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license: cc-by-4.0
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# Human Interleukin-6 Nanobody Interaction Dataset (AVIDa-hIL6)

## Dataset Overview

AVIDa-hIL6 is an antigen-variable domain of heavy chain antibody (VHH) interaction dataset produced from an alpaca immunized with the human interleukin-6 (IL-6) protein. The dataset includes binary labels indicating the binding or non-binding of diverse VHH sequences to wild type and 30 mutants of the IL-6 protein.

This dataset enables the evaluation of computational models for predicting nanobody binding to different IL-6 protein variants, which is important for developing therapeutic antibodies against IL-6-related diseases and understanding antibody specificity.

## Data Collection

The data was collected from experiments with an alpaca immunized with human IL-6 protein. Binding assays were conducted to determine whether specific VHH sequences bind to wild-type IL-6 and its mutant variants. Further details are described in the paper "AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions."

## Dataset Structure

The dataset is strategically split into training, validation, and test sets to evaluate model generalization:

- **Training set**: Contains wild-type (WT) IL-6 protein data
- **Validation set**: Includes 5 randomly selected IL-6 mutants (randomly sampled 10% from the original validation set due to its large size)
- **Test set**: Comprises the remaining 25 IL-6 mutants for final performance evaluation

### File Format

#### Main Dataset File (AVIDa-hIL6.csv)

| Column | Description |
|--------|-------------|
| VHH_sequence | Amino acid sequence of VHH |
| Ag_label | Antigen Type |
| label | Binary label represented by 1 for binding pair and 0 for non-binding pair |
| subject_species | Species of the subject from which VHH was collected |
| subject_name | Name of the subject from which VHH was collected |
| subject_sex | Sex of the subject from which VHH was collected |

#### Antigen embedding File (antigen_embeddings.pt)
precomputed antigen sequence embeddings through ESM-2 (650M)

## Uses and Limitations

### Uses
- Develop models to predict nanobody binding to IL-6 and its variants
- Identify nanobodies with specific or broad recognition of IL-6 mutants
- Understand the impact of IL-6 mutations on antibody recognition
- Design therapeutic nanobodies for inflammatory and autoimmune diseases

## Evaluation Metrics

Model performance is evaluated using:
- Accuracy
- F1 Score
- Precision
- Recall
- AUROC (Area Under the Receiver Operating Characteristic curve)
- AUPRC (Area Under the Precision-Recall Curve)