ArticleeLife2024
Protein language model-embedded geometric graphs power inter-protein contact prediction.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- SPPIDER-seq: sequence-based partner-aware predictor of protein-protein interaction sites.Bioinformatics (Oxford, England) · 2026Article
- SPPIDER-seq: Sequence-based partner-aware predictor of protein-protein interaction sites.bioRxiv : the preprint server for biology · 2026Article
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 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
- GeoGAD: geometry-aware antibody design framework for complementarity-determining region precision engineering.Bioinformatics (Oxford, England) · 2026Article
- 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
- A roadmap for T cell receptor-peptide-bound major histocompatibility complex binding prediction by machine learning: glimpse and foresight.Briefings in bioinformatics · 2025Review
- In silico prediction method for plant Nucleotide-binding leucine-rich repeat- and pathogen effector interactions.The Plant journal : for cell and molecular biology · 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
- Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures.Journal of cheminformatics · 2024Article
- Improving antibody optimization ability of generative adversarial network through large language model.Computational and structural biotechnology journal · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions. Although significant improvement has been made in inter-protein contact prediction recently, there is still a large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets illustrates that PLMGraph-Inter outperforms five top inter-protein contact prediction methods, including DeepHomo, GLINTER, CDPred, DeepHomo2, and DRN-1D2D_Inter, by large margins. In addition, we also show that the prediction of PLMGraph-Inter can complement the result of AlphaFold-Multimer. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein-protein docking can dramatically improve its performance for protein complex structure prediction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.