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.

中文翻译：

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哺乳动物细胞中的合成蛋白质水平神经网络


人工神经网络为非生物信息处理提供了强大的范式。为了了解类似的原理是否可以在活细胞内进行计算，我们将从头设计的蛋白质异二聚体和工程化病毒蛋白酶相结合，以实现执行赢家通吃神经网络分类的合成蛋白质电路。这个“感知素”回路通过可逆结合相互作用将加权输入求和与通过不可逆蛋白水解切割的自我激活和相互抑制相结合。这些相互作用共同产生大量不同的蛋白质种类，这些蛋白质种类源于多达 8 种共表达的起始蛋白种类。整个系统在哺乳动物细胞中实现了多输出信号分类，具有可调谐的决策边界，可用于有条件地控制细胞死亡。这些结果表明，基于工程化蛋白质的网络如何在活细胞中实现可编程信号分类。