| --- |
| dataset_info: |
| features: |
| - name: input_id_x |
| sequence: int64 |
| - name: input_id_y |
| sequence: int64 |
| splits: |
| - name: test |
| num_bytes: 1087504 |
| num_examples: 474 |
| - name: valid |
| num_bytes: 1124160 |
| num_examples: 474 |
| - name: train |
| num_bytes: 65391887792 |
| num_examples: 17070828 |
| download_size: 810671738 |
| dataset_size: 65394099456 |
| license: mit |
| task_categories: |
| - text-generation |
| tags: |
| - biology |
| size_categories: |
| - 10M<n<100M |
| --- |
| # Dataset Card for "ProstT5Dataset" |
|
|
| * **Contributors:** Michael Heinzinger and Konstantin Weissenow, Joaquin Gomez Sanchez and Adrian Henkel, Martin Steinegger and Burkhard Rost |
| * **Licence:** MIT |
|
|
| ## Table of Contents |
| - [Overview](#overview) |
| - [Dataset Description](#dataset-description) |
| - [Data Collection and Annotation](#data-collection-and-annotation) |
| - [Data Splits](#data-splits) |
| - [Dataset Structure](#dataset-structure) |
| - [Data Fields](#data-fields) |
| - [Data Instances](#data-instances) |
| - [Data Considerations](#data-considerations) |
| - [Social Impact of Dataset](#social-impact-of-dataset) |
| - [Discussion of Biases](#discussion-of-biases) |
| - [Other Known Limitations](#other-known-limitations) |
| - [Licensing Information](#licensing-information) |
| - [Citation Information](#citation-information) |
| - [Contributions](#contributions) |
|
|
| ## Overview |
| The ProstT5Dataset is a curated collection of *tokenized* protein sequences and their corresponding structure sequences (3Di). |
| It is derived from the [AlphaFold Protein Structure Database](https://alphafold.ebi.ac.uk/) and includes various steps of clustering and quality filtering. |
| To capture 3D information of the sequence, the [3Di structure string representation](https://www.nature.com/articles/s41587-023-01773-0#Sec2) is leveraged. This format |
| captures the spatial relationship of each residue to its neighbors in 3D space, effectively translating the 3D information of the sequence. |
| The sequence tokens are generated using the [ProstT5 Tokenizer](https://huggingface.co/Rostlab/ProstT5). |
|
|
| ## Data Fields |
| - **input_id_x** (3Di Tokens): Corresponding tokenized 3Di structure representation sequences derived from the proteins. |
| - **input_id_y** (Amino Acid Tokens): Tokenized amino acid sequences of proteins. |
|
|
| ## Dataset Description |
|
|
|  |
| We compare basic protein properties (sequence length, amino acid composition, 3Di-distribution) between our |
| dataset (training, validation, test sets) and proteins obtained from the [Protein Data Bank (PDB)](https://www.rcsb.org/). Key findings include similar amino acid distributions across datasets, |
| an overrepresentation of certain 3Di-tokens (d, v, p) and helical structures in AlphaFold2 predictions compared to PDB, and a tendency for shorter protein |
| lengths in this dataset (average 206-238) relative to PDB proteins (average 255). The analysis also highlights the relationship between |
| 3Di states and secondary structures, with a notable distinction in strand-related tokens between datasets. |
|
|
| ## Data Collection and Annotation |
| The dataset began with the AlphaFold Protein Structure Database , undergoing a two-step clustering process and one step of quality filtering: |
| 1. *First Clustering:* 214M UniprotKB protein sequences were clustered using MMseqs2, resulting in 52M clusters based on pairwise sequence identity. |
| 2. *Second Clustering:* Foldseek further clustered these proteins into 18.8M clusters, expanded to 18.6M proteins by adding diverse members. |
| 3. *Quality Filtering:* Removed proteins with low pLDDT scores, short lengths, and highly repetitive 3Di-strings. The final training split contains 17M proteins. |
|
|
| ## Data Splits |
| Data splits into train, test, and, validation were created by moving whole clusters (after quality filtering - see above), to either of the sets. |
| For validation and test, we only kept representatives to avoid bias towards large families. |
| This resulted in 474 proteins for test, 474 proteins for validation and around 17M proteins for training. |
|
|
| ## Citation |
| ``` |
| @article{heinzinger2023prostt5, |
| title={ProstT5: Bilingual language model for protein sequence and structure}, |
| author={Heinzinger, Michael and Weissenow, Konstantin and Sanchez, Joaquin Gomez and Henkel, Adrian and Steinegger, Martin and Rost, Burkhard}, |
| journal={bioRxiv}, |
| pages={2023--07}, |
| year={2023}, |
| publisher={Cold Spring Harbor Laboratory} |
| } |
| ``` |
|
|
| ## Tokens to Character Mapping |
| | Amino Acid Representation | 3DI | Special Tokens | |
| |---------------------------|-----------|--------------------| |
| | 3: A | 128: a | 0: \<pad\> | |
| | 4: L | 129: l | 1: \</s\> | |
| | 5: G | 130: g | 2: \<unk\> | |
| | 6: V | 131: v | 148: \<fold2AA\> | |
| | 7: S | 132: s | 149: \<AA2fold\> | |
| | 8: R | 133: r | | |
| | 9: E | 134: e | | |
| | 10: D | 135: d | | |
| | 11: T | 136: t | | |
| | 12: I | 137: i | | |
| | 13: P | 138: p | | |
| | 14: K | 139: k | | |
| | 15: F | 140: f | | |
| | 16: Q | 141: q | | |
| | 17: N | 142: n | | |
| | 18: Y | 143: y | | |
| | 19: M | 144: m | | |
| | 20: H | 145: h | | |
| | 21: W | 146: w | | |
| | 22: C | 147: c | | |
| | 23: X | | | |
| | 24: B | | | |
| | 25: O | | | |
| | 26: U | | | |
| | 27: Z | | | |
