ArticleCommunications biology2023
Domain-PFP allows protein function prediction using function-aware domain embedding representations.
Article in Communications biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- On the state of protein function prediction: a report on the fourth CAFA challenge.bioRxiv : the preprint server for biology · 2026Article
- Integrating Protein Language Models with Multimodal Embeddings to Accelerate Function Prediction of Uncharacterized Proteins.International journal of molecular sciences · 2026Review
- MAGIN-GO: Protein function prediction based on dual graph neural networks and gene ontology structure.PloS one · 2026Article
- Vector semantics of multidomain protein architectures.Bioinformatics advances · 2026Article
- Queryome: Orchestrating Retrieval, Reasoning, and Synthesis across Biomedical Literature.bioRxiv : the preprint server for biology · 2025Article
- PPAC: Predicting Protein-Protein Affinity Changes Induced by Amino Acid Mutations Using Protein Large Language Models.ACS omega · 2025Article
- Domain-specific embedding models for hydrology and environmental sciences: enhancing semantic retrieval and question answering.Water science and technology : a journal of the International Association on Water Pollution Research · 2025Article
- Gene sequencing andJournal of advanced veterinary and animal research · 2025Article
- Paying attention to attention: High attention sites as indicators of protein family and function in language models.PLoS computational biology · 2025Article
- DA-HGL: a domain-augmented heterogeneous graph learning framework for protein function prediction.Briefings in bioinformatics · 2025Article
- POSA-GO: Fusion of Hierarchical Gene Ontology and Protein Language Models for Protein Function Prediction.International journal of molecular sciences · 2025Article
- GOBeacon: An ensemble model for protein function prediction enhanced by contrastive learning.Protein science : a publication of the Protein Society · 2025Article
- Protein function prediction using GO similarity-based heterogeneous network propagation.Scientific reports · 2025Article
- Genome-wide identification of the Sec14 gene family and the response to salt and drought stress in soybean (Glycine max).BMC genomics · 2025Article
- DPFunc: accurately predicting protein function via deep learning with domain-guided structure information.Nature communications · 2025Article
- A Survey of Biological Function Prediction Methods with Focus on Natural Language Processing (NLP) and Large Language Models (LLM).Methods in molecular biology (Clifton, N.J.) · 2025Article
- Machine Learning for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Predicting Protein Functions with Function-Aware Domain Embeddings Using Domain-PFP.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Translating a GO Term List to Human Readable Function Description Using GO2Sum.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
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3 authors.
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Abstract
Domains are functional and structural units of proteins that govern various biological functions performed by the proteins. Therefore, the characterization of domains in a protein can serve as a proper functional representation of proteins. Here, we employ a self-supervised protocol to derive functionally consistent representations for domains by learning domain-Gene Ontology (GO) co-occurrences and associations. The domain embeddings we constructed turned out to be effective in performing actual function prediction tasks. Extensive evaluations showed that protein representations using the domain embeddings are superior to those of large-scale protein language models in GO prediction tasks. Moreover, the new function prediction method built on the domain embeddings, named Domain-PFP, substantially outperformed the state-of-the-art function predictors. Additionally, Domain-PFP demonstrated competitive performance in the CAFA3 evaluation, achieving overall the best performance among the top teams that participated in the assessment.
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