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The best starting point depends on whether you need an experimentally determined structure, a proteome-scale prediction, or data for training a machine-learning model. Use the RCSB Protein Data Bank (RCSB PDB) for deposited experimental and integrative structures; use the AlphaFold Protein Structure Database (AlphaFold DB) for large collections of predicted models; and use ProteinNet, OpenProteinSet, or OpenFold3 training data for model development and benchmarking.
This guide explains what each resource contains, how to search or download it, which evidence each file represents, and how to keep a protein-design workflow reproducible.
Choose a source by the decision you need to make
Protein-design projects commonly fail at the data-selection stage rather than at the modeling stage. A ligand-bound crystal structure can answer questions that a predicted apo model cannot, while a proteome-scale prediction archive is far more useful than manually collecting thousands of individual experimental entries. Training a new sequence–structure model requires yet another type of resource: standardized splits, alignments, metadata, and clear licensing.
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|---|---|---|---|
| RCSB Protein Data Bank | Experimentally determined and integrative coordinates, biological assemblies, ligands, and related metadata | Designing around a known fold, interface, ligand pocket, oligomer, or experimentally observed conformation | Targeted entry and search workflows |
| AlphaFold Protein Structure Database | Computationally predicted protein structures, with bulk archives and model identifiers | Obtaining a starting model when no suitable experiment exists, or surveying many proteins consistently | More than 200 million predicted structures |
| ProteinNet | Standardized data series for training and assessing data-driven sequence–structure models | Comparable training and benchmark experiments | Dataset releases with standardized organization |
| OpenProteinSet | More than 16 million multiple-sequence alignments (MSAs) with associated structural homologs from the PDB and AlphaFold2 predictions | Large-scale structure, function, and design machine-learning tasks | More than 16 million MSAs |
| OpenFold3 training data | MSAs and predicted structures used for an open-source all-atom ligand, RNA, and protein structure-prediction system | Reproducing or extending OpenFold-style training pipelines | A registry description lists 245,000 PDB structures and alignments among the inputs |
The categories are complementary, not interchangeable. A predicted coordinate file is not experimental confirmation, and a benchmark corpus is not necessarily the right source for selecting one production design.
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RCSB PDB: the main source for experimental structures
What it contains
RCSB PDB is the primary discovery portal for experimentally determined and integrative Protein Data Bank structures. The archive covers three-dimensional structures of proteins, DNA, and RNA. Entries can include deposited coordinates, ligands, biological assemblies, sequence identifiers, experimental-method information, and other annotations.
Use an experimental entry when the design depends on an observed conformation: for example, the geometry of a catalytic site, a ligand-bound pocket, a protein–protein interface, a multimeric assembly, or a nucleic-acid complex. Integrative entries can combine several evidence types, so read the entry’s method and validation information before treating every coordinate as equivalent.
How to search
The basic-search interface accepts several kinds of queries:
- Protein, complex, gene, or functional keywords.
- PDB identifiers for a known entry.
- UniProt or GenBank identifiers to find structures associated with a sequence record.
- Raw or related sequences when you need homologous structures rather than an exact name match.
- Ligands and chemical identifiers when the pocket or bound molecule is the starting point.
- Computed-structure-model IDs, allowing discovery across deposited and computed models.
After finding candidates, compare construct boundaries, mutations, oligomeric state, bound ligands, resolution or other method-specific evidence, and whether the deposited coordinates represent the biological assembly you intend to design. Download the coordinate and metadata files together; the sequence in a structure entry may describe only the construct used experimentally, not the full reference protein.
When no single entry is perfect
Design work often requires combining evidence from several entries. One structure may have the best ligand geometry, another the most complete chain, and a third the relevant oligomer. Keep the PDB identifier for every structure used and record which chains, residues, ligands, and assembly definitions informed each design decision. Do not silently merge coordinates from different constructs without documenting the operation.
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AlphaFold DB: predicted structures at proteome scale
What the archive provides
AlphaFold DB offers open access to more than 200 million predicted protein structures. Its download page provides bulk archives for model-organism proteomes, global-health proteomes, and the manually curated UniProt Swiss-Prot set. Archives contain compressed PDB and mmCIF files, making them suitable for scripted retrieval and large-scale local processing.
Collaborator datasets may include coordinate files and, where available, MSAs. The database documentation states that its data are available for academic and commercial use under CC-BY-4.0. That permission does not remove the need to preserve attribution or to check the terms attached to a particular release and any external component.
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Use AlphaFold DB when an experimental PDB entry does not exist, when the available experiment covers only part of the sequence, or when you need a consistent structural survey across many proteins. Bulk archives are especially useful for screening homologs, mapping a protein family, or creating a first-pass set of candidate folds before choosing targets for experimental work.
How to use a predicted model responsibly
A predicted structure is a hypothesis, not a measurement. Before using it to place mutations or design an interface:
- Inspect the model’s confidence information and identify low-confidence segments.
- Check for predicted disorder, flexible tails, and domain boundaries that could make a rigid-body design misleading.
- Confirm that the model’s sequence and accession match the construct or isoform in your project.
- Compare the prediction with homologous experimental structures in RCSB PDB when they exist.
- Use experimental validation for important design decisions, especially those involving active sites, interfaces, or conformational switching.
Do not describe an AlphaFold DB coordinate as an experimentally solved structure in a report or dataset card. Preserve the accession, release context, downloaded file, and confidence information so another researcher can retrieve the same model.
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Datasets for training and benchmarking models
ProteinNet for standardized evaluation
ProteinNet is a standardized dataset series for training and assessing data-driven sequence–structure models. Choose it when your priority is a controlled comparison between methods, reproducible benchmark splits, or a published evaluation protocol. Its value is organization and comparability rather than simply the largest possible number of structures.
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OpenProteinSet for large MSA coverage
OpenProteinSet reports an open corpus of more than 16 million MSAs with associated structural homologs from the PDB and AlphaFold2 predictions. It is designed for structure, function, and protein-design machine-learning tasks where broad evolutionary context matters.
MSAs can dominate storage and compute requirements. Plan for separate storage of alignment files, structural coordinates, sequence identifiers, and the mapping that joins them. If you filter sequences, trim columns, or remove redundancy, save those rules and the resulting manifest. Otherwise, a later user cannot determine whether a model learned from the published corpus or from a materially different derivative.
OpenFold3 training data for an open all-atom pipeline
The OpenFold3 training data are distributed through the AWS Open Data Registry. The registry description lists MSAs and predicted structures used to train an open-source all-atom system for ligand, RNA, and protein structure prediction, including 245,000 PDB structures and alignments among the inputs.
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This resource is most appropriate when you are reproducing or extending an OpenFold-style training pipeline and need its broad input distribution. Read the registry description and the associated OpenFold documentation before downloading: the training corpus may be substantially larger than a typical benchmark and may require cloud-oriented transfer, local caching, and substantial preprocessing.
OpenFold states that its training and inference stack and training datasets are released under Apache 2.0. Confirm the license for the exact release you use and for every external dataset or component included in a derivative pipeline.
Evidence, scale, and file support: the practical trade-offs
| Question | RCSB PDB | AlphaFold DB | ProteinNet, OpenProteinSet, or OpenFold3 |
|---|---|---|---|
| What is the evidence? | Experimental or integrative deposited structure | Computational prediction | Curated training inputs, predictions, alignments, and benchmark organization |
| How do I discover data? | Keyword, sequence, motif, chemical, structure-similarity, identifier, and ligand searches | Entry lookup and bulk archive downloads | Dataset documentation, manifests, registries, and release files |
| What files may be present? | PDB/mmCIF coordinates, chain and ligand records, assemblies, and annotations | Compressed PDB/mmCIF coordinates; collaborator packages may also include MSAs | Sequences, MSAs, structures or predictions, labels, mappings, and benchmark splits, depending on the dataset |
| Best scale | One target or a carefully selected set of entries | Proteome-scale coverage | Model training, systematic evaluation, or pipeline reproduction |
| License point to verify | Entry and component-specific attribution requirements | CC-BY-4.0 availability is documented for the database data | OpenFold documents Apache 2.0 for its stack and training datasets; verify every included component |
File format alone does not indicate quality. PDB and mmCIF can both hold coordinates, but the surrounding metadata determines chain identity, residue numbering, ligands, assemblies, and provenance. A machine-learning dataset may provide an MSA and a structure without packaging the same annotations you would expect from an archive entry.
A reproducible workflow from target to design dataset
- Define the design question. Decide whether you need an observed ligand pocket, an interface, a full-length fold, a family-wide survey, a benchmark, or training data. This determines the evidence standard and scale.
- Search RCSB PDB first for experimental evidence. Query the target name, UniProt accession, GenBank identifier, sequence, ligand, or motif. Review construct boundaries, biological assembly, method, and relevant annotations before downloading.
- Fill gaps with AlphaFold DB. If no suitable experimental structure exists, retrieve the matching model, record its accession and release context, and inspect confidence and disorder before using it as a design template.
- Select a training corpus deliberately. Choose ProteinNet for standardized splits and assessment, OpenProteinSet for large MSA coverage and associated structural homologs, or OpenFold3 training data when you need the OpenFold training distribution.
- Build an explicit manifest. For every file, record the resource name, accession or identifier, chain or sequence, release or version, download date, file format, and license or attribution requirement.
- Validate mappings. Check that sequence identifiers, residue numbering, chain labels, MSA entries, and coordinate files refer to the intended protein. Resolve truncations, isoforms, mutations, and duplicate records before model training.
- Preserve preprocessing. Save scripts or configuration for filtering, alignment trimming, structure cleaning, atom selection, train–validation–test splitting, and augmentation. Keep checksums or an immutable archive of downloaded inputs where practical.
- Separate hypothesis from evidence. Label predicted coordinates and derived annotations as computational. Reserve experimental language for deposited experimental or integrative results, and plan validation for designs whose success depends on uncertain regions.
Common failure modes and fixes
Using a prediction as if it were an experiment
Symptom: A design relies on a precise contact or side-chain geometry in a low-confidence region. Fix: compare homologous experimental structures, inspect confidence and disorder, and treat the contact as a hypothesis requiring validation.
Downloading the wrong construct or isoform
Symptom: Residue numbers do not line up with the sequence used in design. Fix: verify the UniProt or GenBank mapping, construct boundaries, chain identifiers, insertions, deletions, and engineered mutations before alignment or modeling.
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Symptom: Reported benchmark scores cannot be recreated. Fix: retain the exact ProteinNet release or other dataset version, split files, filtering rules, and identifier mappings alongside the trained model.
Ignoring licensing and attribution
Symptom: A commercial or public release omits required credit or includes a component with different terms. Fix: record the license for each source and derivative, preserve required attribution, and recheck terms whenever a dataset release changes.
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How to choose quickly
- You need one reliable structure for a known target: start with RCSB PDB and prioritize an entry that matches the sequence, assembly, ligand state, and design question.
- You need coverage for thousands of proteins: use AlphaFold DB bulk archives, then filter by accession, sequence, confidence, disorder, and domain boundaries.
- You are comparing structure-prediction or design models: use ProteinNet or another documented benchmark with fixed splits.
- You need evolutionary context at very large scale: evaluate OpenProteinSet and its MSA-to-structure mappings.
- You are rebuilding an open all-atom training pipeline: inspect OpenFold3 training data and the OpenFold Apache 2.0 release terms.
The most defensible projects often combine these sources: experimental structures anchor interpretation, AlphaFold DB fills coverage gaps, and curated datasets support learning and evaluation. Keeping evidence labels and provenance attached to every coordinate prevents a convenient computational model from being mistaken for a measured fact.
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