SNPeffect 5.0:使用AlphaFold模型从下一代测序数据中提取的蛋白质编码变体的大规模结构表型
Kobe Janssen1,2, Ramon Duran-Romaña1,2, Guy Bottu3
1Switch Laboratory, VIB-KU Leuven Center for Brain and Disease Research, Herestraat 49, 3000, Leuven, Belgium.
BMC bioinformatics
|July 18, 2023
概括
这项研究引入了一个自动化的生物信息学管道,以分析误解遗传变异对蛋白质稳定性和聚合的影响. 该工具集成了多个预测器和结构数据库,用于从下一代测序数据进行全面分析.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 下一代测序 (NGS) 产生了大量的遗传改变,包括改变氨基酸的错误变异.
- 这些变异可以破坏蛋白质的稳定,增加错误折叠和聚合的风险.
- 现有的用于变异效应预测的工具缺乏从NGS数据开始的统一管道.
研究的目的:
- 开发一个自动化和并行生物信息学管道来分析误解变异的影响.
- 评估对蛋白质聚合倾向和结构稳定性的影响.
- 直接从测序数据中实现结构性表型.
主要方法:
- 输入:来自NGS数据的变量调用格式 (VCF) 文件.
- 分析:使用FoldX (稳定性),TANGO和WALTZ (聚合) 的集成管道.
- 结构数据:AlphaFold蛋白质结构数据库集成,以提高覆盖范围.
主要成果:
- 该管道自动化了对蛋白质结构和功能的误解变异效应的分析.
- 它利用AlphaFold数据库进行广泛的结构稳定性预测.
- 人类蛋白质组分析样本特定的稳定性和损伤是启用.
结论:
- 介绍了一个新的生物信息学管道,用于从测序数据中进行结构性表型.
- 该管道集成了FoldX,TANGO,WALTZ和AlphaFold数据库,用于全面分析.
- 该管道免费提供给学术用户,需要计算机集群才能运行.
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