ArticleNature communications2025
DPFunc: accurately predicting protein function via deep learning with domain-guided structure information.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- A Site-Aware Representation Learning Framework For Unified Molecular Interaction Modeling and Generative Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Data-driven pipeline enables discovery of plant protein surfactants.Communications chemistry · 2026Article
- GPCR-GO: Relation-aware graph learning for predicting Gene Ontology terms of G protein-coupled receptors.PLoS computational biology · 2026Article
- Deep Contrastive Learning for High-Throughput Prediction of Drug Resistance Mutations from Sequences.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- PUFFIN: protein unit discovery with functional supervision.Bioinformatics (Oxford, England) · 2026Article
- iPalmT: a new paradigm for palmitoyltransferase discovery via end-to-end deep learning.Oncogene · 2026Article
- Advancing generative large language models toward discriminative performance in protein function prediction.Genome biology · 2026Article
- ProSSF: integrating sequence, structure, and gene ontology for prediction of protein stability, interaction, and function.Molecular genetics and genomics : MGG · 2026Article
- SSAS-GO: structure-sequence adaptive synergy network for protein function prediction.Briefings in bioinformatics · 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
- Artificial intelligence-enabled multi-scale virtual cell: perspective, challenges, and opportunities.Briefings in bioinformatics · 2026Review
- Vector semantics of multidomain protein architectures.Bioinformatics advances · 2026Article
- IMAGO: An Improved Model Based on Attention Mechanism for Enhanced Protein Function Prediction.Biomolecules · 2025Article
- Neurodegeneration Through the Lens of Bioinformatics Approaches: Computational Mechanisms of Protein Misfolding.International journal of molecular sciences · 2025Review
- Endowing protein language models with structural knowledge.Bioinformatics (Oxford, England) · 2025Article
- Topology-aware functional similarity: integrating extended neighborhoods via exponential attenuation.BMC bioinformatics · 2025Article
- Beyond evolution:Biosafety and health · 2025Review
- The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe.Biology · 2025Review
- MKFGO: integrating multi-source knowledge fusion with pretrained language model for high-accuracy protein function prediction.Briefings in bioinformatics · 2025Article
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Authors and funding
5 authors.
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Abstract
Computational methods for predicting protein function are of great significance in understanding biological mechanisms and treating complex diseases. However, existing computational approaches of protein function prediction lack interpretability, making it difficult to understand the relations between protein structures and functions. In this study, we propose a deep learning-based solution, named DPFunc, for accurate protein function prediction with domain-guided structure information. DPFunc can detect significant regions in protein structures and accurately predict corresponding functions under the guidance of domain information. It outperforms current state-of-the-art methods and achieves a significant improvement over existing structure-based methods. Detailed analyses demonstrate that the guidance of domain information contributes to DPFunc for protein function prediction, enabling our method to detect key residues or regions in protein structures, which are closely related to their functions. In summary, DPFunc serves as an effective tool for large-scale protein function prediction, which pushes the border of protein understanding in biological systems.
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