SoftDis dataset

SoftDis is a dataset for the exploration of disordered regions in protein structures, and their relations with interacting sites.

The concept of soft disorder was introduced in (Seoane and Carbone, 2021), as a general term for regions in a protein identified as flexible (characterized by high B-factor) or intermittently missing across different X-ray crystal structures of the same sequence. The definition is derived from an extensive analysis of clusters of alternative structures for very similar protein sequences in the Protein Data Bank (PDB).

The current version of the dataset is based on the structures in the PDB up to 15-10-2024.

Dataset construction

To construct the SoftDis database, we retrieved protein structures from the PDB archive and clustered sequences using MMSeqs2 (Steinegger and Söding,2017) at 90% sequence identity and 90% coverage.
This process yielded 64,285 clusters encompassing a total of 484,044 chains, belonging to 229,376 structures. The average number of structures per cluster is 7.53, with a median of 3. Nearly half of the clusters (31,412) contain only 1 or 2 sequences, while the largest cluster includes 1,727 homologs.
The representative chain for each cluster was selected from the experimental structure with the highest resolution or best R-value. For protein complexes, individual chains were analyzed separately and assigned to their respective clusters.

Residues in each chain were labeled as missing if annotated under REMARK 465 in the PDB file. A residue was classified as soft disordered if it was either missing or had a normalized B-factor $b_i = (B_i − B)/σ_B > 3$, where $B_i$ represents the B- factor of the Cα atom, and $B$ and $σ_B$ are the mean and standard deviation of $B_i$ values within the chain. Additionally, residues were labeled as interface if they participated in protein-protein or protein-DNA/RNA interactions. Protein-protein binding sites were identified using the INTerface Builder tool (Dequeker et al.,2017), where residue contacts are defined by Cα atoms within 5 Å. Protein-DNA/RNA binding sites were determined as residues showing decreased accessible surface area, measured using Naccess (Hubbard and Thornton, 1993) with a 1.4 Å probe, upon binding. For each site in the representative sequence of a cluster, we recorded the number of times it was labeled as soft disordered across all chains in the cluster, excluding sites consistently labeled as missing. Similarly, we noted the number of times each site was labeled as interface.

Usage

Data for different configurations can be loaded using HuggingFace datasets library with the following script:

from datasets import load_dataset

dataset = load_dataset("CQSB/SoftDis", name=config_name)

The function returns a datasets.DatasetDict object, whose items can be accessed by specifying the corresponding split, as in the following

# Assign splits to different datasets.Dataset objects
train_data, val_data, test_data = load_dataset("CQSB/SoftDis", "id05")

# Load default `train` split
train_data = load_dataset("CQSB/SoftDis", split="train")

# Access `train` split after loading
data = load_dataset("CQSB/SoftDis", "id05")
train_data = data['train']

Available configurations

Available configurations are specified in the dataset metadata. We report details about each of them in the following table:

Name	Splits	Num. samples	Description
default	train	64,285	Summary data for all clusters
id05	train, validation, test	26,752 | 1,155 | 2,523	Clusters data filtered at 50% identity
id07	train, validation, test	32,287 | 1,146 | 2,250	Clusters data filtered at 70% identity
id09	train, validation, test	38,253 | 1,068 | 2,282	Clusters data filtered at 90% identity
clusters-all	train	484,044	Detailed info for all chains in each cluster (archive)
clusters	train	484,044	Detailed info for all chains in each cluster (single files)

If config_name is not specified, default data are loaded.

To load data for all chains, clusters-all configuration is recommended, since it only downloads and decompresses a single file (2.66 GB compressed). After the first download and loading, that might take some minutes, the dataset is cached locally on disk using Arrow framework. When needed, the cached dataset is memory-mapped directly from the disk (which offers fast lookup) instead of being loaded in memory. For more information, visit this link.

cluster configuration can instead be useful to inpect information about one or few clusters, without the need to download the full database. Necessary files can be loaded with the following syntax:

dataset = load_dataset(
    "CQSB/SoftDis", "clusters", data_files="clusters/train/*/4zne_E.parquet"
)

Each cluster is stored as <cluster_id>.parquet file in a subdirectory of clusters/train directory. The name of the subirectory corresponds to the first 2 symbols of <cluster_id> (in the example, 4z), but * wildcard can be also used.

Dataset Structure

Data instances

A sample item for configurations default, id05, id07, id09 has the following features:

• 'id': Value(dtype='string'): cluster identifier
• 'sequence': Value(dtype='string'): protein sequence for cluster representative
• 'homologs': Sequence(feature=Value(dtype='string')): chain IDs contained in the cluster
• 'num_homologs': Value(dtype='int64'): number of chains in the cluster
• 'missing_frequency': Sequence(feature=Value(dtype='float32')): fraction of missing residues in the cluster for each position
• 'soft_disorder_frequency': Sequence(feature=Value(dtype='float32')): fraction of soft-disordered residues in the cluster for each position
• 'protein_interface_frequency': Sequence(feature=Value(dtype='float32')): fraction of residues in the cluster in a protein-protein interface for each position
• 'nucleic_acid_interface_frequency': Sequence(feature=Value(dtype='float32')): fraction of residues in the cluster in a protein-DNA/RNA interface for each position
• 'interface_frequency': Sequence(feature=Value(dtype='float32')): union of protein-protein and protein-DNA/RNA interfaces

To convert frequencies to binary labels for classification tasks, the best option is to process data with map() method (see documentation here):

# Assign positive label to soft-disordered residues
def binarize(sample):
    sample["soft_disorder_class"] = (
        sample["soft_disorder_frequency"] > 0
    ).astype(np.int64)
    return sample

dataset.set_format(type="numpy")  # convert items to numpy objects
dataset = dataset.map(binarize)   # apply function to all entries

A sample for configurations clusters or clusters-all has instead the following features:

• 'id': Value(dtype='string'): chain identifier
• 'sequence': Value(dtype='string'): chain sequence
• 'missing': Sequence(feature=Value(dtype='bool')): boolean list of missing residues
• 'soft_disorder': Sequence(feature=Value(dtype='bool')): boolean list of soft-disordered residues
• 'protein_interface': Sequence(feature=Value(dtype='bool')): boolean list of residues at protein-protein interface
• 'nucleic_acid_interface': Sequence(feature=Value(dtype='bool')): boolean list of residues at protein-DNA/RNA interface
• 'bfactors': Sequence(feature=Value(dtype='float64')): list of B-factor for each residue (Cα atom)
• 'residue_ids': Sequence(feature=Value(dtype='string')): list of residue identifiers, as reported in PDB file
• 'coords': Sequence(feature=Sequence(feature=Value(dtype='float64', id=None))): L x 3 array with Cα coordinates. Coordinates of missing residues are set to NaN.