Princeton team launches MESSI, a diffusion model for deimmunizing protein drugs
Kaiyi Jiang, an assistant professor in Princeton University's bioengineering department, and collaborators posted a preprint, "Protein Deimmunization with a Generative Diffusion Model," introducing MESSI, a generative protein design model. Building on inverse-folding approaches such as ProteinMPNN, MESSI adds an MHC-II presentation-related immunogenicity risk predictor and a "Lookahead–Feedback" mechanism that samples candidate sequences mid-diffusion, then re-masks and re-predicts high-risk positions. The team tested designs on three proteins — luciferase, a PD-1 binding protein and urate oxidase — reporting that some candidates had predicted epitope counts reduced to zero while retaining activity, with the best urate oxidase variant about 2.7 times as active as the original protein. The authors note that whether these designs actually trigger weaker immune responses in humans remains unproven.
It tries to turn immunogenicity from a post-design filter into a constraint during generation, offering a new design route for therapeutic proteins that need repeated dosing.
AI 已经能够设计出自然界中未曾出现过的蛋白质，但这并不意味着人体愿意接受它们。
一条氨基酸序列，即使能够折叠成预期结构、完成指定功能，也可能在进入人体后引发免疫反应。对于需要反复给药的治疗，这尤其棘手。人体产生的抗药抗体可能加速药物清除，或中和其活性，使原本有效的治疗难以持续。在制药公司工作的一年里，姜凯议注意到了这一矛盾。
“在有些科研工作中，可能是先开发出一项技术，再思考它能解决什么问题。但在药企，情况恰恰相反。免疫原性的问题早已存在，而且迫切需要解决。”姜凯议在接受 DeepTech 采访时说。
过去，研究人员可以改造天然蛋白质，也可以通过各种方式降低外源蛋白带来的免疫风险。如今，生成式模型能够在更大的序列空间中探索新设计。但蛋白质的结构与功能越容易被设计，免疫原性这个缺口就越值得关注。能否让 AI 在生成序列时，就考虑人体免疫系统可能作出的反应？
#Princeton University#MESSI#Kaiyi Jiang#ProteinMPNN#MHC-II#EVOLVEpro#RADARS#PD-1#urate oxidase#luciferase