Predicting unconventional protein secretion is a much harder problem than predicting signal peptide-based protein secretion, both due to the small number of examples and due to the heterogeneity and the limited knowledge of the pathways involved, especially in eukaryotes. However, the idea that secreted proteins share certain properties regardless of the secretion pathway used made it possible to construct the prediction method SecretomeP in 2004. Here, we take a critical look at SecretomeP and its successors, and we also discuss whether multi-category subcellular location predictors can be used to predict unconventional protein secretion in eukaryotes. A new benchmark shows SecretomeP to perform much worse than initially estimated, casting doubt on the underlying hypothesis. On a more positive note, recent developments in machine learning may have the potential to construct new methods which can not only predict unconventional protein secretion but also point out which parts of a sequence are important for secretion.

与预测基于信号肽的蛋白质分泌相比，预测非常规蛋白质分泌要困难得多，这不仅是因为实例数量少，而且由于涉及方法的异质性和相关途径的知识有限，尤其是在真核生物中。但是，无论使用哪种分泌途径，分泌蛋白都具有某些特性的想法使得在2004年构建预测方法SecretomeP成为可能。在这里，我们对SecretomeP及其继承者进行了批判性研究，并讨论了多类别亚细胞定位预测子可用于预测真核生物中非常规蛋白质的分泌。一个新的基准显示SecretomeP的性能比最初估计的要差得多，这使人们对基本假设产生了怀疑。更积极的一点是，