VHHCorpus-2M is a corpus that contains over two million full-length amino acid sequences of variable domain of heavy chain of heavy chain antibody (VHH). We released VHHBERT, a VHH-specific language model pre-trained using VHHCorpus-2M on the Hugging Face Hub. Further details on VHHCorpus-2M are described in our paper "A SARS-CoV-2 Interaction Dataset and VHH Sequence Corpus for Antibody Language Models.”
| Column | Description |
|---|---|
| VHH_sequence | Amino acid sequence of VHH |
| 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 |
If you use VHHCorpus-2M in your research, please cite the following paper.
@inproceedings{tsuruta2024sars,
title={A {SARS}-{C}o{V}-2 Interaction Dataset and {VHH} Sequence Corpus for Antibody Language Models},
author={Hirofumi Tsuruta and Hiroyuki Yamazaki and Ryota Maeda and Ryotaro Tamura and Akihiro Imura},
booktitle={Advances in Neural Information Processing Systems 37},
year={2024}
}
VHHCorpus-2M is a corpus that contains over two million full-length amino acid sequences of variable domain of heavy chain of heavy chain antibody (VHH). We released VHHBERT, a VHH-specific language model pre-trained using VHHCorpus-2M on the Hugging Face Hub. Further details on VHHCorpus-2M are described in our paper "A SARS-CoV-2 Interaction Dataset and VHH Sequence Corpus for Antibody Language Models.”
| Column | Description |
|---|---|
| VHH_sequence | Amino acid sequence of VHH |
| 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 |
If you use VHHCorpus-2M in your research, please cite the following paper.
@inproceedings{tsuruta2024sars,
title={A {SARS}-{C}o{V}-2 Interaction Dataset and {VHH} Sequence Corpus for Antibody Language Models},
author={Hirofumi Tsuruta and Hiroyuki Yamazaki and Ryota Maeda and Ryotaro Tamura and Akihiro Imura},
booktitle={Advances in Neural Information Processing Systems 37},
year={2024}
}