ArticleBioinformatics (Oxford, England)2023
Struct2GO: protein function prediction based on graph pooling algorithm and AlphaFold2 structure information.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed.
- Problems With GO Enrichment as an Endpoint for Ecological Omics Interpretation.Molecular ecology · 2026Article
- Comprehensive overview of AI methodologies in nano-drug delivery Optimization and Design.NPJ precision oncology · 2026Review
- GPCR-GO: Relation-aware graph learning for predicting Gene Ontology terms of G protein-coupled receptors.PLoS computational biology · 2026Article
- MZSGO: multimodal zero-shot protein function annotation via evolutionary signals and textual semantics.Bioinformatics (Oxford, England) · 2026Article
- Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Graph Learning in Bioinformatics: A Survey of Graph Neural Network Architectures, Biological Graph Construction and Bioinformatics Applications.Biomolecules · 2026Review
- Struct2GO-Enhanced: Multimodal Graph Attention Improves Protein Function Prediction.Journal of chemical information and modeling · 2026Article
- Article
- Structure-guided sequence representation learning for generalizable protein function prediction.Bioinformatics (Oxford, England) · 2025Article
- Integrating ESM‑2 and Graph Neural Networks with AlphaFold‑2 Structures for Enhanced Protein Function Prediction.ACS omega · 2025Article
- SuperEdgeGO: Edge-supervised graph representation learning for enhanced protein function prediction.PLoS computational biology · 2025Article
- POSA-GO: Fusion of Hierarchical Gene Ontology and Protein Language Models for Protein Function Prediction.International journal of molecular sciences · 2025Article
- Multistage attention-based extraction and fusion of protein sequence and structural features for protein function prediction.Bioinformatics (Oxford, England) · 2025Article
- MSNGO: multi-species protein function annotation based on 3D protein structure and network propagation.Bioinformatics (Oxford, England) · 2025Article
- 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
- SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction.BMC bioinformatics · 2025Article
- MEGA-GO: functions prediction of diverse protein sequence length using Multi-scalE Graph Adaptive neural network.Bioinformatics (Oxford, England) · 2025Article
- Integrating Gene Ontology Relationships for Protein Function Prediction Using PFresGO.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Graph Neural Network-Based Approaches for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Struct2SL: Synthetic lethality prediction based on AlphaFold2 structure information and Multilayer Perceptron.Computational and structural biotechnology journal · 2025Article
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Authors and funding
6 authors.
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Abstract
motivationIn recent years, there has been a breakthrough in protein structure prediction, and the AlphaFold2 model of the DeepMind team has improved the accuracy of protein structure prediction to the atomic level. Currently, deep learning-based protein function prediction models usually extract features from protein sequences and combine them with protein-protein interaction networks to achieve good results. However, for newly sequenced proteins that are not in the protein-protein interaction network, such models cannot make effective predictions. To address this, this article proposes the Struct2GO model, which combines protein structure and sequence data to enhance the precision of protein function prediction and the generality of the model.
resultsWe obtain amino acid residue embeddings in protein structure through graph representation learning, utilize the graph pooling algorithm based on a self-attention mechanism to obtain the whole graph structure features, and fuse them with sequence features obtained from the protein language model. The results demonstrate that compared with the traditional protein sequence-based function prediction model, the Struct2GO model achieves better results. AVAILABILITY AND IMPLEMENTATION: The data underlying this article are available at https://github.com/lyjps/Struct2GO.
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