MutPNI:一个统一的模型架构,用于准确预测突变对蛋白质核酸相互作用的影响
Zihao Yan1, Fang Ge2, Ying Zhang1
1School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Journal of chemical information and modeling
|July 28, 2025
概括
预测误解突变对蛋白-DNA和蛋白-RNA结合的影响对于了解疾病至关重要. 使用蛋白质语言模型和结构数据的新模型MutPNI有效地预测了这些结合亲和力变化.
科学领域:
- 基因组学就是基因组学.
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
背景情况:
- 在DNA和RNA结合蛋白中的错误突变可以破坏相互作用并导致疾病.
- 预测突变对结合亲和力的影响的现有方法是低效和分散的.
研究的目的:
- 介绍MutPNI,一种新的一般回归模型,用于预测DNA和RNA结合蛋白中的误解突变效应.
- 为分析蛋白质与核酸相互作用的突变影响提供一个强大且计算效率高的平台.
主要方法:
- 从ESM-2和ProtT5蛋白质语言模型中集成的功能嵌入.
- 来自突变蛋白质结构的内置能量项.
- 训练了一个基于变压器的编码器进行预测.
主要成果:
- 通过0.661的DNA和0.701的RNA的皮尔森相关系数 (PCC) 实现了高的预测准确性.
- 展示了DNA和RNA结合蛋白的统一模型架构,突出了共同和独特的特征.
- 验证了模型的计算效率,用于大规模的生物数据分析.
结论:
- MutPNI在预测蛋白质与核酸结合的突变效应方面取得了重大进展.
- 该模型的效率和准确性支持其在疾病研究和药物发现中的应用.
- 公共可用的网络服务器和数据集促进了更广泛的研究使用.
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