高通量数据和建模揭示了转录因子蛋白合作性DNA结合机制的洞察力
Vincentius Martin1,2, Farica Zhuang1,2, Yuning Zhang2,3
1Department of Computer Science, Durham, NC 27708, USA.
Nucleic acids research
|October 27, 2023
概括
了解合作转录因子 (TF) 结合是基因调节的关键. 本研究开发了一种使用DNA库和机器学习准确建模TF合作性的新方法,揭示关键序列和空间因素.
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 (TF) 的合作DNA结合对于精确的真核生物基因调节至关重要.
- 靠近TF-binding网站并不能保证合作伙伴关系的绑定,并且对基础规则的理解仍然很差.
- 从现有的DNA结合数据中推断直接的TF-TF合作性是具有挑战性的.
研究的目的:
- 开发和验证一种用于建模TF合作性的新体外方法.
- 阐明特定TF之间的合作绑定机制的决定因素.
- 建立一个多功能平台,研究不同TFs的合作DNA结合模式.
主要方法:
- 创建包含数千个基因组序列的定制DNA库,其中包含潜在的合作TF结合事件.
- 在体外结合测试以评估这些图书馆内的TF相互作用.
- 应用机器学习模型来分析约束性数据和预测合作性.
主要成果:
- 证明基于DNA库的体外结合试验可以准确地模拟TF合作性.
- 确定了ETS1-ETS1合作性至关重要的ETS1-ETS1绑定站点之间的距离和方向.
- 表明ETS1-RUNX1合作相互作用表现出更大的灵活性,并且可以从结合点亲和力和序列/形状特征中预测.
结论:
- 综合实验和计算方法提供了TF合作性的准确模型.
- 特定的几何约束决定了ETS1的同质化,而ETS1-RUNX1的异质化更容易适应.
- 这种方法提供了一个广泛适用的工具,用于剖析跨各种转录因子的合作DNA结合机制.
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