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--- |
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language: en |
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license: mit |
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source_datasets: https://doi.org/10.1016/j.cell.2024.03.027 |
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task_categories: |
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- tabular-classification |
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tags: |
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- drug_discovery |
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- cysteine |
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- chemistry |
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- biology |
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pretty_name: Cysteine Structure Database |
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dataset_summary: >- |
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structural data regarding ligandabale and non ligandable cysteins in ~6000 proteins along with probe interaction read outs. |
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PROBE indicates one of three probes KB02, KB03, or KB05 |
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citation: |- |
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@article{ |
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Takahashi_et_al_2024, |
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author={Takahashi, Chong, Harrison, Bar-Peled, et al}, |
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doi={10.1016/j.cell.2024.03.027}, |
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journal={Cell}, |
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number={10}, |
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month={May} |
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title={DrugMap: A quantitative pan-cancer analysis of Cysteine ligandability}, |
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volume={187}, |
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year={2024} |
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url = {https://www.biorxiv.org/content/10.1101/2023.10.20.563287v1} |
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} |
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size_categories: |
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- 1K<n<10K |
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config_names: |
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- KB02 |
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- KB03 |
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- KB05 |
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configs: |
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- config_name: KB02 |
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data_files: |
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- split: test |
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path: KB02_data/structure.test_KB02.csv |
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- split: train |
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path: KB02_data/structure.train_KB02.csv |
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- split: validation |
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path: KB02_data/structure.validation_KB02.csv |
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- config_name: KB03 |
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data_files: |
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- split: test |
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path: KB03_data/structure.test_KB03.csv |
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- split: train |
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path: KB03_data/structure.train_KB03.csv |
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- split: validation |
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path: KB03_data/structure.validation_KB03.csv |
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- config_name: KB05 |
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data_files: |
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- split: test |
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path: KB05_data/structure.test_KB05.csv |
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- split: train |
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path: KB05_data/structure.train_KB05.csv |
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- split: validation |
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path: KB05_data/structure.validation_KB05.csv |
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dataset_info: |
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- config_name: KB02 |
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features: |
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- name: "uniprot_accession" |
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dtype: string |
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description: Uniprot ID of Protein |
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- name: "pdb_id" |
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dtype: string |
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description: PDB Structure ID of Protein |
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- name: "gene_names" |
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dtype: string |
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description: Gene Names Associated with Uniprot ID of Protein |
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- name: "entry_name" |
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dtype: string |
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description: Entry Name of Protein Associated with Uniprot ID |
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- name: "protein_names" |
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dtype: string |
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description: Protein Names Associated with Uniprot ID of Protein |
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- name: "depth" |
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dtype: float64 |
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description: Depth of a Cystein in Protein Pocket (Å) |
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- name: "absolute_sasa" |
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dtype: float64 |
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description: Absolute Solvent Accessible Surface Area (Å^2) |
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- name: "hse_up" |
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dtype: int64 |
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description: Half-Sphere Exposure (up) |
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- name: "hse_down" |
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dtype: int64 |
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description: Half-Sphere Exposure (down) |
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- name: "coord_number" |
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dtype: int64 |
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description: coordination number |
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- name: "rsa" |
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dtype: float64 |
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description: Relative Solvent Accessible Surface Area (Å^2) |
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- name: "h_nho1" |
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dtype: float64 |
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description: Estimated h_nho1 energy (from database of secondary structure assignments in proteins-- DSSP) |
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- name: "h_ohn1" |
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dtype: float64 |
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description: Estimated h_ohn1 energy (DSSP) |
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- name: "h_nho2" |
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dtype: float64 |
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description: Estimated h_nho2 energy (DSSP) |
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- name: "h_ohn2" |
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dtype: float64 |
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description: Estimated h_ohn2 energy (DSSP) |
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- name: "tco" |
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dtype: float64 |
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description: TCO (DSSP) |
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- name: "kappa" |
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dtype: float64 |
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description: Kappa (DSSP) |
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- name: "alpha" |
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dtype: float64 |
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description: Alpha (DSSP) |
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- name: "phi" |
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dtype: float64 |
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description: Phi (DSSP) |
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- name: "psi" |
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dtype: float64 |
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description: Psi (DSSP) |
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- name: "pocket" |
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dtype: float64 |
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description: Pocket Volume (Å^3) |
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- name: "interface" |
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dtype: bool |
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description: Cysteine Presence in Protein Interface (TRUE or FALSE) |
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- name: "basic" |
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dtype: float64 |
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description: Local Basic Content (Fraction of Local Neighbors) |
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- name: "acidic" |
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dtype: float64 |
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description: Local Acidic Content (Fraction of Local Neighbors) |
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- name: "polar" |
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dtype: float64 |
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description: Local Polar Content (Fraction of Local Neighbors) |
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- name: "cysteine" |
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dtype: float64 |
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description: Local Cysteine Content (Fraction of Local Neighbors) |
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- name: "structural" |
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dtype: float64 |
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description: Local Structural Content (Fraction of Local Neighbors) |
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- name: "aliphatic" |
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dtype: float64 |
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description: Local Aliphatic Content (Fraction of Local Neighbors) |
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- name: "aromatic" |
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dtype: float64 |
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description: Local Aromatic Content (Fraction of Local Neighbors) |
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- name: "is_active" |
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dtype: bool |
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description: Probe Interaction Fraction Thresholded to Classify Cysteines as Active or Not (TRUE or FALSE) |
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- name: "struct_motif_B" |
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dtype: bool |
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description: Presense of Structural Motif B (DSSP) |
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- name: "struct_motif_E" |
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dtype: bool |
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description: Presense of Structural Motif E (DSSP) |
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- name: "struct_motif_G" |
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dtype: bool |
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description: Presense of Structural Motif G (DSSP) |
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- name: "struct_motif_H" |
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dtype: bool |
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description: Presense of Structural Motif H (DSSP) |
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- name: "struct_motif_I" |
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dtype: bool |
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description: Presense of Structural Motif I (DSSP) |
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- name: "struct_motif_P" |
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dtype: bool |
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description: Presense of Structural Motif P (DSSP) |
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- name: "struct_motif_S" |
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dtype: bool |
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description: Presense of Structural Motif S (DSSP) |
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- name: "struct_motif_T" |
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dtype: bool |
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description: Presense of Structural Motif T (DSSP) |
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- name: "ligand_name" |
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dtype: string |
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description: Name of Probe Interacting with Cysteine |
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- name: "residue_number" |
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dtype: int64 |
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description: Residue Number of Cysteine |
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- name: "ligand_smiles" |
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dtype: string |
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description: SMILES depiction of Probe Interacting with Cysteine |
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splits: |
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- name: train |
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num_bytes: 979293 |
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num_examples: 4685 |
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- name: test |
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num_bytes: 136187 |
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num_examples: 651 |
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- name: validation |
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num_bytes: 245076 |
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num_examples: 1172 |
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--- |
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# Cysteine Structure Database |
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The [Cysteine Structure Database] is a dataset compiled of strucutral data for 6515 cysteine sites in hundreds of proteins. |
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This dataset was [published in Cell](https://www.biorxiv.org/content/10.1101/2023.10.20.563287v1) |
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and is also available at the official [DrugMap Github repo](https://github.com/bplab-compbio/DrugMap/tree/main). |
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For each cysteine site, this database includes numerical values for Solvent Accessible Surface Area (SASA), Cysteine Depth, etc. |
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Additionally, each cysteine site has a probe engagement score derived from isotopic tandem orthogonal proteolysis-activity-based protein profiling |
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(isoTOP-ABPP) that is represented as True or False in this dataset for three probes: KB02, KB03, KB05. |
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## Probes |
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### KB02 |
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SMILES: COC1=CC=C2C(CCCN2C(CCl)=O)=C1 |
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Depiction: |
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%3C!-- HTML_TAG_END --> |
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### KB03 |
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SMILES: FC(F)(F)C1=CC(C(F)(F)F)=CC(NC(CCl)=O)=C1 |
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Depiction: |
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%3C!-- HTML_TAG_END --> |
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### KB05 |
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SMILES: O=C(C=C)N(C1=CC=C(Br)C=C1)C2=CC=CC=C2 |
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Depiction: |
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%3C!-- HTML_TAG_END --> |
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## Quickstart Usage |
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### Load a Dataset in Python |
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Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library. |
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Install the `datasets` library |
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$ pip install datasets |
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then, in Python, load the `datasets` library |
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>>> import datasets |
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and load one of the `Cysteine Structure Database` datasets, e.g., |
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>>> KB03_data = datasets.load_dataset('ymanasa2000/DrugMap_Ligandability', name='KB03') |
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Downloading readme: 100%|████████████████████████| 4.04k/4.04k [00:00<00:00, 281kB/s] |
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Downloading data: 100%|████████████████████████| 30.5k/30.5k [00:00<00:00, 470kB/s] |
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Downloading data: 100%|████████████████████████| 218k/218k [00:00<00:00, 2.55MB/s] |
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Downloading data: 100%|████████████████████████| 54.8k/54.8k [00:00<00:00, 915kB/s] |
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Generating test split: 100%|████████████████████████| 143/0 [00:00<00:00, 4939.35 examples/s] |
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Generating train split: 100%|████████████████████████| 1029/0 [00:00<00:00, 40692.99 examples/s] |
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Generating validation split: 100%|████████████████████████| 258/0 [00:00<00:00, 22803.78 examples/s] |
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Then, inspect the loaded dataset |
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>>> KB03_data |
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DatasetDict({ |
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test: Dataset({ |
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features: ['Unnamed: 0', 'site', 'depth', 'absolute_sasa', 'hse_up', 'hse_down', 'coord_number', 'rsa', 'h_nho1', 'h_ohn1', 'h_nho2', 'h_ohn2', 'tco', 'kappa', 'alpha', 'phi', 'psi', 'pocket', 'interface', 'basic', 'acidic', 'polar', 'cysteine', 'structural', 'aliphatic', 'aromatic', 'KB03', 'struct_motif_B', 'struct_motif_E', 'struct_motif_G', 'struct_motif_H', 'struct_motif_I', 'struct_motif_P', 'struct_motif_S', 'struct_motif_T'], |
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num_rows: 143 |
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}) |
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train: Dataset({ |
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features: ['Unnamed: 0', 'site', 'depth', 'absolute_sasa', 'hse_up', 'hse_down', 'coord_number', 'rsa', 'h_nho1', 'h_ohn1', 'h_nho2', 'h_ohn2', 'tco', 'kappa', 'alpha', 'phi', 'psi', 'pocket', 'interface', 'basic', 'acidic', 'polar', 'cysteine', 'structural', 'aliphatic', 'aromatic', 'KB03', 'struct_motif_B', 'struct_motif_E', 'struct_motif_G', 'struct_motif_H', 'struct_motif_I', 'struct_motif_P', 'struct_motif_S', 'struct_motif_T'], |
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num_rows: 1029 |
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}) |
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validation: Dataset({ |
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features: ['Unnamed: 0', 'site', 'depth', 'absolute_sasa', 'hse_up', 'hse_down', 'coord_number', 'rsa', 'h_nho1', 'h_ohn1', 'h_nho2', 'h_ohn2', 'tco', 'kappa', 'alpha', 'phi', 'psi', 'pocket', 'interface', 'basic', 'acidic', 'polar', 'cysteine', 'structural', 'aliphatic', 'aromatic', 'KB03', 'struct_motif_B', 'struct_motif_E', 'struct_motif_G', 'struct_motif_H', 'struct_motif_I', 'struct_motif_P', 'struct_motif_S', 'struct_motif_T'], |
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num_rows: 258 |
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}) |
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}) |
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### Use a Dataset to Train a Model |
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One way to use the dataset is by training a Baseline Random Forest Classifier to predict intereaction of a cysteine with one of the three probes (KB02, KB03, KB05). |
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In this example, we will train and test on KB03 data. |
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First, install scikit-learn |
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>>> pip install scikit-learn |
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then load, split, featurize, fit and evaluate the Random Forest model |
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from sklearn.ensemble import RandomForestClassifier |
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import pandas as pd |
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KB03_data = datasets.load_dataset('ymanasa2000/DrugMap_Ligandability', name='KB03') |
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# split into train and test |
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KB03_train = KB03_data['train'] |
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KB03_test = KB03_data['test'] |
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train_set = pd.DataFrame(KB03_train) |
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test_set = pd.DataFrame(KB03_test) |
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# featurize |
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X_train = train_set.drop(columns=['site', 'KB03']) |
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y_train = train_set['KB03'] |
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X_test = test_set.drop(columns=['site', 'KB03']) |
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y_test = test_set['KB03'] |
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# fit |
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model_1 = RandomForestClassifier() |
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model_1.fit(X_train, y_train) |
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# evaluate |
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print(model_1.score(X_test, y_test)) # output: 0.5944 |
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## About the Cysteine Structure Database |
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### Features of the DB |
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This DB features a csv with structural data for ~6,500 bindable cysteines in hundreds of protein active sites. Each cysteine has structural data such as, |
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numerical values for Solvent Accessible Surface Area (SASA), Cysteine Depth, etc. |
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Additionally, this DB contains probe read-outs from an experiment described in Takahashi_et_al_2024. They integrated the isotopic tandem orthogonal |
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proteolysis-activity-based protein profiling (isoTOP-ABPP) platform with tandem mass tag (TMT)-based mass spectrometry quantification (iso-TMT) |
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to measure cysteine reactivity. In this approach, cell lysates are first treated with cysteine-reactive “scout” compounds or vehicle control, |
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allowing reactive cysteines a chance to form covalent adducts, and then this is followed by a chase with a pan-cysteine-reactive probe |
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(iodoacetamide-desthiobiotin `DBIA`), which reacts with all remaining free cysteine thiolate groups. |
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Crucially, cysteines that reacted with the scout compound will escape being tagged by DBIA. |
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Ligandable cysteines are defined as those that are engaged (ε-value) >60% by cysteine-reactive compounds. |
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### Data splits |
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The authors of this dataset suggested using a `Stratified Split` via the train_test_split() method which was used to produce the datasets in this Hugging Face DB. |
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### Citation |
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Please use the following citation in any publication using our Cysteine Structure Dataset: |
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```md |
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@article{ |
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Takahashi_et_al_2024, |
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author={Takahashi, Chong, Harrison, Bar-Peled, et al}, |
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doi={10.1016/j.cell.2024.03.027}, |
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journal={Cell}, |
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number={10}, |
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month={May} |
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title={DrugMap: A quantitative pan-cancer analysis of Cysteine ligandability}, |
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volume={187}, |
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year={2024} |
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url = {https://www.biorxiv.org/content/10.1101/2023.10.20.563287v1} |
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} |
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``` |
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