metadata

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

Overview
Dataset Description
- Data Collection and Annotation
- Data Splits
Dataset Structure
- Data Fields
- Data Instances
Data Considerations
Licensing Information
Citation Information
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 and includes various steps of clustering and quality filtering. To capture 3D information of the sequence, the 3Di structure string representation 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.

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). 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:

First Clustering: 214M UniprotKB protein sequences were clustered using MMseqs2, resulting in 52M clusters based on pairwise sequence identity.
Second Clustering: Foldseek further clustered these proteins into 18.8M clusters, expanded to 18.6M proteins by adding diverse members.
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

Datasets:

Rostlab
/

ProstT5Dataset