Predicting functional effect of missense variants using graph attention neural networks.
Predicting functional effect of missense variants using graph attention neural networks.
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DOI：
10.1038/s42256-022-00561-w
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发表时间：
2022-11
影响因子：
23.8
通讯作者：
Shen, Yufeng
中科院分区：
文献类型：
--
作者：
Zhang, Haicang;Xu, Michelle S.;Fan, Xiao;Chung, Wendy K.;Shen, Yufeng
准确预测破坏性错义变异对于解释基因组序列至关重要。虽然已经开发了许多方法，但它们的性能受到限制。机器学习的最新进展和大规模人群基因组测序数据的可用性为大幅改善计算预测提供了新的机会。在这里，我们描述了图形的错义变异致病性预测（gMVP），一种新的方法，基于图注意力神经网络。它的主要组成部分是一个图，其中的节点捕获氨基酸的预测特征，并通过协同进化强度加权边缘，从而有效地汇集来自局部蛋白质背景和功能相关远端位置的信息。对深度突变扫描数据的评估表明，gMVP在识别TP53、PTEN、BRCA 1和MSH2中的破坏性变体方面优于其他已发表的方法。此外，它实现了最好的分离从头错义变异神经发育障碍的情况下，从那些在控制。最后，该模型支持迁移学习，以优化钠和钙通道的功能获得和丧失预测。总之，我们证明了gMVP可以改善临床检测和遗传研究中错义变异的解释。
Accurate prediction of damaging missense variants is critically important for interpreting a genome sequence. Although many methods have been developed, their performance has been limited. Recent advances in machine learning and the availability of large-scale population genomic sequencing data provide new opportunities to considerably improve computational predictions. Here we describe the graphical missense variant pathogenicity predictor (gMVP), a new method based on graph attention neural networks. Its main component is a graph with nodes that capture predictive features of amino acids and edges weighted by co-evolution strength, enabling effective pooling of information from the local protein context and functionally correlated distal positions. Evaluation of deep mutational scan data shows that gMVP outperforms other published methods in identifying damaging variants in TP53, PTEN, BRCA1 and MSH2. Furthermore, it achieves the best separation of de novo missense variants in neuro developmental disorder cases from those in controls. Finally, the model supports transfer learning to optimize gain- and loss-of-function predictions in sodium and calcium channels. In summary, we demonstrate that gMVP can improve interpretation of missense variants in clinical testing and genetic studies.
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影响因子：
64.8
作者：
Findlay GM;Daza RM;Martin B;Zhang MD;Leith AP;Gasperini M;Janizek JD;Huang X;Starita LM;Shendure J
通讯作者：
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影响因子：
64.8
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28.2
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通讯作者：
Taylor BS