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Large-scale in vitro functional testing and novel variant scoring via protein modeling provide insights into alkaline phosphatase activity in hypophosphatasia.
Human Mutation ( IF 3.3 ) Pub Date : 2020-03-11 , DOI: 10.1002/humu.24010
Guillermo Del Angel 1 , John Reynders 1 , Christopher Negron 2 , Thomas Steinbrecher 2 , Etienne Mornet 3
Affiliation  

Hypophosphatasia (HPP) is a rare metabolic disorder characterized by low tissue‐nonspecific alkaline phosphatase (TNSALP) typically caused by ALPL gene mutations. HPP is heterogeneous, with clinical presentation correlating with residual TNSALP activity and/or dominant‐negative effects (DNE). We measured residual activity and DNE for 155 ALPL variants by transient transfection and TNSALP enzymatic activity measurement. Ninety variants showed low residual activity and 24 showed DNE. These results encompass all missense variants with carrier frequencies above 1/25,000 from the Genome Aggregation Database. We used resulting data as a reference to develop a new computational algorithm that scores ALPL missense variants and predicts high/low TNSALP enzymatic activity. Our approach measures the effects of amino acid changes on TNSALP dimer stability with a physics‐based implicit solvent energy model. We predict mutation deleteriousness with high specificity, achieving a true‐positive rate of 0.63 with false‐positive rate of 0, with an area under receiver operating curve (AUC) of 0.9, better than all in silico predictors tested. Combining this algorithm with other in silico approaches can further increase performance, reaching an AUC of 0.94. This study expands our understanding of HPP heterogeneity and genotype/phenotype relationships with the aim of improving clinical ALPL variant interpretation.

中文翻译:

通过蛋白质建模进行的大规模体外功能测试和新变体评分提供了对低磷酸酯酶症中碱性磷酸酶活性的见解。

低磷酸酯酶症 (HPP) 是一种罕见的代谢疾病,其特征是组织非特异性碱性磷酸酶 (TNSALP) 低,通常由ALPL基因突变引起。HPP 是异质性的,临床表现与残留的 TNSALP 活性和/或显性负效应 (DNE) 相关。我们通过瞬时转染和 TNSALP 酶活性测量测量了 155 个ALPL变体的残留活性和 DNE 。90 个变体显示低残留活性,24 个显示 DNE。这些结果包括来自基因组聚合数据库的载波频率高于 1/25,000 的所有错义变体。我们使用结果数据作为参考来开发一种对ALPL评分的新计算算法错义变体并预测高/低 TNSALP 酶活性。我们的方法使用基于物理的隐式溶剂能量模型测量氨基酸变化对 TNSALP 二聚体稳定性的影响。我们以高特异性预测突变有害性,真阳性率为 0.63,假阳性率为 0,受试者工作曲线下面积 (AUC) 为 0.9,优于所有测试的计算机预测因子。将此算法与其他 in silico 方法相结合可以进一步提高性能,达到 0.94 的 AUC。本研究扩展了我们对 HPP 异质性和基因型/表型关系的理解,旨在改善临床ALPL变异的解释。
更新日期:2020-03-11
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