据介绍，人工神经网络为非生物信息处理提供了一个强大的范式。
为了了解类似的原理是否可以在活细胞内进行计算，研究人员将从头设计的蛋白质异二聚体和工程病毒蛋白酶结合起来，实现了一个执行赢家通吃神经网络分类的合成蛋白质回路。这种“感知器”回路将通过可逆结合相互作用的加权输入求和，与通过不可逆蛋白水解切割的自激活和相互抑制相结合。
这些相互作用共同产生了大量不同的蛋白质物种，这些蛋白质物种来自多达八种共表达的起始蛋白质物种。该完整系统在哺乳动物细胞中，实现了具有可调决策边界的多输出信号分类，可用于有条件地控制细胞死亡。
总之，这一研究结果证明了基于蛋白质的工程网络，如何在活细胞中实现可编程的信号分类。
附：英文原文
Title: A synthetic protein-level neural network in mammalian cells
Author: Zibo Chen, James M. Linton, Shiyu Xia, Xinwen Fan, Dingchen Yu, Jinglin Wang, Ronghui Zhu, Michael B. Elowitz
Issue&Volume: 2024-12-13
Abstract: 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.
DOI: add8468
Source: https://www.science.org/doi/10.1126/science.add8468