Datasets:

gelnesr
/

RelaxDB

license: other
license_name: non-commercial-license-dyna1
license_link: https://github.com/WaymentSteeleLab/Dyna-1/blob/main/LICENSE.txt
tags:
  - proteins
  - dyna1
  - nmr
pretty_name: RelaxDB
size_categories:
  - n<1K
viewer: true
configs:
  - config_name: main
    data_files:
      - split: relaxdb
        path: relaxdb/relaxdb_data.csv

RelaxDB Dataset

The RelaxDB and RelaxDB-CPMG datasets are curated data of relaxation-dispersion NMR data. This dataset was used to evaluate our model Dyna-1. Both the model and the datasets were introduced in our paper "Learning millisecond protein dynamics from what is missing in NMR spectra".

This HF datasets hosts the files for the RelaxDB data. More information on analysis from the paper, evaluation of the dataset using Dyna-1, or the Dyna-1 model itself can be found on GitHub.

Overview

relaxdb/relaxdb_data.csv: contains the entry ids, sequences, labels, and other metadata found in in the data/ metadata.
relaxdb/af2_pdbs: contains all of the af2 models used for input and evaluation in .pdb format
relaxdb/split_files: contains split files for the provided datasets.
data/metadata: contains .npy files describing the proteins for RelaxDB and RelaxDB-CPMG datasets. A.xlsx file with metadata of the curated proteins can be found on GitHub.
data/probs: contains saved frequencies from mBMRB-Train, stored for calculating dummy baselines.

Usage

To download the dataset, you can download the metadata table quickly with the huggingface datasets package:

from datasets import load_dataset

dataset = load_dataset("gelnesr/RelaxDB")
df = dataset["relaxdb"].to_pandas()

This dataset includes protein structure files (.pdb) stored separately from the tabular data. Each dataset row contains a column (e.g. pdb_file) with the relative path to the corresponding PDB file, for example relaxdb/af2_pdbs/4267.pdb

Metadata

Column descriptions of each protein entry in relaxdb/relaxdb_data.csv:

id: entry identifier
dataset: relaxdb or relaxdb-cpmg
pdb: relative path of AF2 predicted structure
protein_name: name of protein
uniprot_id: UniProt accesion identifier
curation_source: data source for experimental data
sequence: protein sequence
label: residue-level annotations aligned to the sequence
assignments: source of assignments if not from original source ... and others!

Labels

These labels indicate the type and availability of data for each residue:

t : no data due to disordered terminus
x: no data; R1/R2/NOE not reported
N : residue assigned, but CPMG data not reported
. : missing data
p, P : proline (not evaluated)
A : no special annotation (default)
v: fast internal motion
b : mixed fast and slow motion
^ : chemical exchange detected (Rex)
X : exchange detected via Rex criteria
Y : exchange inferred from unsuppressed R2

Citation

If you are using our code, datasets, or model, please use the following citation:

@article {Dyna-1,
    author = {Wayment-Steele, Hannah K. and El Nesr, Gina and Hettiarachchi, Ramith and Kariyawasam, Hasindu and Ovchinnikov, Sergey and Kern, Dorothee},
    title = {Learning millisecond protein dynamics from what is missing in NMR spectra},
    year = {2025},
    doi = {10.1101/2025.03.19.642801},
    journal = {bioRxiv}
}

Acknowledgements

We thank Katie Henzler-Wildman, Magnus Wolf-Watz, Elan Eisenmesser, J. Patrick Loria, Marcellus Ubbelink, George Lisi, Sam Butcher, and Nicolas Doucet for sharing data. We thank Martin Stone for sharing the Indiana Dynamics Database data his group curated in 2000.