ArticleBMC bioinformatics2019
ProteinNet: a standardized data set for machine learning of protein structure.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
- HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction.bioRxiv : the preprint server for biology · 2026Article
- A Comprehensive Review on Homology Modelling in Drug Development: Methodologies, Tools, and Evaluation.Mini reviews in medicinal chemistry · 2026Review
- Architectural good practices for reproducible benchmarking in protein machine learning.Frontiers in bioinformatics · 2026Article
- Multistage attention-based extraction and fusion of protein sequence and structural features for protein function prediction.Bioinformatics (Oxford, England) · 2025Article
- Designing single-polymer-chain nanoparticles to mimic biomolecular hydration frustration.Nature chemistry · 2025Article
- RC-GNN: A predictive model of enzyme-reaction pairs.bioRxiv : the preprint server for biology · 2025Article
- Annotating the microbial dark matter with HiFi-NN.iScience · 2025Article
- How well do contextual protein encodings learn structure, function, and evolutionary context?Cell systems · 2025Article
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
- Protein structure prediction via deep learning: an in-depth review.Frontiers in pharmacology · 2025Review
- CryptoBench: cryptic protein-ligand binding sites dataset and benchmark.Bioinformatics (Oxford, England) · 2024Article
- Linking Protein Stability to Pathogenicity: Predicting Clinical Significance of Single-Missense Mutations in Ocular Proteins Using Machine Learning.International journal of molecular sciences · 2024Article
- Machine Learning Using Template-Based-Predicted Structure of Haemagglutinin Predicts Pathogenicity of Avian Influenza.Journal of microbiology and biotechnology · 2024Article
- PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network.BMC bioinformatics · 2024Article
- Evaluating generalizability of artificial intelligence models for molecular datasets.bioRxiv : the preprint server for biology · 2024Article
- Learning the shape of protein microenvironments with a holographic convolutional neural network.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Prop3D: A flexible, Python-based platform for machine learning with protein structural properties and biophysical data.BMC bioinformatics · 2024Article
- Alignment-based Protein Mutational Landscape Prediction: Doing More with Less.Genome biology and evolution · 2023Article
- Article
- A Hybrid Structure-Based Machine Learning Approach for Predicting Kinase Inhibition by Small Molecules.Journal of chemical information and modeling · 2023Article
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Funding
Abstract
backgroundRapid progress in deep learning has spurred its application to bioinformatics problems including protein structure prediction and design. In classic machine learning problems like computer vision, progress has been driven by standardized data sets that facilitate fair assessment of new methods and lower the barrier to entry for non-domain experts. While data sets of protein sequence and structure exist, they lack certain components critical for machine learning, including high-quality multiple sequence alignments and insulated training/validation splits that account for deep but only weakly detectable homology across protein space.
resultsWe created the ProteinNet series of data sets to provide a standardized mechanism for training and assessing data-driven models of protein sequence-structure relationships. ProteinNet integrates sequence, structure, and evolutionary information in programmatically accessible file formats tailored for machine learning frameworks. Multiple sequence alignments of all structurally characterized proteins were created using substantial high-performance computing resources. Standardized data splits were also generated to emulate the difficulty of past CASP (Critical Assessment of protein Structure Prediction) experiments by resetting protein sequence and structure space to the historical states that preceded six prior CASPs. Utilizing sensitive evolution-based distance metrics to segregate distantly related proteins, we have additionally created validation sets distinct from the official CASP sets that faithfully mimic their difficulty.
conclusionProteinNet represents a comprehensive and accessible resource for training and assessing machine-learned models of protein structure.
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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.