在后测序时代的结构和功能预测,评估和验证
Chang Li1,2, Yixuan Luo3, Yibo Xie4
1Clinical Biobank, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Computational and structural biotechnology journal
|January 15, 2024
概括
解释具有不确定的意义 (VUS) 的遗传变异具有挑战性. 人工智能 (AI) 提供高效的in silico预测工具,特别是使用蛋白质结构,以加快VUS分类.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 基因组测序数据的快速增长导致了大量具有不确定的意义的遗传变异 (VUS).
- 分类VUS对于遗传诊断至关重要,但由于大量的变异和有限的实验数据,仍然是一个重大挑战.
- 目前的实验方法不足以解决发现的VUS的规模.
研究的目的:
- 审查基于人工智能 (AI) 的误解变体预测方法的当前状态.
- 突出使用基于蛋白质结构的AI模型用于VUS解释的潜力和挑战.
- 强调在测序后的时代需要先进的in silico功能预测器.
主要方法:
- 对VUS预测中的AI应用现有文献的审查.
- 专注于利用蛋白质结构信息的AI模型.
- 讨论用于变量效应预测的计算方法.
主要成果:
- 人工智能在预测遗传变异的功能影响方面表现出高效率和准确性.
- 基于蛋白质结构的AI模型显示了改善VUS分类的前景.
- 在已识别的VUS和实验验证的变体数量之间存在很大的差距.
结论:
- 人工智能是加速VUS解释的强大工具.
- 基于蛋白质结构的AI预测为解决VUS挑战提供了一个有希望的途径.
- 迫切需要进一步开发和验证in silico VUS预测器.
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