Protein Deimmunization with a Generative Diffusion Model
- 1Princeton University
Abstract
Protein immunogenicity is a major cause of biologic drug failure, leading to the production of anti-drug antibodies (ADA) that accelerate drug clearance, rejection of repeated doses, abolished efficacy, and sometimes life threatening anaphylaxis in patients. These responses constrain the clinical use of synthetic protein therapeutics from non-human origin. To efficiently deimmunize protein therapeutics, we developed a generative discrete diffusion model (MESSI), which takes protein structures as input and outputs sequences that preserve structure without containing peptides predicted to bind major histocompatibility complex class II molecules. MESSI designs sequences with high structural recovery and down to 0 predicted epitopes for specified MHC molecules at higher efficiency than post-hoc filtering approaches. Experimentally, we first applied MESSI to deimmunize a luciferase and a de novo designed mini-binder against PD-1, resulting in variants with no predicted epitopes and higher activity than their parents. Lastly, we coupled MESSI with evolution guidance to deimmunize uricase, a clinically approved enzyme with severe immunogenicity, and found deimmunized variants with higher catalytic activity than their natural orthologs. MESSI enables generative design of functional proteins with minimized immunogenicity, potentially making protein therapeutics safer and more effective.
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BibTeX
@misc{chen2026protein,
title = {{Protein Deimmunization with a Generative Diffusion Model}},
author = {Z. Chen and C.-c. Yu and C. Zhou and X. Jiang and L. Nguyen and C. Myhrvold and T. S. Jaakkola and K. Jiang},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.09.758136},
url = {https://www.biorxiv.org/content/10.64898/2026.10.09.758136v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Protein Deimmunization with a Generative Diffusion Model
AU - Z. Chen
AU - C.-c. Yu
AU - C. Zhou
AU - X. Jiang
AU - L. Nguyen
AU - C. Myhrvold
AU - T. S. Jaakkola
AU - K. Jiang
PY - 2026
DA - 2026/10/09
PB - bioRxiv
DO - 10.64898/2026.10.09.758136
UR - https://www.biorxiv.org/content/10.64898/2026.10.09.758136v1
AB - Protein immunogenicity is a major cause of biologic drug failure, leading to the production of anti-drug antibodies (ADA) that accelerate drug clearance, rejection of repeated doses, abolished efficacy, and sometimes life threatening anaphylaxis in patients. These responses constrain the clinical use of synthetic protein therapeutics from non-human origin. To efficiently deimmunize protein therapeutics, we developed a generative discrete diffusion model (MESSI), which takes protein structures as input and outputs sequences that preserve structure without containing peptides predicted to bind major histocompatibility complex class II molecules. MESSI designs sequences with high structural recovery and down to 0 predicted epitopes for specified MHC molecules at higher efficiency than post-hoc filtering approaches. Experimentally, we first applied MESSI to deimmunize a luciferase and a de novo designed mini-binder against PD-1, resulting in variants with no predicted epitopes and higher activity than their parents. Lastly, we coupled MESSI with evolution guidance to deimmunize uricase, a clinically approved enzyme with severe immunogenicity, and found deimmunized variants with higher catalytic activity than their natural orthologs. MESSI enables generative design of functional proteins with minimized immunogenicity, potentially making protein therapeutics safer and more effective.
ER -