在蛋白质激酶中区分激活,失活和抵抗变体
Gurdeep Singh1, Torsten Schmenger1, Juan Carlos Gonzalez-Sanchez1
1BioQuant & Biochemistry Center, Heidelberg University, Im Neuenheimer Feld 267, Heidelberg, 69121, Germany.
Genome medicine
|October 29, 2025
概括
识别增加蛋白质功能的激酶变体是个性化医学的关键. 我们的新计算方法准确地预测了激活,关闭和抗性变体,帮助临床决策.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 生物化学 生化学
背景情况:
- 对临床遗传学和个性化医学而言,区分蛋白质激酶中的功能增益 (GOF) 和功能丧失 (LOF) 遗传变异对于临床遗传学和个性化医学至关重要.
- 与LOF变异不同的是,GOF变异通常可以用抑制剂治疗,这凸显了需要精确的变异分类的需要.
- 目前的计算工具主要预测变异性病原性 (有害与良性),而不会提供特定的功能洞察力.
研究的目的:
- 开发和验证数据驱动的计算方法,将激酶变异区分为激活型 (GOF),失活型 (LOF) 和电阻型.
- 为遗传变异提供功能性见解,不仅仅是简单的病原性预测.
- 为了方便识别可治疗向的激酶变体.
主要方法:
- 策划了一组数据集,包含441个激酶的2505个变异,分为激活,失活,抗性或中性.
- 利用序列,进化和结构特征来训练用于变体分类的机器学习模型.
- 实现了高预测性能,平均曲线下面积 (AUC) 为0.941.
- 使用基于细胞的测试 (过度表达,基因表达,生物化学测试) 进行实验验证的预测.
主要成果:
- 观察到激活癌症基因组突变的显著丰富,以及遗传性疾病中的失活变异.
- 在癌症样本中实验验验证了预测的激活变体,观察到激酶活性增加.
- 通过实验证明,MAP2K3中预测的变异导致线粒体功能下降,与删除的效果相反.
- 开发了一个在线应用程序,用于分析酶域变体和探索已知的附近变体.
结论:
- 开发的预测因子,结合快速的实验验证,为及时识别激活酶变体提供了可行的策略.
- 这种方法可以显著帮助做出临床决策的患者与酶相关的疾病.
- 该方法通过使遗传变异的快速功能性特征能够推进个性化医学的潜力.
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