High-throughput identification of mammalian secreted proteins using species-specific scheme and application to human proteome

Zhang, J., Chai, H., Guo, S., Guo, H. and Li, Y. (2018) High-throughput identification of mammalian secreted proteins using species-specific scheme and application to human proteome. Molecules, 23(6), 1448. (doi: 10.3390/molecules23061448) (PMID:29903999) (PMCID:PMC6099666)

Abstract

Secreted proteins are widely spread in living organisms and cells. Since secreted proteins are easy to be detected in body fluids, urine, and saliva in clinical diagnosis, they play important roles in biomarkers for disease diagnosis and vaccine production. In this study, we propose a novel predictor for accurate high-throughput identification of mammalian secreted proteins that is based on sequence-derived features. We combine the features of amino acid composition, sequence motifs, and physicochemical properties to encode collected proteins. Detailed feature analyses prove the effectiveness of the considered features. Based on the differences across various species of secreted proteins, we introduce the species-specific scheme, which is expected to further explore the intrinsic attributes of specific secreted proteins. Experiments on benchmark datasets prove the effectiveness of our proposed method. The test on independent testing dataset also promises a good generalization capability. When compared with the traditional universal model, we experimentally demonstrate that the species-specific scheme is capable of significantly improving the prediction performance. We use our method to make predictions on unreviewed human proteome, and find 272 potential secreted proteins with probabilities that are higher than 99%. A user-friendly web server, named iMSPs (identification of Mammalian Secreted Proteins), which implements our proposed method, is designed and is available for free for academic use at: http://www.inforstation.com/webservers/iMSP/.

• Item Type: Articles
• Additional Information: This reseach was supported in part by the National Natural Science Foundation of China under the grants 61672261, 61572417, and 61502199, by Nanhu Scholars Program for Young Scholars of XYNU, and in part by the China Scholarship Council under Grant 201706620069.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Chai, Haiting
• Authors: Zhang, J., Chai, H., Guo, S., Guo, H., and Li, Y.
• College/School: College of Medical Veterinary and Life Sciences
• Journal Name: Molecules
• Publisher: MDPI
• ISSN: 1420-3049
• ISSN (Online): 1420-3049
• Published Online: 14 June 2018
• Copyright Holders: Copyright © 2018 The Authors
• First Published: First published in Molecules 23(6): 1448
• Publisher Policy: Reproduced under a Creative Commons License