Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering

Publication Date: 12/10/2023

Event: NeurIPS 2023

Reference: pp. 1-29, 2023

Authors: Tianxiao Li, NEC Laboratories America, Inc., Yale University; Hongyu Guo, National Research Council Canada; Filippo Grazioli, NEC Laboratories Europe; Mark Gerstein, Yale University; Martin Renqiang Min, NEC Laboratories America, Inc.

Abstract: 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 nontrivial, 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 actionsinvolved. 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 alterthe 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.

Publication Link: https://proceedings.neurips.cc/paper_files/paper/2023/file/e95da8078ec8389533c802e368da5298-Paper-Conference.pdf