Antibodies are crucial proteins produced by the immune system to eliminate
harmful foreign substances and have become pivotal therapeutic agents for
treating human diseases. To accelerate the discovery of antibody therapeutics,
there is growing interest in constructing language models using antibody
sequences. However, the applicability of pre-trained language models for
antibody discovery has not been thoroughly evaluated due to the scarcity of
labeled datasets. To overcome these limitations, we introduce AVIDa-SARS-CoV-2,
a dataset featuring the antigen-variable domain of heavy chain of heavy chain
antibody (VHH) interactions obtained from two alpacas immunized with severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike proteins.
AVIDa-SARS-CoV-2 includes binary labels indicating the binding or non-binding
of diverse VHH sequences to 12 SARS-CoV-2 mutants, such as the Delta and
Omicron variants. Furthermore, we release VHHCorpus-2M, a pre-training dataset
for antibody language models, containing over two million VHH sequences. We
report benchmark results for predicting SARS-CoV-2-VHH binding using VHHBERT
pre-trained on VHHCorpus-2M and existing general protein and antibody-specific
pre-trained language models. These results confirm that AVIDa-SARS-CoV-2
provides valuable benchmarks for evaluating the representation capabilities of
antibody language models for binding prediction, thereby facilitating the
development of AI-driven antibody discovery. The datasets are available at
this https URL

通过使用 AVIDa-SARS-CoV-2 数据集以评估抗体语言模型的表示能力，加速抗体疗法的发现与 AI 驱动的抗体发现的发展。