A synthetic protein-level neural network in mammalian cells

• 成果类型: Article
• 署名作者: Chen, Zibo; Linton, James M.; Xia, Shiyu; Fan, Xinwen; Yu, Dingchen; Wang, Jinglin; Zhu, Ronghui; Elowitz, Michael B.
• 署名单位: Westlake Laboratory; Westlake University; California Institute of Technology; California Institute of Technology; Howard Hughes Medical Institute
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-10552
• DOI: 10.1126/science.add8468
• 发表日期: 2024-12-01
• 页码: 1243-1250
• 关键词: identification; circuits; DESIGN

摘要

Artificial neural networks provide a powerful paradigm for nonbiological information processing. To understand whether similar principles could enable computation within living cells, we combined de novo-designed protein heterodimers and engineered viral proteases to implement a synthetic protein circuit that performs winner-take-all neural network classification. This perceptein circuit combines weighted input summation through reversible binding interactions with self-activation and mutual inhibition through irreversible proteolytic cleavage. These interactions collectively generate a large repertoire of distinct protein species stemming from up to eight coexpressed starting protein species. The complete system achieves multi-output signal classification with tunable decision boundaries in mammalian cells and can be used to conditionally control cell death. These results demonstrate how engineered protein-based networks can enable programmable signal classification in living cells.