  In protein biophysics, the separation between the functionally important
residues (forming the active site or binding surface) and those that create the
overall structure (the fold) is a well-established and fundamental concept.
Identifying and modifying those functional sites is critical for protein
engineering but computationally non-trivial, and requires significant domain
knowledge. To automate this process from a data-driven perspective, we propose
a disentangled Wasserstein autoencoder with an auxiliary classifier, which
isolates the function-related patterns from the rest with theoretical
guarantees. This enables one-pass protein sequence editing and improves the
understanding of the resulting sequences and editing actions involved. To
demonstrate its effectiveness, we apply it to T-cell receptors (TCRs), a
well-studied structure-function case. We show that our method can be used to
alter the function of TCRs without changing the structural backbone,
outperforming several competing methods in generation quality and efficiency,
and requiring only 10% of the running time needed by baseline models. To our
knowledge, this is the first approach that utilizes disentangled
representations for TCR engineering.
