ArticleBioinformatics (Oxford, England)2022
Deep graph learning of inter-protein contacts.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
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- An integrated computational antigen discovery pipeline with hierarchical filtering for emerging viral variants.NAR molecular medicine · 2026Article
- Accurate protein-protein interactions modeling through physics-informed geometric invariant learning.Communications biology · 2026Article
- A paired sequence language model for protein-protein interaction modeling.Nature communications · 2026Article
- BEST: Basic Embedding Search Tool Enhancing Discovery of Novel Enzyme.Interdisciplinary sciences, computational life sciences · 2026Article
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- The power and limits of predicting inter-protein exon-exon interactions using protein 3D structures.Bioinformatics advances · 2026Article
- Molecular mechanism and structural models of protein-mediated copper transfer to the Arabidopsis thaliana ethylene receptor ETR1 at the ER membrane.Scientific reports · 2025Article
- Graph neural network integrated with pretrained protein language model for predicting human-virus protein-protein interactions.Briefings in bioinformatics · 2025Article
- PIPENN-EMB ensemble net and protein embeddings generalise protein interface prediction beyond homology.Scientific reports · 2025Article
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- ToxDL 2.0: Protein toxicity prediction using a pretrained language model and graph neural networks.Computational and structural biotechnology journal · 2025Article
- AI-integrated network for RNA complex structure and dynamic prediction.Biophysics reviews · 2024Review
- Pairing interacting protein sequences using masked language modeling.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure.Interdisciplinary sciences, computational life sciences · 2024Review
- A variational expectation-maximization framework for balanced multi-scale learning of protein and drug interactions.Nature communications · 2024Article
- Protein language model-embedded geometric graphs power inter-protein contact prediction.eLife · 2024Article
- Flexible protein-protein docking with a multitrack iterative transformer.Protein science : a publication of the Protein Society · 2024Article
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2 authors.
Funding
Abstract
motivationInter-protein (interfacial) contact prediction is very useful for in silico structural characterization of protein-protein interactions. Although deep learning has been applied to this problem, its accuracy is not as good as intra-protein contact prediction.
resultsWe propose a new deep learning method GLINTER (Graph Learning of INTER-protein contacts) for interfacial contact prediction of dimers, leveraging a rotational invariant representation of protein tertiary structures and a pretrained language model of multiple sequence alignments. Tested on the 13th and 14th CASP-CAPRI datasets, the average top L/10 precision achieved by GLINTER is 54% on the homodimers and 52% on all the dimers, much higher than 30% obtained by the latest deep learning method DeepHomo on the homodimers and 15% obtained by BIPSPI on all the dimers. Our experiments show that GLINTER-predicted contacts help improve selection of docking decoys. AVAILABILITY AND IMPLEMENTATION: The software is available at https://github.com/zw2x/glinter. The datasets are available at https://github.com/zw2x/glinter/data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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