ArticleBriefings in bioinformatics2022
Accurate protein function prediction via graph attention networks with predicted structure information.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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61 citing papers in PubMed, 120 citations in OpenAlex.
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- GPCR-GO: Relation-aware graph learning for predicting Gene Ontology terms of G protein-coupled receptors.PLoS computational biology · 2026Article
- Machine Learning and Deep Learning Frameworks for Human-Virus Protein-Protein Interaction Prediction: Emerging Architectures, Methods, Benchmarks, and Challenges.International journal of molecular sciences · 2026Review
- SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.Briefings in bioinformatics · 2026Article
- SSAS-GO: structure-sequence adaptive synergy network for protein function prediction.Briefings in bioinformatics · 2026Article
- Artificial Intelligence Powers Protein Functional Annotation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Comparative Performance Evaluation of GCN, GAT, and GraphSAGE Architectures for Drug-Gene Interaction Prediction in Ameloblastoma via MEK-Pathway Targeting.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- DSS-PPI: a self-supervised graph learning framework for protein-protein interaction prediction via multimodal sequence semantics.BMC genomics · 2026Article
- Artificial intelligence-driven discovery of bioactive peptides: Computational approaches and future perspectives.aBIOTECH · 2026Review
- Struct2GO-Enhanced: Multimodal Graph Attention Improves Protein Function Prediction.Journal of chemical information and modeling · 2026Article
- Review
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- KmPred: prediction of Michaelis constants (Km) using an integrative machine learning framework.Frontiers in artificial intelligence · 2026Article
- Protein embeddings and local alignments.Computational and structural biotechnology journal · 2026Article
- MAGIN-GO: Protein function prediction based on dual graph neural networks and gene ontology structure.PloS one · 2026Article
- IMAGO: An Improved Model Based on Attention Mechanism for Enhanced Protein Function Prediction.Biomolecules · 2025Article
- BBATProt: a framework predicting biological function with enhanced feature extraction via interpretable deep learning.Briefings in bioinformatics · 2025Article
- Structure-guided sequence representation learning for generalizable protein function prediction.Bioinformatics (Oxford, England) · 2025Article
1 more citing papers are in PubMed but not listed here.
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2 authors at 1 institution in 1 country.
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Abstract
Experimental protein function annotation does not scale with the fast-growing sequence databases. Only a tiny fraction (<0.1%) of protein sequences has experimentally determined functional annotations. Computational methods may predict protein function very quickly, but their accuracy is not very satisfactory. Based upon recent breakthroughs in protein structure prediction and protein language models, we develop GAT-GO, a graph attention network (GAT) method that may substantially improve protein function prediction by leveraging predicted structure information and protein sequence embedding. Our experimental results show that GAT-GO greatly outperforms the latest sequence- and structure-based deep learning methods. On the PDB-mmseqs testset where the train and test proteins share <15% sequence identity, our GAT-GO yields Fmax (maximum F-score) 0.508, 0.416, 0.501, and area under the precision-recall curve (AUPRC) 0.427, 0.253, 0.411 for the MFO, BPO, CCO ontology domains, respectively, much better than the homology-based method BLAST (Fmax 0.117, 0.121, 0.207 and AUPRC 0.120, 0.120, 0.163) that does not use any structure information. On the PDB-cdhit testset where the training and test proteins are more similar, although using predicted structure information, our GAT-GO obtains Fmax 0.637, 0.501, 0.542 for the MFO, BPO, CCO ontology domains, respectively, and AUPRC 0.662, 0.384, 0.481, significantly exceeding the just-published method DeepFRI that uses experimental structures, which has Fmax 0.542, 0.425, 0.424 and AUPRC only 0.313, 0.159, 0.193.
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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.