使用分子碎片描述器预测致病性单氨基酸替代
Anton Zadorozhny1, Anton Smirnov1, Dmitry Filimonov2
1Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow 117513, Russia.
Bioinformatics (Oxford, England)
|August 3, 2023
概括
这项研究引入了一种新的化学信息学方法,使用蛋白质结构公式来预测遗传变异的致病性. 该方法显示出有前途的结果,特别是对于BRCA1和BRCA2等特定蛋白质,其性能优于现有的工具.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
- 分子遗传学 分子遗传学
背景情况:
- 下一代测序 (NGS) 能够检测罕见的遗传变异.
- 对于氨基酸替代物的现有病原性预测工具具有局限性,并且经常产生矛盾的结果.
- 需要改进的方法来准确地分类遗传变异.
研究的目的:
- 为了研究化学信息学方法的有效性,使用质结构公式进行变异性致病性评估.
- 描述使用结构公式而不是单个字母代码的氨基酸残留替代.
- 将新方法的性能与现有的预测工具进行比较.
主要方法:
- 利用了蛋白质特定数据和以原子为中心的多层次原子邻近 (MNA) 描述器.
- 开发了一个基于MNA描述符的天真贝叶斯分类器.
- 在ClinVar和humsavar数据集上训练并验证了10种蛋白质的模型,与11种已建立的预测工具进行比较.
主要成果:
- 基于MNA的方法在不同蛋白质中显示出可变但具有竞争力的精度 (AUC:0.631-0.993;MCC:0.191-0.891).
- 性能与个人和共识预测工具相似.
- 该方法对BRCA1,BRCA2,COL1A2和RYR1表现出色,有效地捕捉了氨基酸替代的影响.
结论:
- 通过结构公式描述氨基酸替代是一种可行的化学信息学方法,用于病原性预测.
- 基于MNA的方法提供了一个有价值的替代方案,特别是在它卓越的特定基因.
- 这种方法提高了变异分类的准确性,有助于基因诊断.
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