SARS-CoV-2 Nanobody Interaction Dataset (AVIDa-SARS-CoV-2)
1
5 commits
1 linked in READMEs
updated May 1, 2025
This dataset features the antigen-variable domain of heavy chain antibody (VHH) interactions obtained from two alpacas immunized with SARS-CoV-2 spike proteins. It includes binary labels indicating the binding or non-binding of diverse VHH sequences to 12 SARS-CoV-2 mutants, including variants such as Delta and Omicron.
The dataset is designed to evaluate the ability of computational models to predict nanobody binding across different viral variants, which is crucial for understanding antibody cross-reactivity and developing therapeutics against emerging variants.
The data was collected from experiments with two alpacas immunized with SARS-CoV-2 spike proteins. Binding assays were conducted to determine whether specific VHH sequences bind to different SARS-CoV-2 variants. Further details are described in the paper "A SARS-CoV-2 Interaction Dataset and VHH Sequence Corpus for Antibody Language Models."
The dataset is strategically split into training, validation, and test sets to evaluate model generalization:
| 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 |
precomputed antigen sequence embeddings through ESM-2 (650M)
Model performance is evaluated using:
SARS-CoV-2 Nanobody Interaction Dataset (AVIDa-SARS-CoV-2)
1
5 commits
1 linked in READMEs
updated May 1, 2025
This dataset features the antigen-variable domain of heavy chain antibody (VHH) interactions obtained from two alpacas immunized with SARS-CoV-2 spike proteins. It includes binary labels indicating the binding or non-binding of diverse VHH sequences to 12 SARS-CoV-2 mutants, including variants such as Delta and Omicron.
The dataset is designed to evaluate the ability of computational models to predict nanobody binding across different viral variants, which is crucial for understanding antibody cross-reactivity and developing therapeutics against emerging variants.
The data was collected from experiments with two alpacas immunized with SARS-CoV-2 spike proteins. Binding assays were conducted to determine whether specific VHH sequences bind to different SARS-CoV-2 variants. Further details are described in the paper "A SARS-CoV-2 Interaction Dataset and VHH Sequence Corpus for Antibody Language Models."
The dataset is strategically split into training, validation, and test sets to evaluate model generalization:
| 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 |
precomputed antigen sequence embeddings through ESM-2 (650M)
Model performance is evaluated using: