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Genes 2018, 9(2), 91; https://doi.org/10.3390/genes9020091

PClass: Protein Quaternary Structure Classification by Using Bootstrapping Strategy as Model Selection

1
School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
2
Division of Colon & Rectal Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
3
School of Medical Informatics, Chung-Shan Medical University, Taichung 40201, Taiwan
4
IT Office, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
5
Institute of Genomics and Bioinformatics, National Chung Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan
6
Department of Computer Science and Engineering, National Chung-Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan
7
Biotechnology Center, Agricultural Biotechnology Center, Institute of Molecular Biology, Graduate Institute of Biotechnology, National Chung Hsing University, Kuo Kuang Rd., Taichung 402, Taiwan
*
Author to whom correspondence should be addressed.
Received: 21 December 2017 / Revised: 24 January 2018 / Accepted: 8 February 2018 / Published: 14 February 2018
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Abstract

Protein quaternary structure complex is also known as a multimer, which plays an important role in a cell. The dimer structure of transcription factors is involved in gene regulation, but the trimer structure of virus-infection-associated glycoproteins is related to the human immunodeficiency virus. The classification of the protein quaternary structure complex for the post-genome era of proteomics research will be of great help. Classification systems among protein quaternary structures have not been widely developed. Therefore, we designed the architecture of a two-layer machine learning technique in this study, and developed the classification system PClass. The protein quaternary structure of the complex is divided into five categories, namely, monomer, dimer, trimer, tetramer, and other subunit classes. In the framework of the bootstrap method with a support vector machine, we propose a new model selection method. Each type of complex is classified based on sequences, entropy, and accessible surface area, thereby generating a plurality of feature modules. Subsequently, the optimal model of effectiveness is selected as each kind of complex feature module. In this stage, the optimal performance can reach as high as 70% of Matthews correlation coefficient (MCC). The second layer of construction combines the first-layer module to integrate mechanisms and the use of six machine learning methods to improve the prediction performance. This system can be improved over 10% in MCC. Finally, we analyzed the performance of our classification system using transcription factors in dimer structure and virus-infection-associated glycoprotein in trimer structure. PClass is available via a web interface at http://predictor.nchu.edu.tw/PClass/. View Full-Text
Keywords: protein quaternary structure; bootstrap strategy; model selection; classification protein quaternary structure; bootstrap strategy; model selection; classification
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Huang, C.-C.; Chang, C.-C.; Chen, C.-W.; Ho, S.-Y.; Chang, H.-P.; Chu, Y.-W. PClass: Protein Quaternary Structure Classification by Using Bootstrapping Strategy as Model Selection. Genes 2018, 9, 91.

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