|
|
--- |
|
|
license: cc-by-4.0 |
|
|
--- |
|
|
|
|
|
# Nanobody (VHH) Affinity Prediction Dataset |
|
|
|
|
|
## Dataset Overview |
|
|
|
|
|
This dataset helps predict the binding affinity between nanobodies (VHH, single-domain antibodies from camelids) and their target antigens. Affinity is a key parameter that measures how strongly an antibody binds to its antigen, usually expressed as dissociation constant (KD) or binding free energy. |
|
|
|
|
|
High affinity is a critical property for therapeutic antibodies, so accurately predicting nanobody affinity is important for antibody engineering and screening. |
|
|
|
|
|
## Data Collection |
|
|
|
|
|
The dataset is based on experimentally measured nanobody-antigen binding affinities. Data is collected from published literature and split based on score (stratified split) |
|
|
|
|
|
## Dataset Structure |
|
|
|
|
|
The dataset is split into training, validation, and test sets. |
|
|
|
|
|
### File Format |
|
|
|
|
|
CSV files contain these columns: |
|
|
- `seq`: Nanobody amino acid sequence |
|
|
- `score`: Affinity value (typically -log10(KD) where KD is in M), higher values indicate stronger binding affinity |
|
|
|
|
|
## Uses and Limitations |
|
|
|
|
|
### Uses |
|
|
- Develop models to predict nanobody affinity |
|
|
- Help select and optimize nanobodies |
|
|
- Reduce experimental work and speed up drug development |
|
|
|
|
|
### Limitations |
|
|
- Differences in affinity measurement methods may cause data variability |
|
|
- The same antibody-antigen pair may have different affinity values under different conditions |
|
|
- The dataset may not cover all possible nanobody-antigen combinations |
|
|
|
|
|
## Evaluation Metrics |
|
|
|
|
|
Model performance is evaluated using: |
|
|
- Spearman correlation |
|
|
- R² |
|
|
- Root Mean Squared Error (RMSE) |
|
|
- Mean Absolute Error (MAE) |
