Prediction of protein structural class for low-similarity sequences using Chou’s pseudo amino acid composition and wavelet denoising

Yu, Bin; Lou, Lifeng; Li, Shan; Zhang, Yusen; Qiu, Wenying; Wu, Xue; Wang, Minghui; Tian, Baoguang

doi:10.1016/j.jmgm.2017.07.012

Cited by 72 publications

(24 citation statements)

References 69 publications

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“…Such as physicochemical based information (Dehzangi et al, 2013a;Sharma et al, 2013), structural based information (Yang et al, 2009;Zhang et al, 2013;Liu and Jia, 2010;Zhang et al, 2011;Ding et al, 2012;Han et al, 2014;Dehzangi et al, 2013b;Wang et al, 2014). Yu et al (2017) use Chous pseudo amino acid composition and wavelet denoising to prediction structural class. From 2014 to now, several papers (Dehzangi et al, 2014;Wang et al, 2014;Jones, 1999;Faraggi et al, 2012) show that the protein secondary structure is significanc to predict protein structural classes.…”

Section: Introductionmentioning

confidence: 99%

Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network

Zhao¹,

Luo²,

Liu³

2018

American Journal of Biochemistry and Biotechnology

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In this study, based on the predicted secondary structures of proteins, we propose a new approach to predict protein structural classes (α,β,α/β,α+β) for three widely used low-homology data sets. Fist, we obtain two time siries from the chaos game representation of each predicted secondary structure; second, based on two time series, we construct visibility and horizontal visibility network, respectively and generate a set of features using 17 network features; finaly, we predict each protein structure class using support vector machine and Fisher's linear discriminant algorithm, respectively. In order to evaluate our method, the leave one out cross-validating test is employed on three data sets. Results show that our approach has been provided as a effective tool for the prediction of low-homology protein structural classes.

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Section: Introductionmentioning

confidence: 99%

Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network

Zhao¹,

Luo²,

Liu³

2018

American Journal of Biochemistry and Biotechnology

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“…PseAAC was first developed to prevent the loss of hidden information in protein sequences and to provide a better expression of the initial information about AACs [17, 18, 19]. PseAAC reflects a protein expressed by generating 20

+

λ

discrete numbers, and includes both the main features of the AAC and information beyond the AAC.…”

Section: Methodsmentioning

confidence: 99%

Predicting subcellular localization of multisite proteins using differently weighted multi-label k-nearest neighbors sets

Jiang

Wang

et al. 2019

THC

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BACKGROUND: For a protein to execute its function, ensuring its correct subcellular localization is essential. In addition to biological experiments, bioinformatics is widely used to predict and determine the subcellular localization of proteins. However, single-feature extraction methods cannot effectively handle the huge amount of data and multisite localization of proteins. Thus, we developed a pseudo amino acid composition (PseAAC) method and an entropy density technique to extract feature fusion information from subcellular multisite proteins. OBJECTIVE: Predicting multiplex protein subcellular localization and achieve high prediction accuracy. METHOD: To improve the efficiency of predicting multiplex protein subcellular localization, we used the multi-label k-nearest neighbors algorithm and assigned different weights to various attributes. The method was evaluated using several performance metrics with a dataset consisting of protein sequences with single-site and multisite subcellular localizations. RESULTS: Evaluation experiments showed that the proposed method significantly improves the optimal overall accuracy rate of multiplex protein subcellular localization. CONCLUSION: This method can help to more comprehensively predict protein subcellular localization toward better understanding protein function, thereby bridging the gap between theory and application toward improved identification and monitoring of drug targets.

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“…For the bioinformatics data with high dimensionality, small sample and non-linearity, SVM has excellent learning performance under the principle of structural risk minimization. It is widely used to predict membrane protein types [ 83 ], G protein-coupled receptors [ 84 ], protein structural classes [ 55 , 85 , 86 ], protein-protein interaction [ 87 ], protein subcellular localization [ 42 , 88 ], protein post-translational modification sites [ 89 , 90 ] and other protein function prediction research.…”

Section: Methodsmentioning

confidence: 99%

Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising

Qiu

et al. 2017

Oncotarget

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Apoptosis proteins subcellular localization information are very important for understanding the mechanism of programmed cell death and the development of drugs. The prediction of subcellular localization of an apoptosis protein is still a challenging task because the prediction of apoptosis proteins subcellular localization can help to understand their function and the role of metabolic processes. In this paper, we propose a novel method for protein subcellular localization prediction. Firstly, the features of the protein sequence are extracted by combining Chou's pseudo amino acid composition (PseAAC) and pseudo-position specific scoring matrix (PsePSSM), then the feature information of the extracted is denoised by two-dimensional (2-D) wavelet denoising. Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of apoptosis proteins. Quite promising predictions are obtained using the jackknife test on three widely used datasets and compared with other state-of-the-art methods. The results indicate that the method proposed in this paper can remarkably improve the prediction accuracy of apoptosis protein subcellular localization, which will be a supplementary tool for future proteomics research.

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Prediction of protein structural class for low-similarity sequences using Chou’s pseudo amino acid composition and wavelet denoising

Cited by 72 publications

References 69 publications

Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network

Low-Homology Protein Structural Class Prediction from Secondary Structure Based on Visibility and Horizontal Visibility Network

Predicting subcellular localization of multisite proteins using differently weighted multi-label k-nearest neighbors sets

Accurate prediction of subcellular location of apoptosis proteins combining Chou’s PseAAC and PsePSSM based on wavelet denoising

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