预测转录因子突变体的DNA结合特异性,使用家族级生物物理可解释的机器学习
Shaoxun Liu1, Pilar Gomez-Alcala1, Christ Leemans1
1Department of Biological Sciences, Columbia University, New York, NY, USA.
bioRxiv : the preprint server for biology
|February 14, 2024
概括
这项研究引入了一种新的方法来预测转录因子 (TF) 的突变如何改变它们的DNA结合偏好. 这种方法准确地预测了TF突变体的结合自由能量变化,有助于疾病突变影响分析.
科学领域:
- 分子生物学分子生物学
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 转录因子 (TFs) 通过特定序列的DNA结合来调节细胞过程.
- 高通量测试和机器学习定义了TF分子识别和序列偏好.
- 在TF DNA结合域中与疾病相关的突变可以改变这些偏好.
研究的目的:
- 开发一种方法来预测TF DNA结合域中突变对序列偏好的影响.
- 准确预测TF突变体的结合自由能量转变.
主要方法:
- 在TF结构家族内开发了基准偏好变化的无参考四面体表示.
- 利用高通量体外结合试验数据和机器学习模型.
- 将该方法应用于基本螺旋-循环-螺旋 (bHLH) 和家庭主体TF家族.
主要成果:
- 证明了准确预测TF DNA结合的突变效应的可行性.
- 对TF突变的结合自由能量 (ΔΔΔG/RT) 的量化变化.
- 利用同类野生类型TFs的高质量的DNA结合模型.
结论:
- 开发的方法准确地预测了TF序列特异性的突变诱导的变化.
- 这种方法为了解与TF突变相关的疾病机制提供了一个强大的工具.
- 能够对TF-DNA相互作用进行生物物理可解释的预测.
关键词:
具有DNA结合的特异性基本的螺旋环螺旋环 (bHLH)机器学习是生物物理解释的机器学习.误解突变的功能影响.高吞吐量 SELEXEX 的高吞吐量家庭 家庭领域 家庭领域蛋白质结合微阵列 (PBM) 是一种微阵列.转录因子 (TF) 是一种转录因子.更多相关视频
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