  Antibodies comprise the most versatile class of binding molecules, with
numerous applications in biomedicine. Computational design of antibodies
involves generating novel and diverse sequences, while maintaining structural
consistency. Unique to antibodies, designing the complementarity-determining
region (CDR), which determines the antigen binding affinity and specificity,
creates its own unique challenges. Recent deep learning models have shown
impressive results, however the limited number of known antibody
sequence/structure pairs frequently leads to degraded performance, particularly
lacking diversity in the generated sequences. In our work we address this
challenge by leveraging Model Reprogramming (MR), which repurposes pretrained
models on a source language to adapt to the tasks that are in a different
language and have scarce data - where it may be difficult to train a
high-performing model from scratch or effectively fine-tune an existing
pre-trained model on the specific task. Specifically, we introduce ReprogBert
in which a pretrained English language model is repurposed for protein sequence
infilling - thus considers cross-language adaptation using less data. Results
on antibody design benchmarks show that our model on low-resourced antibody
sequence dataset provides highly diverse CDR sequences, up to more than a
two-fold increase of diversity over the baselines, without losing structural
integrity and naturalness. The generated sequences also demonstrate enhanced
antigen binding specificity and virus neutralization ability. Code is available
at https://github.com/IBM/ReprogBERT
