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