ArticleComputational and structural biotechnology journal2025
SpatialPPIv2: Enhancing protein-protein interaction prediction through graph neural networks with protein language models.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- ESpma: A method for assessing biological/non-biological interfaces using point-clouds-based structural features and protein language models.Protein science : a publication of the Protein Society · 2026Article
- DeorphaNN: Virtual screening of GPCR peptide agonists using AlphaFold-predicted active-state complexes and deep learning embeddings.Molecular cell · 2026Article
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Predicting Protein-Protein Interactions by Convolutional Neural Network Model.Biotech (Basel (Switzerland)) · 2026Article
- Multimodal graph, surface, and language-based model for protein protein interaction prediction.Scientific reports · 2026Article
- Prediction of protein-protein interactions using point transformer and spherical Convex Hull graphs.Computational and structural biotechnology journal · 2026Article
- Dual Cross-Attention Network for Hierarchical Feature Fusion in Protein-Protein Interaction Prediction.Computational and structural biotechnology journal · 2026Article
- Adversarial Sequence Mutations in AlphaFold and ESMFold Reveal Nonphysical Structural Invariance, Confidence Failures, and Concerns for Protein Design.Computational and structural biotechnology journal · 2026Article
- GTPLM-GO: Enhancing Protein Function Prediction Through Dual-Branch Graph Transformer and Protein Language Model Fusing Sequence and Local-Global PPI Information.International journal of molecular sciences · 2025Article
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- AlphaFold in protein structure and function prediction.Computational and structural biotechnology journal · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-protein interactions (PPIs) are fundamental to cellular functions, and accurately predicting such interactions is crucial for understanding biological mechanisms and facilitating drug discovery. SpatialPPIv2 is an advanced graph-neural-network-based model that predicts PPIs using large language models to embed sequence features and graph attention networks to capture structural information. By leveraging the comprehensive PINDER dataset, which includes interaction data from the RCSB PDB and AlphaFold databases, SpatialPPIv2 improves the PPI prediction specificity and robustness. Unlike the original SpatialPPI, the updated version no longer depends on protein structure prediction algorithms and can predict protein interactions standalone. SpatialPPIv2 outperforms the state-of-the-art PPI predictors, demonstrating superior accuracy and reliability. Furthermore, the model was robust when using structural prediction methods, including AlphaFold3, AlphaFold2, and ESMFold, indicating its applicability even if experimentally determined structures are unavailable. SpatialPPIv2 offers a promising solution for accurately predicting PPIs, provides insight into protein function, and supports advances in drug discovery and synthetic biology. SpatialPPIv2 is available at https://github.com/ohuelab/SpatialPPIv2.
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Registered trials
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