用大规模突变扫描和深度学习预测和设计转录抑制器域
bioRxiv : the preprint server for biology
|October 10, 2024
概括
了解遗传变异如何影响转录抑制器域 (RDs) 是至关重要的. 这项研究绘制了变异活性图,确定了与疾病相关的突变,并开发了一个深度学习模型 (TENet) 来预测和设计调节性蛋白质功能.
科学领域:
- 遗传学 遗传学 是一个
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 调节性蛋白利用多种不同的抑制器域 (RD) 进行精确的转录控制.
- 对于序列变化如何影响RD功能活动的理解有限.
研究的目的:
- 为了全面地绘制RD序列变体的功能格局.
- 为了识别与疾病相关的变异和关键的监管动机.
- 开发和验证一种深度学习模型,用于预测研发和开发活动,并使蛋白质设计成为可能.
主要方法:
- 在人类细胞中生成了115,000个RD变异的高通量突变扫描数据集.
- 确定了具有改变抑制器功能的临床变异 (例如,TWIST1,MECP2).
- 开发了深度学习模型TENet,集成序列,结构和生物化学数据来预测抑制器活动.
主要成果:
- 确定了成千上万的变体,这些变体具有抑制器功能的增加或损失.
- 简短的线性相互作用动机 (SLiMs) 关键的抑制在无序的RDs被注释.
- TENet准确地预测了压缩器活动,并通过定向进化实现了RD功能的实验调整.
结论:
- 这项工作为了解RD变体功能和优先考虑临床变体提供了宝贵的资源.
- 开发的深度学习模型 (TENet) 为预测调控蛋白活性提供了强大的工具.
- 这些发现为设计具有定制功能的合成调节蛋白的策略提供了信息.
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