---
language: en
license: mit
source_datasets: https://doi.org/10.1016/j.cell.2024.03.027
task_categories:
  - tabular-classification
tags:
  - drug_discovery
  - cysteine
  - chemistry
  - biology
pretty_name: Cysteine Structure Database
dataset_summary: >-
  structural data regarding ligandabale and non ligandable cysteins in ~6000 proteins along with probe interaction read outs. 
  PROBE indicates one of three probes KB02, KB03, or KB05
citation: |-
  @article{
    Takahashi_et_al_2024, 
    author={Takahashi, Chong, Harrison, Bar-Peled, et al},
    doi={10.1016/j.cell.2024.03.027},
    journal={Cell},
    number={10},
    month={May}
    title={DrugMap: A quantitative pan-cancer analysis of Cysteine ligandability},
    volume={187},
    year={2024}
    url = {https://www.biorxiv.org/content/10.1101/2023.10.20.563287v1}
  } 
size_categories:
  - 1K<n<10K
config_names:
- KB02
- KB03 
- KB05
configs:
- config_name: KB02
  data_files:
  - split: test
    path: KB02_data/structure.test_KB02.csv
  - split: train
    path: KB02_data/structure.train_KB02.csv
  - split: validation
    path: KB02_data/structure.validation_KB02.csv
- config_name: KB03
  data_files:
  - split: test
    path: KB03_data/structure.test_KB03.csv
  - split: train
    path: KB03_data/structure.train_KB03.csv
  - split: validation
    path: KB03_data/structure.validation_KB03.csv
- config_name: KB05
  data_files:
  - split: test
    path: KB05_data/structure.test_KB05.csv
  - split: train
    path: KB05_data/structure.train_KB05.csv
  - split: validation
    path: KB05_data/structure.validation_KB05.csv
dataset_info:
- config_name: KB02
  features:
    - name: "uniprot_accession"
      dtype: string
      description: Uniprot ID of Protein
    - name: "pdb_id"
      dtype: string
      description: PDB Structure ID of Protein
    - name: "gene_names"
      dtype: string
      description: Gene Names Associated with Uniprot ID of Protein
    - name: "entry_name"
      dtype: string
      description: Entry Name of Protein Associated with Uniprot ID
    - name: "protein_names"
      dtype: string
      description: Protein Names Associated with Uniprot ID of Protein 
    - name: "depth"
      dtype: float64
      description: Depth of a Cystein in Protein Pocket (Å)
    - name: "absolute_sasa"
      dtype: float64
      description: Absolute Solvent Accessible Surface Area (Å^2)
    - name: "hse_up"
      dtype: int64
      description: Half-Sphere Exposure (up)
    - name: "hse_down"
      dtype: int64
      description: Half-Sphere Exposure (down)
    - name: "coord_number"
      dtype: int64
      description: coordination number
    - name: "rsa"
      dtype: float64
      description: Relative Solvent Accessible Surface Area (Å^2)
    - name: "h_nho1"
      dtype: float64
      description: Estimated h_nho1 energy (from database of secondary structure assignments in proteins-- DSSP)
    - name: "h_ohn1"
      dtype: float64
      description: Estimated h_ohn1 energy (DSSP)
    - name: "h_nho2"
      dtype: float64
      description: Estimated h_nho2 energy (DSSP)
    - name: "h_ohn2"
      dtype: float64
      description: Estimated h_ohn2 energy (DSSP)
    - name: "tco"
      dtype: float64
      description: TCO (DSSP)
    - name: "kappa"
      dtype: float64
      description: Kappa (DSSP)
    - name: "alpha"
      dtype: float64
      description: Alpha (DSSP)
    - name: "phi"
      dtype: float64
      description: Phi (DSSP)
    - name: "psi"
      dtype: float64
      description: Psi (DSSP)
    - name: "pocket"
      dtype: float64
      description: Pocket Volume (Å^3)
    - name: "interface"
      dtype: bool
      description: Cysteine Presence in Protein Interface (TRUE or FALSE)
    - name: "basic"
      dtype: float64
      description: Local Basic Content (Fraction of Local Neighbors)
    - name: "acidic"
      dtype: float64
      description: Local Acidic Content (Fraction of Local Neighbors)
    - name: "polar"
      dtype: float64
      description: Local Polar Content (Fraction of Local Neighbors)
    - name: "cysteine"
      dtype: float64
      description: Local Cysteine Content (Fraction of Local Neighbors)
    - name: "structural"
      dtype: float64
      description: Local Structural Content (Fraction of Local Neighbors)
    - name: "aliphatic"
      dtype: float64
      description: Local Aliphatic Content (Fraction of Local Neighbors)
    - name: "aromatic"
      dtype: float64
      description: Local Aromatic Content (Fraction of Local Neighbors)
    - name: "is_active"
      dtype: bool
      description: Probe Interaction Fraction Thresholded to Classify Cysteines as Active or Not (TRUE or FALSE)
    - name: "struct_motif_B"
      dtype: bool
      description: Presense of Structural Motif B (DSSP)
    - name: "struct_motif_E"
      dtype: bool
      description: Presense of Structural Motif E (DSSP)
    - name: "struct_motif_G"
      dtype: bool
      description: Presense of Structural Motif G (DSSP)
    - name: "struct_motif_H"
      dtype: bool
      description: Presense of Structural Motif H (DSSP)
    - name: "struct_motif_I"
      dtype: bool
      description: Presense of Structural Motif I (DSSP)
    - name: "struct_motif_P"
      dtype: bool
      description: Presense of Structural Motif P (DSSP)
    - name: "struct_motif_S"
      dtype: bool
      description: Presense of Structural Motif S (DSSP)
    - name: "struct_motif_T"
      dtype: bool
      description: Presense of Structural Motif T (DSSP)
    - name: "ligand_name"
      dtype: string
      description: Name of Probe Interacting with Cysteine 
    - name: "residue_number"
      dtype: int64
      description: Residue Number of Cysteine 
    - name: "ligand_smiles"
      dtype: string
      description: SMILES depiction of Probe Interacting with Cysteine 
  splits:
    - name: train
      num_bytes: 979293
      num_examples: 4685
    - name: test
      num_bytes: 136187
      num_examples: 651
    - name: validation
      num_bytes: 245076
      num_examples: 1172
      
---

# Cysteine Structure Database

The [Cysteine Structure Database] is a dataset compiled of strucutral data for 6515 cysteine sites in hundreds of proteins. 
This dataset was [published in Cell](https://www.biorxiv.org/content/10.1101/2023.10.20.563287v1) 
and is also available at the official [DrugMap Github repo](https://github.com/bplab-compbio/DrugMap/tree/main).

For each cysteine site, this database includes numerical values for Solvent Accessible Surface Area (SASA), Cysteine Depth, etc.
Additionally, each cysteine site has a probe engagement score derived from isotopic tandem orthogonal proteolysis-activity-based protein profiling 
(isoTOP-ABPP) that is represented as True or False in this dataset for three probes: KB02, KB03, KB05. 

## Probes
### KB02
SMILES: COC1=CC=C2C(CCCN2C(CCl)=O)=C1
Depiction: 

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6663058cf9690b71435e02ed/iDBhPa2i9QL_YjPuHBDtK.png)

### KB03
SMILES: FC(F)(F)C1=CC(C(F)(F)F)=CC(NC(CCl)=O)=C1
Depiction: 

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6663058cf9690b71435e02ed/81anebswZ2UDMHTM8XrVS.png)

### KB05
SMILES: O=C(C=C)N(C1=CC=C(Br)C=C1)C2=CC=CC=C2
Depiction: 

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6663058cf9690b71435e02ed/50sAyacIM2QPMDbr7PpdL.png)

## Quickstart Usage

### Load a Dataset in Python
Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.
Install the `datasets` library

    $ pip install datasets

then, in Python, load the `datasets` library

    >>> import datasets

and load one of the `Cysteine Structure Database` datasets, e.g.,

    >>> KB03_data = datasets.load_dataset('ymanasa2000/DrugMap_Ligandability', name='KB03')
    Downloading readme: 100%|████████████████████████| 4.04k/4.04k [00:00<00:00, 281kB/s]
    Downloading data: 100%|████████████████████████| 30.5k/30.5k [00:00<00:00, 470kB/s]
    Downloading data: 100%|████████████████████████| 218k/218k [00:00<00:00, 2.55MB/s]
    Downloading data: 100%|████████████████████████| 54.8k/54.8k [00:00<00:00, 915kB/s]
    Generating test split: 100%|████████████████████████| 143/0 [00:00<00:00, 4939.35 examples/s]
    Generating train split: 100%|████████████████████████| 1029/0 [00:00<00:00, 40692.99 examples/s]
    Generating validation split: 100%|████████████████████████| 258/0 [00:00<00:00, 22803.78 examples/s]

Then, inspect the loaded dataset

    >>> KB03_data
    DatasetDict({
      test: Dataset({
          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'],
          num_rows: 143
      })
      train: Dataset({
          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'],
          num_rows: 1029
      })
      validation: Dataset({
          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'],
          num_rows: 258
      })
  })

### Use a Dataset to Train a Model
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).
In this example, we will train and test on KB03 data.

First, install scikit-learn

    >>> pip install scikit-learn

then load, split, featurize, fit and evaluate the Random Forest model

    from sklearn.ensemble import RandomForestClassifier
    import pandas as pd

    KB03_data = datasets.load_dataset('ymanasa2000/DrugMap_Ligandability', name='KB03')

    # split into train and test
    KB03_train = KB03_data['train']
    KB03_test = KB03_data['test']
    
    train_set = pd.DataFrame(KB03_train)
    test_set = pd.DataFrame(KB03_test)
    
    # featurize
    X_train = train_set.drop(columns=['site', 'KB03'])
    y_train = train_set['KB03']
    
    X_test = test_set.drop(columns=['site', 'KB03'])
    y_test = test_set['KB03']

    # fit
    model_1 = RandomForestClassifier()
    model_1.fit(X_train, y_train)

    # evaluate 
    print(model_1.score(X_test, y_test)) # output: 0.5944

## About the Cysteine Structure Database

### Features of the DB
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, 
numerical values for Solvent Accessible Surface Area (SASA), Cysteine Depth, etc.
Additionally, this DB contains probe read-outs from an experiment described in Takahashi_et_al_2024. They integrated the isotopic tandem orthogonal 
proteolysis-activity-based protein profiling (isoTOP-ABPP) platform with tandem mass tag (TMT)-based mass spectrometry quantification (iso-TMT) 
to measure cysteine reactivity. In this approach, cell lysates are first treated with cysteine-reactive “scout” compounds or vehicle control, 
allowing reactive cysteines a chance to form covalent adducts, and then this is followed by a chase with a pan-cysteine-reactive probe 
(iodoacetamide-desthiobiotin `DBIA`), which reacts with all remaining free cysteine thiolate groups. 
Crucially, cysteines that reacted with the scout compound will escape being tagged by DBIA. 
Ligandable cysteines are defined as those that are engaged (ε-value) >60% by cysteine-reactive compounds.

### Data splits
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. 

### Citation
Please use the following citation in any publication using our Cysteine Structure Dataset: 

```md
@article{
    Takahashi_et_al_2024, 
    author={Takahashi, Chong, Harrison, Bar-Peled, et al},
    doi={10.1016/j.cell.2024.03.027},
    journal={Cell},
    number={10},
    month={May}
    title={DrugMap: A quantitative pan-cancer analysis of Cysteine ligandability},
    volume={187},
    year={2024}
    url = {https://www.biorxiv.org/content/10.1101/2023.10.20.563287v1}
  } 
```