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README.md

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-license: cc-by-4.0
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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)