使用人工智能预测的蛋白质结构作为参考来预测瘤抑制性乳腺癌基因中的功能丧失活性
Rohan Gnanaolivu1, Steven N Hart1,2
1Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
Computational and structural biotechnology journal
|October 21, 2024
概括
生成性AI预测的蛋白质结构改善了乳腺癌基因的功能丧失 (LOF) 变异分类. AlphaMissense在预测误解变异的LOF活动方面表现出卓越的表现.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 瘤抑制基因BRCA1,BRCA2,PALB2和RAD51C中的错误变异通常缺乏功能丧失 (LOF) 分类,阻碍了临床决策.
- 变异的稀有性需要依赖于in silico预测方法进行分类.
- 高分辨率的蛋白质结构对于准确的稳定性预测至关重要,但实验结构通常是不可用的.
研究的目的:
- 探索使用生成AI来预测高分辨率蛋白质结构.
- 评估蛋白稳定性和误解预测方法,以预测BRCA1,BRCA2,PALB2和RAD51C的有序区域中的LOF活动.
- 建立可靠的数据集,使用同质性重组DNA修复 (HDR) 试验和ClinVar分类进行方法评估.
主要方法:
- 利用生成性AI (AlphaFold2) 来预测BRCA1和BRCA2域的蛋白质结构.
- 在预测和实验结构上使用蛋白质稳定性预测工具 (FoldX).
- 评估了来自dbNSFP v4.7数据库的各种in silico误解预测方法.
- 从HDR测试和ClinVar数据库中对功能分类进行验证的预测.
主要成果:
- 使用FoldX稳定性分析预测的AlphaFold2结构在预测错觉变异的LOF活动时优于实验衍生结构.
- 在所有测试的in silico错误感预测器中,AlphaMissense实现了最高的性能,在四个基因中平均AUC为0.890.
- 仅仅蛋白质稳定性并没有超过专门的in silico误解预测器.
结论:
- 生成性AI预测的蛋白质结构增强了从关键乳腺癌基因中的误解变异中LOF活动的评估.
- AlphaMissense被认为是预测BRCA1,BRCA2,PALB2和RAD51C错误感变异中的LOF活动的首屈一指的in silico工具.
- 该研究在GitHub上提供了一个免费可访问的代码库,用于更广泛的研究应用.
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