metadata
license: apache-2.0
language:
- en
tags:
- biology
- biochemistry
- protein-protein_interfaces
pretty_name: FoldDock
source_datasets: >-
Improved prediction of protein-protein interactions using AlphaFold2 (Bryant,
P., Pozzati, G., Elofsson, A. 2022 Nature Communications)
configs:
- config_name: complexes
data_files:
- split: marks_complexes
path: data/marks_complexes-*
- split: dockground_complexes
path: data/dockground_complexes-*
dataset_info:
features:
- name: Uniprot ID 1
dtype: string
- name: Uniprot ID 2
dtype: string
- name: Sequence Length 1
dtype: float64
- name: Sequence Length 2
dtype: float64
- name: PDB
dtype: string
- name: Chain 1
dtype: string
- name: Chain 2
dtype: string
- name: N residue contacts (5Ang)
dtype: float64
- name: PFAM domains 1
dtype: string
- name: PFAM domains 2
dtype: string
- name: Chain start 1
dtype: float64
- name: Chain end 1
dtype: float64
- name: Chain start 2
dtype: float64
- name: Chain end 2
dtype: float64
- name: Protein name 1
dtype: string
- name: Gene name 1
dtype: string
- name: Protein name 2
dtype: string
- name: Gene name 2
dtype: string
- name: Organism 1
dtype: string
- name: Organism 2
dtype: string
- name: Resolution (Ang)
dtype: float64
- name: contains DNA
dtype: string
- name: contains RNA
dtype: string
- name: Org1
dtype: string
- name: Org2
dtype: string
- name: kingdom1
dtype: string
- name: kingdom2
dtype: string
- name: Sequence 1
dtype: string
- name: Sequence 2
dtype: string
- name: Concatenated_Sequence
dtype: string
splits:
- name: marks_complexes
num_bytes: 1578063
num_examples: 1675
- name: dockground_complexes
num_bytes: 214944
num_examples: 219
download_size: 1102564
dataset_size: 1793007
- config_name: quality
data_files:
- split: marks_quality
path: data/marks_quality-*
- split: dockground_quality
path: data/dockground_quality-*
dataset_info:
features:
- name: id1
dtype: string
- name: id2
dtype: string
- name: if_plddt_av_2
dtype: float64
- name: if_plddt_std_2
dtype: float64
- name: ch1_plddt_av_2
dtype: float64
- name: ch1_plddt_std_2
dtype: float64
- name: ch2_plddt_av_2
dtype: float64
- name: ch2_plddt_std_2
dtype: float64
- name: plddt_av_2
dtype: float64
- name: plddt_std_2
dtype: float64
- name: num_atoms_in_interface_2
dtype: int64
- name: num_res_in_interface_2
dtype: int64
- name: if_plddt_av_1
dtype: float64
- name: if_plddt_std_1
dtype: float64
- name: ch1_plddt_av_1
dtype: float64
- name: ch1_plddt_std_1
dtype: float64
- name: ch2_plddt_av_1
dtype: float64
- name: ch2_plddt_std_1
dtype: float64
- name: plddt_av_1
dtype: float64
- name: plddt_std_1
dtype: float64
- name: num_atoms_in_interface_1
dtype: int64
- name: num_res_in_interface_1
dtype: int64
- name: if_plddt_av_4
dtype: float64
- name: if_plddt_std_4
dtype: float64
- name: ch1_plddt_av_4
dtype: float64
- name: ch1_plddt_std_4
dtype: float64
- name: ch2_plddt_av_4
dtype: float64
- name: ch2_plddt_std_4
dtype: float64
- name: plddt_av_4
dtype: float64
- name: plddt_std_4
dtype: float64
- name: num_atoms_in_interface_4
dtype: int64
- name: num_res_in_interface_4
dtype: int64
- name: if_plddt_av_3
dtype: float64
- name: if_plddt_std_3
dtype: float64
- name: ch1_plddt_av_3
dtype: float64
- name: ch1_plddt_std_3
dtype: float64
- name: ch2_plddt_av_3
dtype: float64
- name: ch2_plddt_std_3
dtype: float64
- name: plddt_av_3
dtype: float64
- name: plddt_std_3
dtype: float64
- name: num_atoms_in_interface_3
dtype: int64
- name: num_res_in_interface_3
dtype: int64
- name: if_plddt_av_5
dtype: float64
- name: if_plddt_std_5
dtype: float64
- name: ch1_plddt_av_5
dtype: float64
- name: ch1_plddt_std_5
dtype: float64
- name: ch2_plddt_av_5
dtype: float64
- name: ch2_plddt_std_5
dtype: float64
- name: plddt_av_5
dtype: float64
- name: plddt_std_5
dtype: float64
- name: num_atoms_in_interface_5
dtype: int64
- name: num_res_in_interface_5
dtype: int64
- name: complex_id
dtype: string
- name: source
dtype: string
splits:
- name: marks_quality
num_bytes: 664969
num_examples: 1481
- name: dockground_quality
num_bytes: 96984
num_examples: 216
download_size: 742707
dataset_size: 761953
- config_name: pdbs
data_files:
- split: marks_pdbs
path: data/marks_pdbs-*
- split: dockground_pdbs
path: data/dockground_pdbs-*
dataset_info:
features:
- name: pdb_name
dtype: string
- name: pdb_path
dtype: string
splits:
- name: marks_pdbs
num_bytes: 73696
num_examples: 1504
- name: dockground_pdbs
num_bytes: 11880
num_examples: 220
download_size: 44333
dataset_size: 85576
size_categories:
- 1K<n<10K
FoldDock
A collection of 219 heterodimers from dockground benchmark 4 dataset, and 1503 heterodimeric structures from a recent study (Green, A. G. et al. Nat. Commun. 12, 1–12 (2021)) (dubbed “marks”). The benchmark dataset was used to train an original model, that model was used to predict the structures in the second dataset. This dataset is split into three subsets, each with two splits corresponding to the groups used in the source (marks, dockground).
Quickstart Usage
Install HuggingFace Datasets package
Each subset can be loaded into python using the HuggingFace datasets library. First, from the command line install the datasets library
$ pip install datasets
Optionally set the cache directory, e.g.
$ HF_HOME=${HOME}/.cache/huggingface/
$ export HF_HOME
then, from within python load the datasets library
import datasets
Load model datasets
To load one of the RosettaMegathon2026-group3/FoldDock model datasets, use datasets.load_dataset(...):
dataset = datasets.load_dataset(f"RosettaCommons/FoldDock", "complexes")
and the dataset is loaded as a datasets.arrow_dataset.Dataset
>>> print(dataset)
DatasetDict({
marks_complexes: Dataset({
features: ['Uniprot ID 1', 'Uniprot ID 2', 'Sequence Length 1', 'Sequence Length 2', 'PDB', 'Chain 1', 'Chain 2', 'N residue contacts (5Ang)', 'PFAM domains 1', 'PFAM domains 2', 'Chain start 1', 'Chain end 1', 'Chain start 2', 'Chain end 2', 'Protein name 1', 'Gene name 1', 'Protein name 2', 'Gene name 2', 'Organism 1', 'Organism 2', 'Resolution (Ang)', 'contains DNA', 'contains RNA', 'Org1', 'Org2', 'kingdom1', 'kingdom2', 'Sequence 1', 'Sequence 2', 'Concatenated_Sequence'],
num_rows: 1675
})
dockground_complexes: Dataset({
features: ['Uniprot ID 1', 'Uniprot ID 2', 'Sequence Length 1', 'Sequence Length 2', 'PDB', 'Chain 1', 'Chain 2', 'N residue contacts (5Ang)', 'PFAM domains 1', 'PFAM domains 2', 'Chain start 1', 'Chain end 1', 'Chain start 2', 'Chain end 2', 'Protein name 1', 'Gene name 1', 'Protein name 2', 'Gene name 2', 'Organism 1', 'Organism 2', 'Resolution (Ang)', 'contains DNA', 'contains RNA', 'Org1', 'Org2', 'kingdom1', 'kingdom2', 'Sequence 1', 'Sequence 2', 'Concatenated_Sequence'],
num_rows: 219
})
})
which is a column oriented format that can be accessed directly, converted in to a pandas.DataFrame, or parquet format, e.g.
dataset.data.column('<COLUMN NAME IN DATASET>')
dataset.to_pandas()
dataset.to_parquet("dataset.parquet")
Dataset Details
Subset 1: complexes
This subset contains structural information for each of the complexes (ex. The full sequence, length of sequence, origin kingdom, etc.). Marks and dockground are combined into a single subset, but the marks set contains more comprehensive information that includes Uniprot IDs and interaction site numbers.
Subset 2: pdbs
Included in this dataset are all of the complex PDBs used in the origin work, in the pdbs directory. This subset has all of the relative paths for each of the files.
Subset 3: quality
This subset contains the quality metrics for each of the predicted protein-protein pairs.
Dataset Sources
- Repository: https://doi.org/10.17044/scilifelab.16866202.v1
- Paper: Bryant, P., Pozzati, G., & Elofsson, A. (2021). Data and most relevant results for the FoldDock project [Data set]. doi:10.17044/SCILIFELAB.16866202.V1
Citation
\@misc{https://doi.org/10.17044/scilifelab.16866202.v1,
doi = {10.17044/SCILIFELAB.16866202.V1},
url = {https://figshare.scilifelab.se/articles/dataset/Data_and_most_relevant_results_for_the_FoldDock_project/16866202/1},
author = {Bryant, Patrick and Pozzati, Gabriele and Elofsson, Arne},
keywords = {Bioinformatics and computational biology not elsewhere classified},
title = {Data and most relevant results for the FoldDock project},
publisher = {Stockholm University},
year = {2021},
copyright = {Apache 2.0}
}
Dataset Card Authors
- Katie Sie katiesie/@/uw.edu
- John Bickel johndjbickel/@/gmail.com
- Ryan Mauder rmauder/@/butler.edu
- Corleigh Forrester corleighforr/@/gmail.com
Acknowledgements: Patrick Bryant, Gabriele Pozzati, Arne Elofsson
License: apache-2.0