DeepBovC2H2-ZF:深度学习引导的预测和C2H2指转录因子的分子动力学验证
Bharati Pandey1, Manbir Singh2
1ICAR-National Dairy Research Institute (NDRI), Karnal, Haryana, India.
Journal, genetic engineering & biotechnology
|December 12, 2025
概括
我们开发了DeepBovC2H2-ZF,这是一个深度学习工具,用于识别C2H2指 (ZF) 转录因子 (TF) 在牛类牲畜中. 这个工具有助于理解与经济重要特征相关的基因.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 分子生物学分子生物学
背景情况:
- C2H2指 (ZF) 转录因子 (TFs) 是发展和应激反应的关键调节者.
- 在Bovidae牲畜中,这些TFs影响经济上重要的特征,如生长,繁殖和抗病能力.
- 由于缺乏专门的计算工具,在牛类动物中对C2H2-ZF TFs的全基因组识别受到阻碍.
研究的目的:
- 开发一个计算框架,用于准确的全基因组识别C2H2-ZFTFs.
- 利用深度学习来预测C2H2-ZF TFs,仅基于蛋白质序列数据.
主要方法:
- 开发了DeepBovC2H2-ZF,这是一个利用蛋白质序列信息的深度学习框架.
- 在经过验证的C2H2-ZF和非C2H2-ZFTF的精选数据集上训练模型.
- 采用序列衍生功能,捕获独特的域名签名以进行预测.
主要成果:
- DeepBovC2H2-ZF表现出高的预测准确度,灵敏度和特异性.
- 该框架在识别C2H2-ZFTF方面表现优于传统的机器学习模型.
- 使用分子对接和分子动力学模拟验证了KLF4蛋白质预测,证实了结构稳定性和DNA结合亲和力.
结论:
- DeepBovC2H2-ZF是一个可靠的工具,用于准确识别C2H2-ZFTFs.
- 该框架通过允许精确的TF识别,促进了牲畜中的功能基因组学研究.
- 这一进步通过更好地了解基因调节,支持对 Bovidae 中经济重要特征的研究.
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