| --- |
| configs: |
| - config_name: default |
| data_files: default/data.parquet |
| - config_name: clusters |
| data_dir: clusters |
| - config_name: clusters-all |
| data_dir: clusters-all |
| - config_name: id05 |
| data_files: |
| - split: train |
| path: splits/id05/train.parquet |
| - split: validation |
| path: splits/id05/valid.parquet |
| - split: test |
| path: splits/id05/test.parquet |
| - config_name: id07 |
| data_files: |
| - split: train |
| path: splits/id07/train.parquet |
| - split: validation |
| path: splits/id07/valid.parquet |
| - split: test |
| path: splits/id07/test.parquet |
| - config_name: id09 |
| data_files: |
| - split: train |
| path: splits/id09/train.parquet |
| - split: validation |
| path: splits/id09/valid.parquet |
| - split: test |
| path: splits/id09/test.parquet |
| --- |
| |
| # 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)](https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://journals.plos.org/ploscompbiol/article%3Fid%3D10.1371/journal.pcbi.1008546&ved=2ahUKEwj43OOAmISLAxW4UKQEHRszOaIQFnoECBQQAQ&usg=AOvVaw1YXiijzojYENeuv-rxtzLD), |
| 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)](https://www.nature.com/articles/nbt.3988) 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)](https://pubs.acs.org/doi/full/10.1021/acs.jcim.7b00360), |
| 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](http://www.bioinf.manchester.ac.uk/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: |
|
|
| ```python |
| 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 |
|
|
| ```python |
| # 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](https://arrow.apache.org/) 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](https://huggingface.co/docs/datasets/about_arrow). |
|
|
| `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: |
|
|
| ```python |
| 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](https://huggingface.co/docs/datasets/process#map)): |
|
|
| ```python |
| # 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. |
| |
