Work

DeepPhosPPI: a deep learning framework with attention-CNN and transformer for predicting phosphorylation effects on protein–protein interactions

Year: 2025

Type: article

Abstract: Protein phosphorylation regulates protein function and cellular signaling pathways, and is strongly associated with diseases, including neurodegenerative disorders and cancer. Phosphorylation plays a critical role in regulating protein activity and cellular signaling by modulating protein-protein interactions (PPIs). It alters binding affinities and interaction networks, thereby influencing biological processes and maintaining cellular homeostasis. Experimental validation of these effects is labor-intensive and expensive, highlighting the need for efficient computational approaches. We propose DeepPhosPPI, the first sequence-based deep learning framework for phosphorylation effects on PPIs prediction, which employs the pre-trained protein language model for feature embedding, with ProtBERT and ESM-2 as alternative backbone encoders. By combining attention-based convolutional neural network and Transformer models, DeepPhosPPI accurately predicts phosphorylation effects. The experimental results show that DeepPhosPPI consistently outperforms state-of-the-art methods in multiple tasks, including functional sites identification and regulatory effect classification.

Source: Briefings in Bioinformatics

DOI: 10.1093/bib/bbaf462

Authors: Yinyin Gong, Rui Li, Yan Liu, Jilong Wang, Danny Z Chen, Chee Keong Kwoh

Institutions: Hunan University, Nanyang Technological University, Peng Cheng Laboratory, University of Notre Dame

Language: English

Topic: Machine Learning in Bioinformatics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $3,885

Funders: National Natural Science Foundation of China, Natural Science Foundation of Hainan Province, Natural Science Foundation of Chongqing, Centre Scientifique et Technique du Bâtiment