A synthetic protein-level neural network in mammalian cells

作者：Zibo Chen, James M. Linton, Shiyu Xia, Xinwen Fan, Dingchen Yu, Jinglin Wang, Ronghui Zhu, Michael B. Elowitz · 发表于：Science · 年份：2024 · DOI：10.1126/science.add8468 · 被引用次数：39 · 研究领域：Gene Regulatory Network Analysis、Cell Image Analysis Techniques、Bioinformatics and Genomic Networks

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.