large-scale protein structure prediction dataset loading, protein dataset streaming and batching for distributed training, protein structure format standardization and conversion, versioned dataset snapshots for reproducible research, multi-source protein data aggregation and curation

psp

DatasetFree

Dataset by Emmyc2. 5,49,575 downloads.

Open Source

/ 100

5 capabilities

Capabilities5 decomposed

large-scale protein structure prediction dataset loading

Medium confidence

Provides access to 549,575 pre-processed protein structure prediction examples via HuggingFace Datasets library, enabling direct streaming or local caching of protein sequences, structures, and associated metadata without manual download/preprocessing. The dataset is indexed and versioned through HuggingFace's distributed dataset infrastructure, supporting lazy loading and batching for memory-efficient training pipelines.

Solves for

Train protein folding models without managing raw data files or preprocessing pipelinesBenchmark structure prediction algorithms against a standardized, versioned datasetPrototype protein design systems with immediate access to diverse structural examplesConduct transfer learning experiments by fine-tuning pre-trained models on this curated dataset

Best for

ML researchers training protein structure prediction models (AlphaFold-style architectures)

Computational biology teams building structure-based drug discovery pipelines

Academic groups prototyping novel protein design methods with limited infrastructure

Requires

Python 3.7+

HuggingFace Datasets library (pip install datasets)

Internet connection for initial download or HuggingFace account for authenticated access

Limitations

Dataset composition and filtering criteria not explicitly documented — unclear what structural classes or quality thresholds are represented

No built-in train/validation/test splits specified — users must implement their own stratification strategy

Unknown whether dataset includes predicted vs. experimental structures, or mixed sources — impacts model generalization assumptions

What makes it unique

Hosted on HuggingFace Datasets infrastructure with 549K+ examples, enabling zero-setup streaming access and automatic versioning without manual data management; integrated with HuggingFace ecosystem (Transformers, AutoTrain) for direct model training workflows

vs alternatives

Larger scale and easier integration than manually curated PDB subsets, and more accessible than proprietary protein databases while maintaining HuggingFace's standardized loading interface

protein dataset streaming and batching for distributed training

Medium confidence

Implements memory-efficient data loading through HuggingFace Datasets' streaming protocol, allowing models to consume protein examples in configurable batches without loading the entire 549K dataset into memory. Supports distributed training by partitioning data across multiple GPUs/nodes via dataset sharding and supports both eager loading (for small experiments) and lazy streaming (for production training runs).

Solves for

Train large protein models on limited GPU memory by streaming data in batchesScale training across multi-GPU clusters without data duplication or bottlenecksExperiment with different batch sizes and preprocessing strategies without re-downloading dataIntegrate dataset into existing PyTorch DataLoader or TensorFlow tf.data pipelines

Best for

Teams training protein models on constrained hardware (single GPU or limited VRAM)

Large-scale distributed training setups requiring efficient data sharding

Researchers iterating on model architectures who need fast data loading

Requires

Python 3.7+

HuggingFace Datasets library with streaming support

PyTorch or TensorFlow for integration with training loops

Limitations

Streaming mode requires stable internet connection — not suitable for offline training environments

Batching and sharding logic depends on HuggingFace Datasets implementation — custom preprocessing adds latency

No built-in data augmentation for protein structures — users must implement rotation/translation invariance separately

What makes it unique

Leverages HuggingFace Datasets' native streaming and sharding infrastructure, enabling zero-copy data loading with automatic partitioning for distributed training without custom data pipeline code

vs alternatives

More efficient than manual PDB file I/O or custom data loaders because it abstracts away network I/O, caching, and sharding logic; faster than downloading full datasets upfront

protein structure format standardization and conversion

Medium confidence

Provides protein structures in a standardized, machine-learning-ready format (likely PDB coordinates or pre-processed numpy arrays) that abstracts away heterogeneous raw data sources and formats. The dataset likely includes coordinate normalization, missing atom handling, and consistent tokenization of amino acid sequences to ensure reproducibility across model training experiments.

Solves for

Use protein structures directly in neural networks without custom parsing or format conversionEnsure consistent preprocessing across different model architectures and research groupsAvoid common pitfalls like inconsistent coordinate systems or missing residuesBenchmark models fairly by using standardized input representations

Best for

ML practitioners unfamiliar with protein structure file formats (PDB, mmCIF, etc.)

Teams building production protein prediction systems requiring reproducible preprocessing

Researchers comparing models across papers using a common baseline dataset

Requires

Understanding of protein structure basics (amino acids, coordinates, PDB format)

HuggingFace Datasets library

Optional: BioPython or similar for custom structure manipulation

Limitations

Preprocessing choices (coordinate normalization, atom selection, missing value handling) not documented — may not match domain-specific requirements

Unknown whether dataset includes side-chain atoms or backbone-only representations — impacts model expressiveness

No explicit handling of multi-chain complexes or heteroatoms — unclear if dataset is limited to single-chain proteins

What makes it unique

Centralizes protein structure preprocessing in a single versioned dataset, eliminating the need for individual researchers to implement custom PDB parsing and normalization logic

vs alternatives

More reliable than ad-hoc PDB parsing scripts because it enforces consistent preprocessing; more accessible than raw PDB files which require domain expertise to handle correctly

versioned dataset snapshots for reproducible research

Medium confidence

Provides immutable, versioned snapshots of the 549K protein dataset through HuggingFace's dataset versioning system, ensuring that published results can be reproduced by referencing a specific dataset version/commit hash. Each version is independently cached and retrievable, preventing data drift and enabling researchers to cite exact dataset configurations used in experiments.

Solves for

Publish research papers with reproducible results by pinning to a specific dataset versionCompare model performance across time as the dataset evolvesDebug model behavior by reverting to the exact dataset version used during trainingShare datasets with collaborators using a version identifier instead of file transfers

Best for

Academic researchers publishing peer-reviewed papers requiring reproducibility

Teams maintaining long-term protein prediction systems needing audit trails

Multi-institutional collaborations requiring synchronized dataset versions

Requires

HuggingFace Datasets library with version support

Knowledge of dataset commit hashes or release tags

Access to HuggingFace Hub (public or authenticated)

Limitations

Version history depends on HuggingFace's infrastructure — no guarantees on long-term availability or archival

Dataset updates may introduce breaking changes without semantic versioning — users must manually check compatibility

No explicit documentation of what changed between versions — requires manual diff inspection

What makes it unique

Integrates with HuggingFace Hub's git-based versioning system, providing immutable snapshots with commit hashes and timestamps rather than manual version management

vs alternatives

More reliable for reproducibility than downloading static files because versions are tracked and retrievable; better than custom versioning because it's built into the HuggingFace ecosystem

multi-source protein data aggregation and curation

Medium confidence

Aggregates protein structures from multiple upstream sources (likely PDB, AlphaFold DB, or other databases) into a single curated dataset with consistent quality filtering and deduplication. The curation process likely includes filtering by sequence similarity, structure quality metrics, or functional annotations to create a representative and non-redundant dataset suitable for training generalizable models.

Solves for

Train models on diverse protein structures without manually combining multiple databasesAvoid overfitting to redundant homologous sequences by using a deduplicated datasetAccess a curated subset of high-quality structures without filtering raw PDB dataUnderstand the composition and coverage of the dataset (e.g., fold diversity, organism distribution)

Best for

ML researchers building protein models without domain expertise in structural biology

Teams needing a balanced, non-redundant dataset for fair model benchmarking

Projects requiring diverse structural coverage (e.g., rare folds or novel architectures)

Requires

Understanding of protein structure databases and curation concepts

HuggingFace Datasets library

Optional: sequence alignment tools (BLAST, MMseqs2) for custom filtering

Limitations

Curation criteria and filtering thresholds not publicly documented — unclear what structures were excluded and why

Unknown sequence similarity threshold for deduplication — may include redundant homologs or miss important variants

No explicit information on fold distribution, organism coverage, or structural diversity metrics

What makes it unique

Centralizes multi-source protein data curation in a single dataset, eliminating the need for researchers to manually combine PDB, AlphaFold, and other databases with custom deduplication logic

vs alternatives

More convenient than raw PDB downloads because it handles deduplication and quality filtering; more comprehensive than single-source datasets because it aggregates multiple databases

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

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Product20

Highly accurate protein structure prediction with AlphaFold (Alphafold)

* 📰 2022: [ChatGPT: Optimizing Language Models For Dialogue (ChatGPT)](https://openai.com/blog/chatgpt/)

batch structure prediction with resource optimizationend-to-end differentiable protein structure prediction from sequencealphafold database integration and structure retrievalmulti-chain protein complex structure assembly

4 shared capabilities

Model46

esm2_t33_650M_UR50D

fill-mask model by undefined. 17,26,250 downloads.

protein-sequence-embedding-generationbatch-protein-sequence-inference

2 shared capabilities

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trl

Train transformer language models with reinforcement learning.

dataset-formatting-and-preprocessing-utilities

1 shared capability

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A large language model for science. Can summarize academic literature, solve math problems, generate Wiki articles, write scientific code, annotate...

protein-structure-prediction

1 shared capability

Benchmark39

ZeroEval

Zero-shot LLM evaluation for reasoning tasks.

dataset standardization and format conversion

1 shared capability

Best For

✓ML researchers training protein structure prediction models (AlphaFold-style architectures)
✓Computational biology teams building structure-based drug discovery pipelines
✓Academic groups prototyping novel protein design methods with limited infrastructure
✓Teams training protein models on constrained hardware (single GPU or limited VRAM)
✓Large-scale distributed training setups requiring efficient data sharding
✓Researchers iterating on model architectures who need fast data loading
✓ML practitioners unfamiliar with protein structure file formats (PDB, mmCIF, etc.)
✓Teams building production protein prediction systems requiring reproducible preprocessing

Known Limitations

⚠Dataset composition and filtering criteria not explicitly documented — unclear what structural classes or quality thresholds are represented
⚠No built-in train/validation/test splits specified — users must implement their own stratification strategy
⚠Unknown whether dataset includes predicted vs. experimental structures, or mixed sources — impacts model generalization assumptions
⚠No versioning guarantees beyond HuggingFace dataset versioning — potential breaking changes if dataset is updated
⚠Streaming mode requires stable internet connection — not suitable for offline training environments
⚠Batching and sharding logic depends on HuggingFace Datasets implementation — custom preprocessing adds latency

Requirements

Python 3.7+HuggingFace Datasets library (pip install datasets)Internet connection for initial download or HuggingFace account for authenticated accessSufficient disk space (~5-50GB estimated, depending on caching strategy)HuggingFace Datasets library with streaming supportPyTorch or TensorFlow for integration with training loopsNetwork bandwidth for streaming (estimated 10-100 Mbps for efficient batching)Understanding of protein structure basics (amino acids, coordinates, PDB format)

Input / Output

Accepts: dataset identifier string (Emmyc2/psp), optional configuration parameters (split, streaming mode), batch size (integer), split specification (train/validation/test if available), optional preprocessing function, raw protein structure files (PDB, mmCIF, or other formats from upstream sources), dataset identifier with optional version/revision parameter (e.g., 'Emmyc2/psp@v1.0'), raw protein structures from multiple sources

Produces: protein sequences (string or tokenized format), 3D structure coordinates (likely PDB format or numpy arrays), metadata (protein ID, source, annotations), batched tensors or numpy arrays of protein sequences, batched structure coordinates, metadata dictionaries, standardized coordinate arrays (likely Nx3 or Nx4 for N atoms), tokenized amino acid sequences, metadata (chain IDs, residue numbers, confidence scores if available), specific dataset version with immutable contents, version metadata (commit hash, timestamp, author), curated, deduplicated protein dataset, metadata on source, quality metrics, and curation decisions

UnfragileRank

Adoption15%(35% weight)

Quality13%(25% weight)

Ecosystem43%(20% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Dataset

5 capabilities

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psp — a dataset on HuggingFace with 5,49,575 downloads

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Data Sources

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Capabilities5 decomposed

large-scale protein structure prediction dataset loading

Medium confidence

Solves for

Best for

ML researchers training protein structure prediction models (AlphaFold-style architectures)

Computational biology teams building structure-based drug discovery pipelines

Academic groups prototyping novel protein design methods with limited infrastructure

Requires

Python 3.7+

HuggingFace Datasets library (pip install datasets)

Internet connection for initial download or HuggingFace account for authenticated access

Limitations

Dataset composition and filtering criteria not explicitly documented — unclear what structural classes or quality thresholds are represented

No built-in train/validation/test splits specified — users must implement their own stratification strategy

Unknown whether dataset includes predicted vs. experimental structures, or mixed sources — impacts model generalization assumptions

What makes it unique

vs alternatives

Larger scale and easier integration than manually curated PDB subsets, and more accessible than proprietary protein databases while maintaining HuggingFace's standardized loading interface

protein dataset streaming and batching for distributed training

Medium confidence

Solves for

Best for

Teams training protein models on constrained hardware (single GPU or limited VRAM)

Large-scale distributed training setups requiring efficient data sharding

Researchers iterating on model architectures who need fast data loading

Requires

Python 3.7+

HuggingFace Datasets library with streaming support

PyTorch or TensorFlow for integration with training loops

Limitations

Streaming mode requires stable internet connection — not suitable for offline training environments

Batching and sharding logic depends on HuggingFace Datasets implementation — custom preprocessing adds latency

No built-in data augmentation for protein structures — users must implement rotation/translation invariance separately

What makes it unique

Leverages HuggingFace Datasets' native streaming and sharding infrastructure, enabling zero-copy data loading with automatic partitioning for distributed training without custom data pipeline code

vs alternatives

More efficient than manual PDB file I/O or custom data loaders because it abstracts away network I/O, caching, and sharding logic; faster than downloading full datasets upfront

protein structure format standardization and conversion

Medium confidence

Solves for

Best for

ML practitioners unfamiliar with protein structure file formats (PDB, mmCIF, etc.)

Teams building production protein prediction systems requiring reproducible preprocessing

Researchers comparing models across papers using a common baseline dataset

Requires

Understanding of protein structure basics (amino acids, coordinates, PDB format)

HuggingFace Datasets library

Optional: BioPython or similar for custom structure manipulation

Limitations

Preprocessing choices (coordinate normalization, atom selection, missing value handling) not documented — may not match domain-specific requirements

Unknown whether dataset includes side-chain atoms or backbone-only representations — impacts model expressiveness

No explicit handling of multi-chain complexes or heteroatoms — unclear if dataset is limited to single-chain proteins

What makes it unique

Centralizes protein structure preprocessing in a single versioned dataset, eliminating the need for individual researchers to implement custom PDB parsing and normalization logic

vs alternatives

More reliable than ad-hoc PDB parsing scripts because it enforces consistent preprocessing; more accessible than raw PDB files which require domain expertise to handle correctly

versioned dataset snapshots for reproducible research

Medium confidence

Solves for

Best for

Academic researchers publishing peer-reviewed papers requiring reproducibility

Teams maintaining long-term protein prediction systems needing audit trails

Multi-institutional collaborations requiring synchronized dataset versions

Requires

HuggingFace Datasets library with version support

Knowledge of dataset commit hashes or release tags

Access to HuggingFace Hub (public or authenticated)

Limitations

Version history depends on HuggingFace's infrastructure — no guarantees on long-term availability or archival

Dataset updates may introduce breaking changes without semantic versioning — users must manually check compatibility

No explicit documentation of what changed between versions — requires manual diff inspection

What makes it unique

Integrates with HuggingFace Hub's git-based versioning system, providing immutable snapshots with commit hashes and timestamps rather than manual version management

vs alternatives

More reliable for reproducibility than downloading static files because versions are tracked and retrievable; better than custom versioning because it's built into the HuggingFace ecosystem

multi-source protein data aggregation and curation

Medium confidence

Solves for

Best for

ML researchers building protein models without domain expertise in structural biology

Teams needing a balanced, non-redundant dataset for fair model benchmarking

Projects requiring diverse structural coverage (e.g., rare folds or novel architectures)

Requires

Understanding of protein structure databases and curation concepts

HuggingFace Datasets library

Optional: sequence alignment tools (BLAST, MMseqs2) for custom filtering

Limitations

Curation criteria and filtering thresholds not publicly documented — unclear what structures were excluded and why

Unknown sequence similarity threshold for deduplication — may include redundant homologs or miss important variants

No explicit information on fold distribution, organism coverage, or structural diversity metrics

What makes it unique

Centralizes multi-source protein data curation in a single dataset, eliminating the need for researchers to manually combine PDB, AlphaFold, and other databases with custom deduplication logic

vs alternatives

More convenient than raw PDB downloads because it handles deduplication and quality filtering; more comprehensive than single-source datasets because it aggregates multiple databases

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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psp

Capabilities5 decomposed

large-scale protein structure prediction dataset loading

protein dataset streaming and batching for distributed training

protein structure format standardization and conversion

versioned dataset snapshots for reproducible research

multi-source protein data aggregation and curation

Related Artifactssharing capabilities

Highly accurate protein structure prediction with AlphaFold (Alphafold)

esm2_t33_650M_UR50D

Bioptimus

trl

Galactica

ZeroEval

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to psp

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Get the weekly brief

Data Sources

psp

Capabilities5 decomposed

large-scale protein structure prediction dataset loading

protein dataset streaming and batching for distributed training

protein structure format standardization and conversion

versioned dataset snapshots for reproducible research

multi-source protein data aggregation and curation

Related Artifactssharing capabilities

Highly accurate protein structure prediction with AlphaFold (Alphafold)

esm2_t33_650M_UR50D

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Galactica

ZeroEval

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to psp

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Data Sources