name stringlengths 17 17 | source stringclasses 2
values | PDB stringlengths 31.8k 1.09M |
|---|---|---|
1g6v_A-1g6v_B.pdb | dockground | ATOM 1 N HIS A 1 72.616 83.828 77.161 1.00 34.87 N ATOM 2 CA HIS A 1 73.953 83.572 76.548 1.00 34.87 C ATOM 3 C HIS A 1 74.018 82.111 76.097 1.00 34.87 C ATOM 4 O HIS A 1 73.472 81.756 75.049 1.00 34.87 O ATOM 5 CB HIS A 1 75.060 83.878 77.571 1.00 67.72 C ATOM 6 CG HIS A 1 76.444 83.892 76.993 1.00 67.72 C ATOM 7 CD2 ... |
1y8x_A-1y8x_B.pdb | dockground | "ATOM 5 N SER A 5 7.793 -22.957 40.073 1.00 80.87 N ATOM 6 CA SER A 5 7.487 -22.801 41.495 1.00 81.6(...TRUNCATED) |
3lwn_A-3lwn_B.pdb | dockground | "ATOM 1 N ASN A 6 16.343 39.233 13.905 1.00 31.92 N ATOM 2 CA ASN A 6 15.168 39.701 14.722 1.00 32.4(...TRUNCATED) |
1clv_A-1clv_B.pdb | dockground | "ATOM 9 N LYS A 1 29.983 7.994 5.735 1.00 14.51 N ATOM 10 CA LYS A 1 30.178 8.269 4.313 1.00 13.28 C(...TRUNCATED) |
2aq3_A-2aq3_B.pdb | dockground | "ATOM 1 N GLU A 1 1.070 -14.962 51.952 1.00 84.06 N ATOM 2 CA GLU A 1 0.659 -15.418 50.546 1.00 84.7(...TRUNCATED) |
3pv6_A-3pv6_B.pdb | dockground | "ATOM 1 N ALA A 1 6.661 16.168 -1.933 1.00 64.14 N ATOM 2 CA ALA A 1 5.902 17.425 -1.671 1.00 64.75 (...TRUNCATED) |
1tmq_A-1tmq_B.pdb | dockground | "ATOM 9 N LYS A 1 53.334 81.008 115.494 1.00 25.42 N ATOM 10 CA LYS A 1 53.447 80.000 114.482 1.00 2(...TRUNCATED) |
2o8v_A-2o8v_B.pdb | dockground | "ATOM 1 N SER A 1 3.418 -81.475 22.284 1.00 87.64 N ATOM 2 CA SER A 1 4.179 -80.306 21.758 1.00 87.9(...TRUNCATED) |
3k9m_A-3k9m_B.pdb | dockground | "ATOM 1 N LEU A 3 -7.682 2.278 14.083 1.00 44.19 N ATOM 2 CA LEU A 3 -9.084 1.819 14.325 1.00 44.72 (...TRUNCATED) |
4nzl_A-4nzl_B.pdb | dockground | "ATOM 1 N ILE A 1 -35.483 25.514 -11.663 1.00 15.90 N ATOM 2 CA ILE A 1 -34.343 25.023 -12.450 1.00 (...TRUNCATED) |
FoldDock
Use AlphaFold2 to predict structures for 65,484 human protein interactions. We show that experiments can orthogonally confirm higher-confidence models. We identify 3,137 high-confidence models, of which 1,371 have no homology to a known structure.>
Quick Start Usage
Install Hugging Face Datasets Packages
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 <rmauder/FoldDock> model datasets, use datasets.load_dataset(...):
>>> dataset_tag = "<DATASET TAG>" >>> dataset = datasets.load_dataset( path = "<HF PATH TO DATASET>", name = f"{dataset_tag}", data_dir = f"{dataset_tag}")['train']
and the dataset is loaded as a datasets.arrow_dataset.Dataset
>>> dataset <RESULT OF LOADING DATASET MODEL>
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
Dataset Description
Acknowledgements:
License: apache-2.0
Dataset Sources - Repository: https://doi.org/10.17044/scilifelab.16866202.v1 - Paper: References
Bryant, P., Pozzati, G., & Elofsson, A. (2021). Data and most relevant results for the FoldDock project [Data set]. doi:10.17044/SCILIFELAB.16866202.V1
Uses
Out-of-Scope Use
Source Data
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} }
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