README.md

---
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: cc
task_categories:
- text-generation
tags:
- biology
size_categories:
- 10M<n<100M
---
# Dataset Card for "ProstT5Dataset"

# ⚠ Currently under review ⚠

* **Contributors:** Michael Heinzinger and Konstantin Weissenow, Joaquin Gomez Sanchez and Adrian Henkel, Martin Steinegger and Burkhard Rost
* **Licence:** TBD

## 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

![image/png](https://cdn-uploads.huggingface.co/production/uploads/62c412251f45e8bdb2b05855/BgiKOoFUGjlHDPjbxJWOX.png)
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.

## 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                     |           |                    |