PIPENN-EMB组合网和蛋白质嵌入将蛋白质接口预测推广到超出同质性的范围
David P G Thomas1, Carlos M Garcia Fernandez1, Reza Haydarlou2
1Department of Computer Science, Vrije Universiteit Amsterdam, 1081HV, Amsterdam, The Netherlands.
Scientific reports
|February 5, 2025
概括
本研究介绍了PIPENN-EMB,这是一种深度学习模型,使用先进的蛋白质语言模型嵌入来增强蛋白质相互作用预测. 它显著优于以前的方法,特别是对于序列相似性较低的蛋白质.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 结构生物学 结构生物学
背景情况:
- 蛋白质相互作用是生物过程和疾病的基础.
- 预测蛋白质-蛋白质接口是一项具有挑战性但至关重要的任务.
- 深度学习模型在推进接口预测准确性方面表现有前途.
研究的目的:
- 评估PIPENN-EMB的性能,这是一个用于预测蛋白相互作用接口的新型深度学习模型.
- 评估ProtT5-XL蛋白质语言模型嵌入对预测准确性的贡献.
- 为了证明该模型对具有低序列同质性的蛋白质的概括性.
主要方法:
- 在PIPENN深度学习架构中使用ProtT5-XL嵌入式.
- 在BIO_DL_TE数据集上对之前的PIPENN模型进行了比较分析.
- 评估了ZK448数据集的性能,并进行了废除研究.
- 测试了对不同程度的序列相同性到训练数据的蛋白质的概括性.
主要成果:
- 在BIO_DL_TE测试组中,PIPENN-EMB在BIO_DL_TE测试组中实现了0.313的马修相关系数 (MCC) 和0.800的接收器操作特征曲线下的面积 (AUROC),超过了PIPENN的0.249MCC和0.755AUROC.
- 在ZK448数据集上展示了最先进的性能,用于蛋白质-蛋白质接口预测.
- 展示了25种来自Mycobacterium tuberculosis的耐药性相关蛋白质的成功预测.
- 向远程同类体 (蛋白质与训练数据相同度<30%甚至<15%) 展现出强大的概括性,保持稳定的AUROC.
结论:
- 通过结合先进的蛋白质语言模型嵌入,PIPENN-EMB显著提高了蛋白质相互作用接口预测的准确性.
- 该模型显示出卓越的性能和增强的通用性,特别是在培训数据集中缺乏显著同质性的蛋白质.
- 这种方法代表了计算生物学在理解蛋白质功能和疾病机制方面的实质性进步.
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