Signal peptide (SP) is a short peptide located in the N-terminus of proteins. It is essential to target and transfer transmembrane and secreted proteins to correct positions. Compared with traditional experimental methods to identify signal peptides, computational methods are faster and more efficient, which are more practical for analyzing thousands or even millions of protein sequences, especially for metagenomic data. Here we present Unbiased Organism-agnostic Signal Peptide Network (USPNet), a signal peptide classification and cleavage site prediction deep learning method that takes advantage of protein language models. We propose to apply label distribution-aware margin loss to handle data imbalance problems and use evolutionary information of protein to enrich representation and overcome species information dependence.

翻译：信号肽（SP）是位于蛋白质N末端的一段短肽，对于引导和转运跨膜蛋白及分泌蛋白到达正确位置至关重要。相较于传统实验方法识别信号肽，计算方法更为快速高效，尤其适用于分析数千甚至数百万条蛋白质序列（如宏基因组数据）。本文提出无偏性物种无关信号肽网络（USPNet），这是一种利用蛋白质语言模型的信号肽分类与切割位点预测深度学习方法。我们采用标签分布感知的边际损失函数处理数据不平衡问题，并利用蛋白质的进化信息增强表征能力，从而克服物种信息依赖性。