ArticleBioinformatics (Oxford, England)2019
High precision protein functional site detection using 3D convolutional neural networks.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- Learnable protein representations in computational biology for predicting drug-target affinity.Journal of cheminformatics · 2026Review
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- Protein functional site annotation using local structure embeddings.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Deep learning-enhanced clustering and classification of protein molecule tertiary structures using weighted distance matrices.Briefings in bioinformatics · 2025Article
- Integrating artificial intelligence in drug discovery and early drug development: a transformative approach.Biomarker research · 2025Review
- Recent advances on protein engineering for improved stability.Biodesign research · 2025Review
- Deep Learning Approaches for the Prediction of Protein Functional Sites.Molecules (Basel, Switzerland) · 2025Review
- How to build the virtual cell with artificial intelligence: Priorities and opportunities.Cell · 2024Article
- How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities.ArXiv · 2024Article
- DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures.Briefings in bioinformatics · 2024Article
- GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking.Briefings in bioinformatics · 2024Article
- Enzyme Databases in the Era of Omics and Artificial Intelligence.International journal of molecular sciences · 2023Review
- Discovering functionally important sites in proteins.Nature communications · 2023Article
- Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins.Nature communications · 2023Article
- Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network.Nature communications · 2023Article
- Classifying metal-binding sites with neural networks.Protein science : a publication of the Protein Society · 2023Article
- COLLAPSE: A representation learning framework for identification and characterization of protein structural sites.Protein science : a publication of the Protein Society · 2023Article
- PyUUL provides an interface between biological structures and deep learning algorithms.Nature communications · 2022Article
- Identification of Iron-Sulfur (Fe-S) Cluster and Zinc (Zn) Binding Sites Within Proteomes Predicted by DeepMind's AlphaFold2 Program Dramatically Expands the Metalloproteome.Journal of molecular biology · 2022Article
Corrections and comments
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2 authors.
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Abstract
motivationAccurate annotation of protein functions is fundamental for understanding molecular and cellular physiology. Data-driven methods hold promise for systematically deriving rules underlying the relationship between protein structure and function. However, the choice of protein structural representation is critical. Pre-defined biochemical features emphasize certain aspects of protein properties while ignoring others, and therefore may fail to capture critical information in complex protein sites.
resultsIn this paper, we present a general framework that applies 3D convolutional neural networks (3DCNNs) to structure-based protein functional site detection. The framework can extract task-dependent features automatically from the raw atom distributions. We benchmarked our method against other methods and demonstrate better or comparable performance for site detection. Our deep 3DCNNs achieved an average recall of 0.955 at a precision threshold of 0.99 on PROSITE families, detected 98.89 and 92.88% of nitric oxide synthase and TRYPSIN-like enzyme sites in Catalytic Site Atlas, and showed good performance on challenging cases where sequence motifs are absent but a function is known to exist. Finally, we inspected the individual contributions of each atom to the classification decisions and show that our models successfully recapitulate known 3D features within protein functional sites. AVAILABILITY AND IMPLEMENTATION: The 3DCNN models described in this paper are available at https://simtk.org/projects/fscnn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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