ArticleCommunications biology2023
Learning the protein language of proteome-wide protein-protein binding sites via explainable ensemble deep learning.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Hierarchical Graph Representation Learning for Protein-Protein Interaction Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Hierarchical Contrastive Learning for Protein-Protein Interaction Prediction Across Organisms.International journal of molecular sciences · 2026Article
- Predicting protein-protein interaction sites based on dynamic perception mechanism within a hierarchical E(n)-equivariant graph.Briefings in bioinformatics · 2026Article
- Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks.PLoS computational biology · 2026Article
- PLM-eXplain: divide and conquer the protein embedding space.Bioinformatics (Oxford, England) · 2026Article
- ProMoHGT: a heterogeneous graph transformer with graph contrastive learning for robust microbial protein function prediction.BMC genomics · 2025Article
- PPISHES-an enhanced physicochemical approach for predicting protein interaction sites using graph neural networks.Protein science : a publication of the Protein Society · 2025Article
- Review
- MVSO-PPIS: a structured objective learning model for protein-protein interaction sites prediction via multi-view graph information integration.Bioinformatics (Oxford, England) · 2025Article
- GTE-PPIS: a protein-protein interaction site predictor based on graph transformer and equivariant graph neural network.Briefings in bioinformatics · 2025Article
- Feature fusion with attributed deepwalk for protein-protein interaction prediction.Scientific reports · 2025Article
- HSSPPI: hierarchical and spatial-sequential modeling for PPIs prediction.Briefings in bioinformatics · 2025Article
- PyPropel: a Python-based tool for efficiently processing and characterising protein data.BMC bioinformatics · 2025Article
- Large Context, Deeper Insights: Harnessing Large Language Models for Advancing Protein-Protein Interaction Analysis.Methods in molecular biology (Clifton, N.J.) · 2025Article
- A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale.Communications biology · 2024Article
- Deciphering 3'UTR Mediated Gene Regulation Using Interpretable Deep Representation Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- PMSFF: Improved Protein Binding Residues Prediction through Multi-Scale Sequence-Based Feature Fusion Strategy.Biomolecules · 2024Article
- Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis.BMC medical informatics and decision making · 2024Article
- Diversification of the Rho transcription termination factor in bacteria.Nucleic acids research · 2024Article
- MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-protein interactions (PPIs) govern cellular pathways and processes, by significantly influencing the functional expression of proteins. Therefore, accurate identification of protein-protein interaction binding sites has become a key step in the functional analysis of proteins. However, since most computational methods are designed based on biological features, there are no available protein language models to directly encode amino acid sequences into distributed vector representations to model their characteristics for protein-protein binding events. Moreover, the number of experimentally detected protein interaction sites is much smaller than that of protein-protein interactions or protein sites in protein complexes, resulting in unbalanced data sets that leave room for improvement in their performance. To address these problems, we develop an ensemble deep learning model (EDLM)-based protein-protein interaction (PPI) site identification method (EDLMPPI). Evaluation results show that EDLMPPI outperforms state-of-the-art techniques including several PPI site prediction models on three widely-used benchmark datasets including Dset_448, Dset_72, and Dset_164, which demonstrated that EDLMPPI is superior to those PPI site prediction models by nearly 10% in terms of average precision. In addition, the biological and interpretable analyses provide new insights into protein binding site identification and characterization mechanisms from different perspectives. The EDLMPPI webserver is available at http://www.edlmppi.top:5002/ .
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