ArticleNature communications2021
Structure-based protein function prediction using graph convolutional networks.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 437 papers, 1 of them a synthesis that pooled it.
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Who cites it
437 citing papers in PubMed, 1 synthesis or guideline pooled it.
- (Re)defining the human chromatome: an integrated meta-analysis of localization, function, abundance, physical properties, and domain composition of chromatin proteins.Nucleic acids research · 2026Pooled it
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- Problems With GO Enrichment as an Endpoint for Ecological Omics Interpretation.Molecular ecology · 2026Article
- Democratizing protein language model training, sharing and collaboration.Nature biotechnology · 2026Article
- Article
- Genolator enables protein function interpretation using a multimodal large language model fusing genomic and structural interpretation with natural language interaction.Genome biology · 2026Article
- Large-scale benchmarking of prokaryotic annotation tools across thousands of species.Genome biology · 2026Article
- DyProL: Dynamic Ensemble Representation Learning for Protein-Nucleic Acid Binding Site Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Predicting membrane protein localization by deep learning on structure and chemistry.Protein science : a publication of the Protein Society · 2026Article
- Structure-based virulence factor classification using a dual-driven graph transformer with a pretrained language model.Briefings in bioinformatics · 2026Article
- Investigating enzyme function by geometric matching of catalytic motifs.Protein science : a publication of the Protein Society · 2026Article
- DeepPNI: a language- and graph-based model for mutation-driven protein-nucleic acid binding energetics.Nucleic acids research · 2026Article
- Rhobot-Screen: an integrated robotic platform for functional screening of rhodopsin variants.BMC biology · 2026Article
- The limits of bio-molecular modeling with large language models: a cross-scale evaluation.Bioinformatics (Oxford, England) · 2026Article
- A trimodal protein language model enables advanced protein searches.Nature biotechnology · 2026Article
- GPCR-GO: Relation-aware graph learning for predicting Gene Ontology terms of G protein-coupled receptors.PLoS computational biology · 2026Article
- Metal binding site alignment enables network-driven discovery of recurrent geometries across sequence-divergent proteins and drug off-targets.PLoS computational biology · 2026Article
- ProtPen combines sequence- and structure-based approaches to facilitate protein function predictions on a proteome-wide scale.bioRxiv : the preprint server for biology · 2026Article
- APOC1⁺IFI30⁺ TAMs orchestrate colorectal cancer liver metastasis by coupling cholesterol biosynthesis with secretion of immunosuppressive factors.Cellular and molecular life sciences : CMLS · 2026Article
- Gut Microbiomes of Rainbow Trout and Atlantic Salmon: Nutritional Modulation, Mucosal Immunity, and Resistome Risk.Biology · 2026Review
377 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors.
Funding
Abstract
The rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmenting the training set of experimental structures with homology models allows us to significantly expand the number of predictable functions. DeepFRI has significant de-noising capability, with only a minor drop in performance when experimental structures are replaced by protein models. Class activation mapping allows function predictions at an unprecedented resolution, allowing site-specific annotations at the residue-level in an automated manner. We show the utility and high performance of our method by annotating structures from the PDB and SWISS-MODEL, making several new confident function predictions. DeepFRI is available as a webserver at https://beta.deepfri.flatironinstitute.org/ .
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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.