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使用 AlphaMissense 进行精确的全蛋白质错义变异效应预测。

Accurate proteome-wide missense variant effect prediction with AlphaMissense.

机构信息

Google DeepMind, London, UK.

出版信息

Science. 2023 Sep 22;381(6664):eadg7492. doi: 10.1126/science.adg7492.

Abstract

The vast majority of missense variants observed in the human genome are of unknown clinical significance. We present AlphaMissense, an adaptation of AlphaFold fine-tuned on human and primate variant population frequency databases to predict missense variant pathogenicity. By combining structural context and evolutionary conservation, our model achieves state-of-the-art results across a wide range of genetic and experimental benchmarks, all without explicitly training on such data. The average pathogenicity score of genes is also predictive for their cell essentiality, capable of identifying short essential genes that existing statistical approaches are underpowered to detect. As a resource to the community, we provide a database of predictions for all possible human single amino acid substitutions and classify 89% of missense variants as either likely benign or likely pathogenic.

摘要

在人类基因组中观察到的绝大多数错义变体的临床意义未知。我们提出了 AlphaMissense,这是对经过人类和灵长类变异人群频率数据库微调的 AlphaFold 的改编,用于预测错义变体的致病性。通过结合结构背景和进化保守性,我们的模型在广泛的遗传和实验基准测试中取得了最先进的结果,所有这些都没有在这些数据上进行显式训练。基因的平均致病性评分也可预测其细胞必需性,能够识别出现有统计方法无法检测到的短必需基因。作为社区的资源,我们提供了一个包含所有可能的人类单个氨基酸替换的预测数据库,并将 89%的错义变体分类为可能良性或可能致病性。

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