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.

用计算方法准确识别信号肽，尤其对于元基因组数据分析，提出了一种基于蛋白质语言模型的信号肽分类和切割位点预测的深度学习方法，并应用标签分布感知边界损失处理数据不平衡问题，并利用蛋白质的进化信息丰富表示来克服物种信息依赖。