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Front. Genet. | doi: 10.3389/fgene.2019.01231

Predicting endoplasmic reticulum resident proteins using auto-cross covariance transformation with U-shaped residue weight transfer function

Yang-Yang Miao1, Wei Zhao1, Guang-Ping Li1,  Yang Gao2* and  Pu-Feng Du1*
  • 1Tianjin University, China
  • 2Nankai University, China

The endoplasmic reticulum (ER) is an important organelle of 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 ER are called ER-resident proteins. These proteins are closely related to biological functions of 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 introduced a U-shaped weight transfer function, along with the positional-specific physiochemical properties (PSPCP), to integrate sequence order information, signaling peptides information, and evolutionary information together. 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 to identify ER-resident proteins.

Keywords: Pseudo amino acid composition, Support vector machine, Endoplasmic reticulum resident protein, Leave-One-Out Cross-Validation, U-shaped residue weight transfer function

Received: 09 Oct 2019; Accepted: 06 Nov 2019.

Copyright: © 2019 Miao, Zhao, Li, Gao and Du. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Prof. Yang Gao, Nankai University, Tianjin, 300071, China, gaoy@nankai.edu.cn
Prof. Pu-Feng Du, Tianjin University, Tianjin, 300072, Tianjin, China, pdu@tju.edu.cn