# ProteinNet: a standardized data set for machine learning of protein structure

arXiv: https://arxiv.org/abs/1902.00249 · HTML: https://mlsanity.com/paper/1902.00249/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2019.

## Built on by (most cited first, 30 of 39)

- [The geometry of hidden representations of large transformer models](https://mlsanity.com/paper/2302.00294/) (2023)
- [PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding](https://mlsanity.com/paper/2206.02096/) (2022)
- [A Text-guided Protein Design Framework](https://mlsanity.com/paper/2302.04611/) (2023)
- [xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein](https://mlsanity.com/paper/2401.06199/) (2024)
- [Ankh: Optimized Protein Language Model Unlocks General-Purpose Modelling](https://mlsanity.com/paper/2301.06568/) (2023)
- [Entangled Datasets for Quantum Machine Learning](https://mlsanity.com/paper/2109.03400/) (2021)
- [Modeling Protein Using Large-scale Pretrain Language Model](https://mlsanity.com/paper/2108.07435/) (2021)
- [Flow Matching Meets Biology and Life Science: A Survey](https://mlsanity.com/paper/2507.17731/) (2025)
- [Structure Inducing Pre-Training](https://mlsanity.com/paper/2103.10334/) (2021)
- [DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces](https://mlsanity.com/paper/2307.12451/) (2023)
- [OpenProteinSet: Training data for structural biology at scale](https://mlsanity.com/paper/2308.05326/) (2023)
- [Mimetic Neural Networks: A unified framework for Protein Design and Folding](https://mlsanity.com/paper/2102.03881/) (2021)
- [Retrieved Sequence Augmentation for Protein Representation Learning](https://mlsanity.com/paper/2302.12563/) (2023)
- [ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling](https://mlsanity.com/paper/2403.12995/) (2024)
- [VenusX: Unlocking Fine-Grained Functional Understanding of Proteins](https://mlsanity.com/paper/2505.11812/) (2025)
- [GLProtein: Global-and-Local Structure Aware Protein Representation Learning](https://mlsanity.com/paper/2506.06294/) (2025)
- [Learning the shape of protein micro-environments with a holographic convolutional neural network](https://mlsanity.com/paper/2211.02936/) (2022)
- [Protein sequence-to-structure learning: Is this the end(-to-end revolution)?](https://mlsanity.com/paper/2105.07407/) (2021)
- [PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction](https://mlsanity.com/paper/2206.12240/) (2022)
- [Holographic-(V)AE: an end-to-end SO(3)-Equivariant (Variational) Autoencoder in Fourier Space](https://mlsanity.com/paper/2209.15567/) (2022)
- [Protein Representation Learning via Knowledge Enhanced Primary Structure Modeling](https://mlsanity.com/paper/2301.13154/) (2023)
- [Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations](https://mlsanity.com/paper/2505.20052/) (2025)
- [Manifold-Aware Perturbations for Constrained Generative Modeling](https://mlsanity.com/paper/2601.23151/) (2026)
- [Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM](https://mlsanity.com/paper/2102.06125/) (2021)
- [Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle Approach](https://mlsanity.com/paper/2212.03320/) (2022)
- [GOProteinGNN: Leveraging Protein Knowledge Graphs for Protein Representation Learning](https://mlsanity.com/paper/2408.00057/) (2024)
- [Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification](https://mlsanity.com/paper/2502.07299/) (2025)
- [KGOT: Unified Knowledge Graph and Optimal Transport Pseudo-Labeling for Molecule-Protein Interaction Prediction](https://mlsanity.com/paper/2512.09365/) (2025)
- [Multiscale Graph Neural Networks for Protein Residue Contact Map Prediction](https://mlsanity.com/paper/2212.02251/) (2022)
- [HERMES: Holographic Equivariant neuRal network model for Mutational Effect and Stability prediction](https://mlsanity.com/paper/2407.06703/) (2024)

## Abstract (arXiv)

Rapid 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. We have 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. ProteinNet thus represents a comprehensive and accessible resource for training and assessing machine-learned models of protein structure.