一种基于蛋白质语言模型的蛋白质-DNA结合点的新预测方法,其融合特征与SE连接金字塔网络和集体学习
Chenrui Zhang1, Jingqing Jiang1, Haiyan Zhao1
1College of Computer Science and Technology, Inner Mongolia Minzu University, Inner Mongolia, China.
BMC genomics
|October 31, 2025
概括
预测蛋白质-DNA结合点对于理解生物活动至关重要. 新的进化规模建模-SE-连接金字塔 (ESM-SECP) 框架通过结合序列特征和同质性来准确识别这些网站,改进了传统方法.
科学领域:
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 蛋白质-DNA相互作用是基因表达和调节的基础.
- 精确预测蛋白质上的DNA结合部位对于促进生物学理解至关重要.
- 预测蛋白质-DNA结合位点的现有方法存在局限性.
研究的目的:
- 开发一种用于预测蛋白质-DNA结合位点的新框架.
- 提高DNA结合部位预测的准确性和可靠性.
- 整合基于序列特征和序列同质性的预测方法.
主要方法:
- 提出了进化规模建模-SE-连接金字塔 (ESM-SECP) 框架.
- 利用ESM-2蛋白语言模型嵌入和PSI-BLAST进化保存用于序列特征预测.
- 采用多头注意力机制和SE连接金字塔 (SECP) 网络.
- 集成了一种基于序列同质性的序列模板方法.
- 应用集体学习将两个预测方法结合起来.
主要成果:
- 与传统方法相比,ESM-SECP在TE46和TE129数据集上表现优越.
- 该框架在预测DNA结合残留物方面取得了很高的准确性.
- 合体学习有效地提高了整体预测能力.
结论:
- 该ESM-SECP框架为蛋白质-DNA结合部位预测提供了出色的性能.
- 多种预测策略的整合提高了准确性.
- 这种方法推进了计算生物学和分子相互作用的领域.
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