DCMF-PPI: a protein-protein interaction predictor based on dynamic condition and multi-feature fusion

Background The identification of protein-protein interaction (PPI) plays a crucial role in understanding the mechanisms of complex biological processes. Current research in predicting PPI has shown remarkable progress by integrating protein information with PPI topology structure. Nevertheless, thes...

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Bibliographic Details
Published in:BMC bioinformatics Vol. 26; no. 1; p. 247
Main Authors: Chen, Siqi, Zheng, Anhong, Yu, Weichi, Zhan, Chao
Format: Journal Article
Language:English
Published: London BioMed Central 15.10.2025
BioMed Central Ltd
Springer Nature B.V
Subjects:
Algorithms
Amino acids
Artificial neural networks
Bioinformatics
Biological activity
Biological research
Biology, Experimental
Biomedical and Life Sciences
Cell cycle
Cellular structure
Computational Biology - methods
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Data mining
Databases, Protein
Datasets
Deep learning
Dynamic models
Feature extraction
Graph neural networks
Graphical representations
Heterogeneity
Life Sciences
Machine learning
Methods
Microarrays
Modules
Neural networks
Neural Networks, Computer
Protein interaction
Protein Interaction Mapping - methods
Protein-protein interactions
Proteins
Proteins - chemistry
Proteins - metabolism
Residues
Signal transduction
Software
Topology
Wavelet transforms
China
Wavelet transform
Dynamic condition
Protein-protein interaction
Multi-feature
ISSN:1471-2105, 1471-2105
Online Access:Get full text
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Summary:Background The identification of protein-protein interaction (PPI) plays a crucial role in understanding the mechanisms of complex biological processes. Current research in predicting PPI has shown remarkable progress by integrating protein information with PPI topology structure. Nevertheless, these approaches frequently overlook the dynamic nature of protein and PPI structures during cellular processes, including conformational alterations and variations in binding affinities under diverse environmental circumstances. Additionally, the insufficient availability of comprehensive protein data hinders accurate protein representation. Consequently, these shortcomings restrict the model’s generalizability and predictive precision. Results To address this, we introduce DCMF-PPI (Dynamic condition and multi-feature fusion framework for PPI), a novel hybrid framework that integrates dynamic modeling, multi-scale feature extraction, and probabilistic graph representation learning. DCMF-PPI comprises three core modules: (1) PortT5-GAT Module: The protein language model PortT5 is utilized to extract residue-level protein features, which are integrated with dynamic temporal dependencies. Graph attention networks are then employed to capture context-aware structural variations in protein interactions; (2) MPSWA Module: Employs parallel convolutional neural networks combined with wavelet transform to extract multi-scale features from diverse protein residue types, enhancing the representation of sequence and structural heterogeneity; (3) VGAE Module: Utilizes a Variational Graph Autoencoder to learn probabilistic latent representations, facilitating dynamic modeling of PPI graph structures and capturing uncertainty in interaction dynamics. Conclusion We conducted comprehensive experiments on benchmark datasets demonstrating that DCMF-PPI outperforms state-of-the-art methods in PPI prediction, achieving significant improvements in accuracy, precision, and recall. The framework’s ability to fuse dynamic conditions and multi-level features highlights its effectiveness in modeling real-world biological complexities, positioning it as a robust tool for advancing PPI research and downstream applications in systems biology and drug discovery.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-025-06272-4