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减少了11种致病性误解STXBP1/MUNC18-1变体的蛋白质稳定性,并改善了疾病预测

Timon André¹, Annemiek A van Berkel², Gurdeep Singh³

¹Heidelberg University Biochemistry Centre, Heidelberg, Germany.

Biological psychiatry

|March 15, 2024

概括

由于蛋白质不稳定性,STXBP1中的致病变体会导致严重的神经发育障碍. 一个新的机器学习工具,PRESR,准确地预测了变体的病原性,帮助诊断.

科学领域:

神经遗传学神经遗传学
分子生物学分子生物学
计算生物学计算生物学

背景情况:

在STXBP1 (Syntaxin Binding Protein 1) 中的致病变体是导致遗传神经发育障碍的主要原因.
目前对STXBP1相关疾病机制的理解尚不完整,限制了准确的病原性预测.
这项研究旨在建立统一的疾病概念,并提高STXBP1变种的预测准确性.

研究的目的:

定义与STXBP1相关的疾病背后的概括性疾病机制.
开发和验证一种新的计算工具,用于预测STXBP1变种的病原性.

主要方法:

测试了11种与疾病相关的和5种中性STXBP1变体在无细胞测试,异质细胞和初级神经元中.
使用凝过和Triton X-100不溶性评估蛋白质聚合.
开发了一种机器学习算法PRESR,该算法集成了序列和结构特征,用于病原性预测,并对231个变体进行了训练.

主要成果:

与疾病相关的STXBP1变种显示蛋白质水平降低和温度稳定性受损,变质接近体温.
大多数与疾病相关的变体在体外中断了SNARE介导的膜融合.
在试验室或神经元中没有观察到任何变异的聚合.
该 PRESR 算法显著优于现有的预测工具 (MCC = 0.71 与<0.55).

结论:

内在蛋白质不稳定性被确定为STXBP1相关疾病的主要,可概括的原因.
结合特定蛋白质的正统学和3D结构数据可以提高疾病预测的准确性.
PRESR是一种经过验证的,公开可访问的诊断工具,用于STXBP1变体的评估.

关键词:

聚合聚合是一种聚合.细胞外 (exocytosis) 是一种细胞外.在 MUNC18-1-1 中.机器学习是机器学习.蛋白质的稳定性蛋白质的稳定性合成素是一种合成素.

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