Protein-protein interaction and site prediction using transfer learning

文献类型： 外文期刊

第一作者： Liu, Tuoyu

作者： Liu, Tuoyu;Ren, Xiaopu;Liu, Bo;Wu, Ningfeng;Gao, Han;Xu, Guoshun;Luo, Huiying;Wang, Yuan;Tu, Tao;Yao, Bin;Huang, Huoqing;Tian, Jian;Teng, Yue

作者机构：

关键词： protein-protein interaction; transformer; PPI site; transfer learning; BERT

期刊名称：BRIEFINGS IN BIOINFORMATICS （ 影响因子：9.5； 五年影响因子：10.6 ）

ISSN： 1467-5463

年卷期： 2023 年 24 卷 6 期

页码：

收录情况： SCI

摘要： The advanced language models have enabled us to recognize protein-protein interactions (PPIs) and interaction sites using protein sequences or structures. Here, we trained the MindSpore ProteinBERT (MP-BERT) model, a Bidirectional Encoder Representation from Transformers, using protein pairs as inputs, making it suitable for identifying PPIs and their respective interaction sites. The pretrained model (MP-BERT) was fine-tuned as MPB-PPI (MP-BERT on PPI) and demonstrated its superiority over the state-of-the-art models on diverse benchmark datasets for predicting PPIs. Moreover, the model's capability to recognize PPIs among various organisms was evaluated on multiple organisms. An amalgamated organism model was designed, exhibiting a high level of generalization across the majority of organisms and attaining an accuracy of 92.65%. The model was also customized to predict interaction site propensity by fine-tuning it with PPI site data as MPB-PPISP. Our method facilitates the prediction of both PPIs and their interaction sites, thereby illustrating the potency of transfer learning in dealing with the protein pair task.

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