Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition. (7th August 2018)

• Record Type: Journal Article
• Authors: Qiu, Wenying; Li, Shan; Cui, Xiaowen; Yu, Zhaomin; Wang, Minghui; Du, Junwei; Peng, Yanjun; Yu, Bin
• Is Part Of: Journal of theoretical biology. Volume 450(2018)
• Page Start: 86
• Page End: 103
• Publication Date: 2018-08-07
• Subjects: Submitochondrial locations -- Pseudo-amino acid composition -- Pseudo-position specific scoring matrix -- Two-dimensional wavelet denoising -- Support vector machine -- Machine learning; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2018.04.026
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: A new method (PseAAC-PsePSSM-WD) to prediction protein submitochondrial locations. The protein sequence features are extracted by fusing the PseAAC and PsePSSM methods. 2-D wavelet denoising can effectively remove the redundant information in the protein sequences. We investigate the effect of the five different classifiers on the results. The proposed method increases the prediction performance over several methods. Abstract: Mitochondrion is important organelle of most eukaryotes and play an important role in participating in various life activities of cells. However, some functions of mitochondria can only be achieved in specific submitochondrial location, the study of submitochondrial locations will help to further understand the biological function of protein, which is a hotspot in proteomics research. In this paper, we propose a new method for protein submitochondrial locations prediction. Firstly, the features of protein sequence are extracted by combining Chou's pseudo-amino acid composition (PseAAC) and pseudo-position specific scoring matrix (PsePSSM). Then the extracted feature information is denoised by two-dimensional (2-D) wavelet denoising. Finally, the optimal feature vectors are input to the SVM classifier to predict the protein submitochondrial locations. We obtained the ideal prediction results by jackknife test and compared with other prediction methods. The results indicate that the proposed method is significantly better than the existing research results, which can provide a new method to predict protein locations in other organelles. The source code and all datasets are available athttps://github.com/QUST-BSBRC/PseAAC-PsePSSM-WD/ for academic use.