Pathogenic variants in /MUNC18-1 cause severe encephalopathies that are among the most common in genetic neurodevelopmental disorders. Different molecular disease mechanisms have been proposed, and pathogenicity prediction is limited. In this study, we aimed to define a generalized disease concept for STXBP1-related disorders and improve prediction. A cohort of 11 disease-associated and 5 neutral variants (detected in healthy individuals) were tested in 3 cell-free assays and in heterologous cells and primary neurons. Protein aggregation was tested using gel filtration and Triton X-100 insolubility. PRESR (predicting STXBP1-related disorder), a machine learning algorithm that uses both sequence- and 3-dimensional structure–based features, was developed to improve pathogenicity prediction using 231 known disease-associated variants and comparison to our experimental data. Disease-associated variants, but none of the neutral variants, produced reduced protein levels. Cell-free assays demonstrated directly that disease-associated variants have reduced thermostability, with most variants denaturing around body temperature. In addition, most disease-associated variants impaired SNARE-mediated membrane fusion in a reconstituted assay. Aggregation/insolubility was observed for none of the variants in vitro or in neurons. PRESR outperformed existing tools substantially: Matthews correlation coefficient = 0.71 versus <0.55. These data establish intrinsic protein instability as the generalizable, primary cause for STXBP1-related disorders and show that protein-specific ortholog and 3-dimensional information improve disease prediction. PRESR is a publicly available diagnostic tool.

中文翻译：

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降低 11 种致病性错义 STXBP1/MUNC18-1 变体的蛋白质稳定性并改善疾病预测

/Munc18-1 的致病性变异会导致严重的脑病，这是遗传性神经发育障碍中最常见的疾病之一。已经提出了不同的分子疾病机制，并且致病性预测是有限的。本研究旨在定义 STXBP1 相关疾病的广义疾病概念并改进预测。在三项无细胞检测以及异源细胞和原代神经元中测试了由 11 种疾病相关变异和 5 种中性变异（在健康个体中检测到）组成的队列。使用凝胶过滤和 Triton-x-100 不溶性测试蛋白质聚集。开发了一种同时使用基于序列和 3D 结构的特征的机器学习算法 (PRESR)，以使用 231 种已知的疾病相关变异并与我们的实验数据进行比较来改进致病性预测。与疾病相关，但没有一个中性变体产生蛋白质水平降低。无细胞测定直接证明，与疾病相关的变异降低了热稳定性，大多数变异在体温附近变性。此外，在重构测定中，大多数与疾病相关的变异都会损害 SNARE 介导的膜融合。没有观察到任何变体或神经元中的聚集/不溶性。PRESR 的性能显着优于现有工具：Matthews 相关系数 = 0.71 对比 <0.55。这些数据确定了内在蛋白质不稳定性是 STXBP1 相关疾病的普遍主要原因，并表明蛋白质特异性直向同源物和 3D 信息可以改善疾病预测。PRESR 是一个公开可用的诊断工具 (PRESR.russelllab.org)。