ArticlePLoS computational biology2023
E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Physical priors improve performance of structure-based binding affinity models.bioRxiv : the preprint server for biology · 2026Article
- Predicting Enzyme pH Optima from Structure Using Equivariant Graph Neural Networks.Journal of chemical information and modeling · 2026Article
- M2-PRNet: multi-scale and multi-modal learning for protein-RNA binding affinity prediction.Bioinformatics (Oxford, England) · 2026Article
- ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures.Journal of chemical information and modeling · 2026Article
- xBind: an integrated webserver for large language model-enabled cross-molecular protein binding site prediction.Nucleic acids research · 2026Article
- ORIGAMI: Orientation-Aware Graph Neural Network for Assessing Multimeric Interfaces of Protein Complex Structures.bioRxiv : the preprint server for biology · 2026Article
- StrEAMM-Thioether: Efficient Structure Prediction for Thioether-Linked Cyclic Peptides.The journal of physical chemistry. B · 2026Article
- PPISHES-an enhanced physicochemical approach for predicting protein interaction sites using graph neural networks.Protein science : a publication of the Protein Society · 2025Article
- MPBind: a multitask protein binding site predictor using protein language models and equivariant GNNs.Bioinformatics (Oxford, England) · 2025Article
- Single-sequence protein-RNA complex structure prediction by geometric attention-enabled pairing of biological language models.Cell systems · 2025Article
- AlphaFold model quality self-assessment improvement via deep graph learning.Protein science : a publication of the Protein Society · 2025Article
- To pack or not to pack: revisiting protein side-chain packing in the post-AlphaFold era.Briefings in bioinformatics · 2025Article
- Advances in Language-Model-Informed Protein-Nucleic Acid Binding Site Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review.Computational and structural biotechnology journal · 2024Review
- MIPPIS: protein-protein interaction site prediction network with multi-information fusion.BMC bioinformatics · 2024Article
- EMPDTA: An End-to-End Multimodal Representation Learning Framework with Pocket Online Detection for Drug-Target Affinity Prediction.Molecules (Basel, Switzerland) · 2024Article
- EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks.Nucleic acids research · 2024Article
- Review
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Authors and funding
4 authors.
Funding
Abstract
Artificial intelligence-powered protein structure prediction methods have led to a paradigm-shift in computational structural biology, yet contemporary approaches for predicting the interfacial residues (i.e., sites) of protein-protein interaction (PPI) still rely on experimental structures. Recent studies have demonstrated benefits of employing graph convolution for PPI site prediction, but ignore symmetries naturally occurring in 3-dimensional space and act only on experimental coordinates. Here we present EquiPPIS, an E(3) equivariant graph neural network approach for PPI site prediction. EquiPPIS employs symmetry-aware graph convolutions that transform equivariantly with translation, rotation, and reflection in 3D space, providing richer representations for molecular data compared to invariant convolutions. EquiPPIS substantially outperforms state-of-the-art approaches based on the same experimental input, and exhibits remarkable robustness by attaining better accuracy with predicted structural models from AlphaFold2 than what existing methods can achieve even with experimental structures. Freely available at https://github.com/Bhattacharya-Lab/EquiPPIS, EquiPPIS enables accurate PPI site prediction at scale.
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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.