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license: cc-by-4.0 |
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# Human Interleukin-6 Nanobody Interaction Dataset (AVIDa-hIL6) |
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## Dataset Overview |
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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. |
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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. |
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## Data Collection |
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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." |
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## Dataset Structure |
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The dataset is strategically split into training, validation, and test sets to evaluate model generalization: |
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- **Training set**: Contains wild-type (WT) IL-6 protein data |
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- **Validation set**: Includes 5 randomly selected IL-6 mutants (randomly sampled 10% from the original validation set due to its large size) |
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- **Test set**: Comprises the remaining 25 IL-6 mutants for final performance evaluation |
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### File Format |
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#### Main Dataset File (AVIDa-hIL6.csv) |
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| Column | Description | |
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|--------|-------------| |
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| VHH_sequence | Amino acid sequence of VHH | |
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| Ag_label | Antigen Type | |
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| label | Binary label represented by 1 for binding pair and 0 for non-binding pair | |
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| subject_species | Species of the subject from which VHH was collected | |
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| subject_name | Name of the subject from which VHH was collected | |
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| subject_sex | Sex of the subject from which VHH was collected | |
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#### Antigen embedding File (antigen_embeddings.pt) |
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precomputed antigen sequence embeddings through ESM-2 (650M) |
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## Uses and Limitations |
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### Uses |
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- Develop models to predict nanobody binding to IL-6 and its variants |
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- Identify nanobodies with specific or broad recognition of IL-6 mutants |
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- Understand the impact of IL-6 mutations on antibody recognition |
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- Design therapeutic nanobodies for inflammatory and autoimmune diseases |
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## Evaluation Metrics |
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Model performance is evaluated using: |
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- Accuracy |
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- F1 Score |
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- Precision |
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- Recall |
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- AUROC (Area Under the Receiver Operating Characteristic curve) |
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- AUPRC (Area Under the Precision-Recall Curve) |
