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Antibodies have become an important class of therapeutic agents to treathuman diseases. To accelerate therapeutic antibody discovery, computationalmethods, especially machine learning, have attracted considerable interest forpredicting specific interactions between antibody candidates and targetantigens such as viruses and bacteria. However, the publicly available datasetsin existing works have notable limitations, such as small sizes and the lack ofnon-binding samples and exact amino acid sequences. To overcome theselimitations, we have developed AVIDa-hIL6, a large-scale dataset for predictingantigen-antibody interactions in the variable domain of heavy chain of heavychain antibodies (VHHs), produced from an alpaca immunized with the humaninterleukin-6 (IL-6) protein, as antigens. By leveraging the simple structureof VHHs, which facilitates identification of full-length amino acid sequencesby DNA sequencing technology, AVIDa-hIL6 contains 573,891 antigen-VHH pairswith amino acid sequences. All the antigen-VHH pairs have reliable labels forbinding or non-binding, as generated by a novel labeling method. Furthermore,via introduction of artificial mutations, AVIDa-hIL6 contains 30 differentmutants in addition to wild-type IL-6 protein. This characteristic providesopportunities to develop machine learning models for predicting changes inantibody binding by antigen mutations. We report experimental benchmark resultson AVIDa-hIL6 by using machine learning models. The results indicate that theexisting models have potential, but further research is needed to generalizethem to predict effective antibodies against unknown mutants. The dataset isavailable at https://avida-hil6.cognanous.com.

AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions