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通过机器学习将蛋白质聚合和结构稳定性联系起来,通过机器学习预测致病MYH7变体.

Ivan A Pyankov1, Marina A Kokorina2, Georgy N Rychkov3

  • 1Department of Chemical Medicine, Institute of Chemistry, St. Petersburg State University, St. Petersburg, Russian Federation; Chemical Engineering Center, ITMO University, St. Petersburg, Russian Federation.

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概括

导致髓存储肌病 (MSM) 的遗传变异破坏了MYH7蛋白的稳定. 一个新的机器学习工具RDSM-MYH7准确地预测了致病性MYH7突变,用于早期疾病诊断.

关键词:
人工智能的人工智能是人工智能.卷曲卷曲的卷曲卷曲的卷曲.基因组测序是指基因组的测序.这是一架MYH-7飞机.机器学习是机器学习.误解突变是一种错误的突变.肌酸氨酸储存肌病症 肌酸氨酸储存肌病症蛋白质结构 蛋白质结构

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科学领域:

  • 基因组学和生物信息学
  • 分子生物学分子生物学
  • 计算生物学 计算生物学

背景情况:

  • 基因测序数据正在迅速扩大,超过了引起疾病的变异的识别.
  • 计算方法对于将遗传突变与特定病理联系起来至关重要.

研究的目的:

  • 开发一种用于预测MYH7基因误解突变致病性的计算工具.
  • 为了改善遗传性髓存储肌病 (MSM) 的诊断.

主要方法:

  • 对MYH7α-螺旋式卷轴-卷轴域中引起疾病的变体的分析.
  • 开发一种机器学习模型 (RDSM-MYH7),集成蛋白质聚合和结构稳定特征.
  • 与现有的预测工具进行性能比较.

主要成果:

  • 与疾病相关的MYH7变体比非致病变体更多地破坏了α-螺旋式线圈-线圈域的稳定.
  • 致病变体聚集在MYH7卷轴-卷轴二次体的特定应变区域.
  • RDSM-MYH7实现了高性能 (F1=0.869,精度=0.875),超过了现有的工具.

结论:

  • 该RDSM-MYH7工具可以可靠地识别与存储肌病相关的致病性MYH7变体.
  • 临床实施RDSM-MYH7可以帮助早期诊断肌肉病和其他蛋白质储存疾病.
  • 了解蛋白质的展开和聚合是诊断遗传性蛋白质储存疾病的关键.