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afdb-1.6M

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protein-structure
alphafold
structural-biology
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  1. README.md +127 -3
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README.md CHANGED
@@ -1,3 +1,127 @@
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- ---
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- license: cc-by-4.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: cc-by-4.0
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+ task_categories:
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+ - text-generation
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+ language:
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+ - en
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+ tags:
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+ - protein-structure
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+ - alphafold
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+ - structural-biology
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+ size_categories:
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+ - 1M<n<10M
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+ ---
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+
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+ # AFDB-1.6M — One Representative Structure Per Structural Cluster
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+
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+ A deduplicated subset of [AFDB-24M](https://huggingface.co/datasets/timodonnell/afdb-24M), containing approximately 1.6 million AlphaFold Database predicted protein structures — one per structural cluster.
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+
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+ ## How This Dataset Was Created
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+
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+ This dataset was derived from [AFDB-24M](https://huggingface.co/datasets/timodonnell/afdb-24M) using the following procedure:
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+
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+ 1. All ~24 million rows across 12,005 shards were scanned.
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+ 2. Rows were grouped by `struct_cluster_id` (structural cluster representative from AFDB Foldseek clustering).
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+ 3. For each unique `struct_cluster_id`, the single row with the **highest `global_plddt`** (global mean pLDDT confidence score) was selected.
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+ 4. The selected rows were written into new Parquet shards (2,000 rows each, ZSTD level 12 compression).
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+
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+ This yields approximately 1.6 million entries — one high-confidence representative per 3D structural fold cluster.
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+
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+ ## Dataset Summary
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+
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+ | Property | Value |
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+ |----------|-------|
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+ | Source | [AFDB-24M](https://huggingface.co/datasets/timodonnell/afdb-24M) |
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+ | Total entries | ~1.6M (one per `struct_cluster_id`) |
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+ | Selection criterion | Highest `global_plddt` per structural cluster |
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+ | Format | Apache Parquet, ZSTD compressed (level 12) |
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+ | Splits | train (98%), val (1%), test (1%) — inherited from AFDB-24M |
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+
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+ ## Schema
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+
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+ Each Parquet file contains a flat table with the following columns (same schema as AFDB-24M):
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+
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+ | Column | Type | Description |
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+ |--------|------|-------------|
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+ | `entry_id` | `string` | AFDB entry ID (e.g., `AF-A0A1C0V126-F1`) |
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+ | `uniprot_accession` | `string` | UniProt accession (e.g., `A0A1C0V126`) |
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+ | `tax_id` | `int64` | NCBI taxonomy ID |
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+ | `organism_name` | `string` | Scientific name of the organism |
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+ | `global_plddt` | `float32` | Global mean pLDDT confidence score (70–100) |
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+ | `seq_len` | `int32` | Sequence length in residues |
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+ | `seq_cluster_id` | `string` | AFDB50 sequence cluster representative entry ID |
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+ | `struct_cluster_id` | `string` | Structural cluster representative entry ID |
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+ | `split` | `string` | `train`, `val`, or `test` |
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+ | `gcs_uri` | `string` | Original GCS URI |
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+ | `cif_content` | `string` | Complete raw mmCIF file text |
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+
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+ ## Usage
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+
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+ ### Loading with PyArrow
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+
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+ ```python
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+ import pyarrow.parquet as pq
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+
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+ table = pq.read_table("shard_000000.parquet")
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+ print(table.schema)
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+ print(f"{len(table)} rows")
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+ ```
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+
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+ ### Loading with Pandas
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+
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+ ```python
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+ import pandas as pd
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+
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+ df = pd.read_parquet("shard_000000.parquet")
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+ print(df[["entry_id", "organism_name", "global_plddt", "seq_len", "split"]].head())
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+ ```
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+
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+ ### Parsing Structures with Gemmi
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+
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+ ```python
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+ import gemmi
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+
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+ row = table.to_pydict()
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+ cif_text = row["cif_content"][0]
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+ doc = gemmi.cif.read_string(cif_text)
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+ structure = gemmi.make_structure_from_block(doc.sole_block())
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+ model = structure[0]
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+ chain = model[0]
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+ print(f"{len(chain)} residues")
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+ ```
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+
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+ ## Data Source and License
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+
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+ - **AlphaFold Database** structures are provided by DeepMind and EMBL-EBI under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
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+ - **Cluster files** are from the [Steinegger lab](https://afdb-cluster.steineggerlab.workers.dev/), based on Foldseek clustering of AFDB v4 (Version 3 clusters).
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+
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+ ### Citation
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+
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+ If you use this dataset, please cite the AlphaFold Database:
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+
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+ ```bibtex
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+ @article{varadi2022alphafold,
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+ title={AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models},
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+ author={Varadi, Mihaly and Anyango, Stephen and Deshpande, Mandar and others},
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+ journal={Nucleic Acids Research},
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+ volume={50},
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+ number={D1},
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+ pages={D439--D444},
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+ year={2022},
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+ doi={10.1093/nar/gkab1061}
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+ }
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+ ```
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+
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+ And the AFDB cluster resource:
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+
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+ ```bibtex
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+ @article{barrio2024clustering,
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+ title={Clustering predicted structures at the scale of the known protein universe},
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+ author={Barrio-Hernandez, Inigo and Yeo, Jimin and Jänes, Jürgen and others},
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+ journal={Nature},
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+ volume={622},
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+ pages={637--645},
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+ year={2023},
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+ doi={10.1038/s41586-023-06510-w}
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+ }
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+ ```
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