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)