Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou's general PseAAC. (7th February 2019)

• Record Type: Journal Article
• Authors: Shen, Yinan; Tang, Jijun; Guo, Fei
• Is Part Of: Journal of theoretical biology. Volume 462(2019)
• Page Start: 230
• Page End: 239
• Publication Date: 2019-02-07
• Subjects: Protein subcellular localization -- Physicochemical -- PSSM -- Multi-kernel SVM; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2018.11.012
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: Multi-label protein subcellular localization prediction are focused on this paper. PP matrix uses the physicochemical properties of proteins PSSM matrix provides information about the evolution of proteins. Multi-kernel SVM integrates multiple features to improve prediction accuracy. Abstract: Identifying the location of proteins in a cell plays an important role in understanding their functions, such as drug design, therapeutic target discovery and biological research. However, the traditional subcellular localization experiments are time-consuming, laborious and small scale. With the development of next-generation sequencing technology, the number of proteins has grown exponentially, which lays the foundation of the computational method for identifying protein subcellular localization. Although many methods for predicting subcellular localization of proteins have been proposed, most of them are limited to single-location. In this paper, we propose a multi-kernel SVM to predict subcellular localization of both multi-location and single-location proteins. First, we make use of the evolutionary information extracted from position specific scoring matrix (PSSM) and physicochemical properties of proteins, by Chou's general PseAAC and other efficient functions. Then, we propose a multi-kernel support vector machine (SVM) model to identify multi-label protein subcellular localization. As a result, our method has a good performance on predicting subcellular localization of proteins. It achieves an average precision of 0.7065 and 0.6889 on two human datasets, respectively. All results are higher than those achieved by other existing methods. Therefore, we provide an efficient system via a novel perspective to study the protein subcellular localization.