响。最大的挑战之一是准确预测单核苷酸变异的影响，并获取相关信息，以便对遗传数据进行更好的功能解读。药理学重要蛋白质氨基酸序列变化时的不同构象情况可能会影响其稳定性和可塑性，进而可能改变与药物的相互作用。当前基于序列的注释方法获取这类信息的能力有限。受这些需求的推动，我们开发了用于注释药物代谢动力学（ADME）靶点的结构工作流程（SWAAT），它能够基于结构特性预测变异效应。SWAAT注释一组36个ADME基因，包括PharmVar联盟确定的23个临床重要成员中的22个。该工作流程由一组Python代码组成，其执行在Nextflow中进行管理，以便根据37个标准注释编码变异。SWAAT还包括一个辅助工作流程，允许对ADME成员以外的基因进行通用使用。我们的工具还包括一个机器学习随机森林二元分类器，其准确率为73%。此外 , 在敏感性方面，SWAAT优于六种常用的基于序列的变异预测工具（PROVEAN、SIFT、PolyPhen - 2、CADD、MetaSVM和FATHMM），并且具有相当的特异性。SWAAT作为一个开源工具可供使用。
