Miao Yang-Yang, Zhao Wei, Li Guang-Ping, Gao Yang, Du Pu-Feng
College of Intelligence and Computing, Tianjin University, Tianjin, China.
School of Chemical Engineering, Tianjin University, Tianjin, China.
Front Genet. 2019 Dec 20;10:1231. doi: 10.3389/fgene.2019.01231. eCollection 2019.
The endoplasmic reticulum (ER) is an important organelle in eukaryotic cells. It is involved in many important biological processes, such as cell metabolism, protein synthesis, and post-translational modification. The proteins that reside within the ER are called ER-resident proteins. These proteins are closely related to the biological functions of the ER. The difference between the ER-resident proteins and other non-resident proteins should be carefully studied. We developed a support vector machine (SVM)-based method. We developed a U-shaped weight-transfer function and used it, along with the positional-specific physiochemical properties (PSPCP), to integrate together sequence order information, signaling peptides information, and evolutionary information. Our method achieved over 86% accuracy in a jackknife test. We also achieved roughly 86% sensitivity and 67% specificity in an independent dataset test. Our method is capable of identifying ER-resident proteins.
内质网（ER）是真核细胞中的一种重要细胞器。它参与许多重要的生物学过程，如细胞代谢、蛋白质合成和翻译后修饰。驻留在内质网中的蛋白质称为内质网驻留蛋白。这些蛋白质与内质网的生物学功能密切相关。内质网驻留蛋白与其他非驻留蛋白之间的差异应仔细研究。我们开发了一种基于支持向量机（SVM）的方法。我们开发了一种U形权重转移函数，并将其与位置特异性理化性质（PSPCP）一起用于整合序列顺序信息、信号肽信息和进化信息。我们的方法在留一法测试中准确率超过86%。在独立数据集测试中，我们也获得了约86%的灵敏度和67%的特异性。我们的方法能够识别内质网驻留蛋白。
