PTransIPs: Identification of Phosphorylation Sites Enhanced by Protein PLM Embeddings

鉴定(生物学) 过度拟合 磷酸化 卷积神经网络 人工智能 深度学习 计算机科学 蛋白质磷酸化 编码(集合论) 机器学习 计算生物学 人工神经网络 生物 蛋白激酶A 生物化学 程序设计语言 植物 集合(抽象数据类型)
作者
Ziyang Xu,Haitian Zhong,Bingrui He,Xueying Wang,Tianchi Lu
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:28 (6): 3762-3771 被引量:4
标识
DOI:10.1109/jbhi.2024.3377362
摘要

Phosphorylation is pivotal in numerous fundamental cellular processes and plays a significant role in the onset and progression of various diseases. The accurate identification of these phosphorylation sites is crucial for unraveling the molecular mechanisms within cells and during viral infections, potentially leading to the discovery of novel therapeutic targets. In this study, we develop PTransIPs, a new deep learning framework for the identification of phosphorylation sites. Independent testing results demonstrate that PTransIPs outperforms existing state-of-the-art (SOTA) methods, achieving AUCs of 0.9232 and 0.9660 for the identification of phosphorylated S/T and Y sites, respectively. PTransIPs contributes from three aspects. 1) PTransIPs is the first to apply protein pre-trained language model (PLM) embeddings to this task. It utilizes ProtTrans and EMBER2 to extract sequence and structure embeddings, respectively, as additional inputs into the model, effectively addressing issues of dataset size and overfitting, thus enhancing model performance; 2) PTransIPs is based on Transformer architecture, optimized through the integration of convolutional neural networks and TIM loss function, providing practical insights for model design and training; 3) The encoding of amino acids in PTransIPs enables it to serve as a universal framework for other peptide bioactivity tasks, with its excellent performance shown in extended experiments of this paper. Our code, data and models are publicly available at https://github.com/StatXzy7/PTransIPs .
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