PTransIPs : Identification of Phosphorylation Sites Enhanced by Protein PLM Embeddings

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

3 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)3762-3771
Number of pages10
Journal / PublicationIEEE Journal of Biomedical and Health Informatics
Volume28
Issue number6
Online published14 Mar 2024
Publication statusPublished - Jun 2024

Abstract

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. © 2024 IEEE.

Research Area(s)

  • Amino acids, Biological system modeling, CNN, Data models, Phosphorylation sites, Protein engineering, protein pre-trained language model, Proteins, Training, Transformer, Vectors