|
--- |
|
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 | | | |
