Gianluca Lombardi commited on
Commit ·
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Parent(s): 8cea212
Upload cluster data as single files and compressed archive, update README
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- README.md +62 -7
- clusters-all/train/clusters-all.tar.gz +3 -0
- clusters/mapping.json +0 -0
- clusters/train/10/107l_A.parquet +3 -0
- clusters/train/11/11as_A.parquet +3 -0
- clusters/train/12/12e8_H.parquet +3 -0
- clusters/train/13/13pk_A.parquet +3 -0
- clusters/train/15/155c_A.parquet +3 -0
- clusters/train/16/16vp_A.parquet +3 -0
- clusters/train/19/1914_A.parquet +3 -0
- clusters/train/1a/1a02_J.parquet +3 -0
- clusters/train/1a/1a05_B.parquet +3 -0
- clusters/train/1a/1a0a_B.parquet +3 -0
- clusters/train/1a/1a0c_A.parquet +3 -0
- clusters/train/1a/1a0d_A.parquet +3 -0
- clusters/train/1a/1a0e_D.parquet +3 -0
- clusters/train/1a/1a0h_D.parquet +3 -0
- clusters/train/1a/1a0i_A.parquet +3 -0
- clusters/train/1a/1a0j_A.parquet +3 -0
- clusters/train/1a/1a0m_A.parquet +3 -0
- clusters/train/1a/1a0p_A.parquet +3 -0
- clusters/train/1a/1a0r_P.parquet +3 -0
- clusters/train/1a/1a0t_Q.parquet +3 -0
- clusters/train/1a/1a17_A.parquet +3 -0
- clusters/train/1a/1a1b_D.parquet +3 -0
- clusters/train/1a/1a1m_C.parquet +3 -0
- clusters/train/1a/1a1n_C.parquet +3 -0
- clusters/train/1a/1a1o_C.parquet +3 -0
- clusters/train/1a/1a1r_C.parquet +3 -0
- clusters/train/1a/1a1s_A.parquet +3 -0
- clusters/train/1a/1a1x_A.parquet +3 -0
- clusters/train/1a/1a21_B.parquet +3 -0
- clusters/train/1a/1a22_B.parquet +3 -0
- clusters/train/1a/1a25_B.parquet +3 -0
- clusters/train/1a/1a27_A.parquet +3 -0
- clusters/train/1a/1a2c_I.parquet +3 -0
- clusters/train/1a/1a2c_J.parquet +3 -0
- clusters/train/1a/1a2o_B.parquet +3 -0
- clusters/train/1a/1a2x_B.parquet +3 -0
- clusters/train/1a/1a2y_B.parquet +3 -0
- clusters/train/1a/1a2z_A.parquet +3 -0
- clusters/train/1a/1a30_C.parquet +3 -0
- clusters/train/1a/1a37_Q.parquet +3 -0
- clusters/train/1a/1a38_Q.parquet +3 -0
- clusters/train/1a/1a3a_A.parquet +3 -0
- clusters/train/1a/1a3e_I.parquet +3 -0
- clusters/train/1a/1a3i_A.parquet +3 -0
- clusters/train/1a/1a3i_B.parquet +3 -0
- clusters/train/1a/1a3q_B.parquet +3 -0
- clusters/train/1a/1a3r_P.parquet +3 -0
README.md
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- config_name: default
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data_files: default/data.parquet
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- config_name: clusters
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- config_name: id05
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data_files:
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- split: train
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# *SoftDis* dataset
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*SoftDis* is a dataset for the exploration of disordered regions in
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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 Bankh (PDB).
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## Dataset Structure
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- config_name: default
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data_files: default/data.parquet
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- config_name: clusters
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data_dir: clusters
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- config_name: clusters-all
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data_dir: clusters-all
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- config_name: id05
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data_files:
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- split: train
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# *SoftDis* dataset
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*SoftDis* is a dataset for the exploration of disordered regions in
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protein structures, and their relations with interacting sites.
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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),
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as a general term for regions in a protein identified as flexible
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(characterized by high B-factor) or intermittently missing across different
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X-ray crystal structures of the same sequence. The definition is derived from
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an extensive analysis of clusters of alternative structures for very similar
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protein sequences in the Protein Data Bankh (PDB).
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The current version of the dataset is based on the structures in the PDB up to 15-10-2024.
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## Dataset construction
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To construct the SoftDis database, we retrieved protein
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structures from the PDB archive (snapshot as of 15-10-2024)
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and clustered sequences using MMSeqs2 [(Steinegger and Söding,
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2017)](https://www.nature.com/articles/nbt.3988) at 90% sequence identity and 90%
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coverage.
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This process yielded 64,285 clusters encompassing a total
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of 484,044 chains belonging to 229,376 structures. The average
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number of structures per cluster is 7.53, with a median of 3.
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Nearly half of the clusters (31,412) contains only 1 or 2 sequences,
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while the largest cluster includes 1,727 homologs.
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The representative chain for each cluster was selected from the
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experimental structure with the highest resolution or best R-value.
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For protein complexes, individual chains were analyzed separately
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and assigned to their respective clusters.
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Residues in each chain were labeled as missing if annotated
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under REMARK 465 in the PDB file. A residue was classified
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as soft disordered if it was either missing or had a normalized
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B-factor $b_i = (B_i − B)/σ_B > 3$, where $B_i$ represents the B-
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factor of the Cα atom, and $B$ and $σ_B$ are the mean and standard
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deviation of $B_i$ values within the chain. Additionally, residues
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were labeled as interface if they participated in protein-protein
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or protein-DNA/RNA interactions. Protein-protein binding sites
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were identified using the INTerface Builder tool [(Dequeker et al.,
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2017)](https://pubs.acs.org/doi/full/10.1021/acs.jcim.7b00360),
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where residue contacts are defined by Cα atoms within
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5 Å. Protein-DNA/RNA binding sites were determined as residues
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showing decreased accessible surface area, measured using Naccess
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(Hubbard and Thornton, 1993) with a 1.4 Å probe, upon binding.
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For each site in the representative sequence of a cluster, we
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recorded the number of times it was labeled as soft disordered
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across all chains in the cluster, excluding sites consistently labeled
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as missing. Similarly, we noted the number of times each site was
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labeled as interface.
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## Dataset Structure
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## Usage
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Data for different configurations can be loaded with the following script:
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```python
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from datasets import load_data
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dataset = load_data("CQSB/SoftDis", name=config_name)
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```
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Available configurations are specified in the dataset metadata.
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If `config_name` is not specified, the default data will be loaded, containing summary information about the 64,285 clusters.
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clusters-all/train/clusters-all.tar.gz
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clusters/mapping.json
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clusters/train/10/107l_A.parquet
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