Next-generation genome sequencing has revolutionized genetic testing, identifying numerous rare disease-associated gene variants. However, to impute pathogenicity, computational approaches remain inadequate and functional testing of gene variant is required to provide the highest level of evidence. The emergence of AlphaFold2 has transformed the field of protein structure determination, and here we outline a strategy that leverages predicted protein structure to enhance genetic variant classification. We used the gene as a case study due to its clinical relevance, its critical role in cleft lip/palate malformation, and the availability of experimental data on the pathogenicity of gene variants through phenotype rescue experiments in zebrafish. We compared results from over 30 pathogenicity prediction tools on 37 missense variants. lacks an experimentally derived structure, so we used predicted structures to explore associations between mutational clustering and pathogenicity. We found that among these variants, 19 of 37 were unanimously predicted as deleterious by computational tools. Comparing predictions with experimental findings, 12 variants predicted as pathogenic were experimentally determined as benign. Even with the recently published AlphaMissense model, 15/18 (83%) of the predicted pathogenic variants were misclassified as benign. In comparison, mapping variants to the protein revealed deleterious mutation clusters around the protein binding domain, whereas N-terminal variants tend to be benign, suggesting the importance of structural information in determining pathogenicity of mutations in this gene. In conclusion, incorporating gene-specific structural features of known pathogenic/benign mutations may provide meaningful insights into pathogenicity predictions in a gene-specific manner and facilitate the interpretation of variant pathogenicity.

中文翻译：

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通过预测蛋白质结构进行遗传变异分类：IRF6 案例研究

下一代基因组测序彻底改变了基因测试，识别出许多与罕见疾病相关的基因变异。然而，为了估算致病性，计算方法仍然不足，需要对基因变异进行功能测试以提供最高水平的证据。AlphaFold2 的出现改变了蛋白质结构测定领域，在这里我们概述了一种利用预测蛋白质结构来增强遗传变异分类的策略。我们使用该基因作为案例研究，因为它的临床相关性、它在唇裂/腭裂畸形中的关键作用，以及通过斑马鱼表型拯救实验获得的基因变异致病性实验数据的可用性。我们比较了 30 多种致病性预测工具对 37 种错义变异的结果。缺乏实验衍生的结构，因此我们使用预测的结构来探索突变聚类和致病性之间的关联。我们发现，在这些变体中，计算工具一致预测 37 个变体中有 19 个是有害的。将预测与实验结果进行比较，12 个预测为致病性的变异被实验确定为良性。即使使用最近发布的 AlphaMissense 模型，15/18 (83%) 的预测致病变异也被错误分类为良性。相比之下，将变体映射到蛋白质上揭示了蛋白质结合域周围的有害突变簇，而N末端变体往往是良性的，这表明结构信息在确定该基因突变的致病性中的重要性。总之，结合已知致病性/良性突变的基因特异性结构特征可以以基因特异性方式为致病性预测提供有意义的见解，并促进变异致病性的解释。