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Open AccessArticle

Computational Identification and Analysis of Ubiquinone-Binding Proteins

by Chang Lu 1,2, Wenjie Jiang 1,2, Hang Wang 1,2, Jinxiu Jiang 1,2, Zhiqiang Ma 1,2,3,* and Han Wang 1,2,3,*
1
School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
2
Institute of Computational Biology, Northeast Normal University, Changchun 130117, China
3
Department of Computer Science, College of Humanities & Sciences of Northeast Normal University, Changchun 130117, China
*
Authors to whom correspondence should be addressed.
Cells 2020, 9(2), 520; https://doi.org/10.3390/cells9020520
Received: 21 January 2020 / Revised: 21 February 2020 / Accepted: 21 February 2020 / Published: 24 February 2020
(This article belongs to the Special Issue Biocomputing and Synthetic Biology in Cells)
Ubiquinone is an important cofactor that plays vital and diverse roles in many biological processes. Ubiquinone-binding proteins (UBPs) are receptor proteins that dock with ubiquinones. Analyzing and identifying UBPs via a computational approach will provide insights into the pathways associated with ubiquinones. In this work, we were the first to propose a UBPs predictor (UBPs-Pred). The optimal feature subset selected from three categories of sequence-derived features was fed into the extreme gradient boosting (XGBoost) classifier, and the parameters of XGBoost were tuned by multi-objective particle swarm optimization (MOPSO). The experimental results over the independent validation demonstrated considerable prediction performance with a Matthews correlation coefficient (MCC) of 0.517. After that, we analyzed the UBPs using bioinformatics methods, including the statistics of the binding domain motifs and protein distribution, as well as an enrichment analysis of the gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. View Full-Text
Keywords: ubiquinone-binding proteins; XGBoost; binding domain motifs; gene ontology; KEGG pathway ubiquinone-binding proteins; XGBoost; binding domain motifs; gene ontology; KEGG pathway
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MDPI and ACS Style

Lu, C.; Jiang, W.; Wang, H.; Jiang, J.; Ma, Z.; Wang, H. Computational Identification and Analysis of Ubiquinone-Binding Proteins. Cells 2020, 9, 520.

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