The Characterization of Structure and Prediction for Aquaporin in Tumour Progression by Machine Learning

Zheng Chen; Shihu Jiao; Da Zhao; Quan Zou; Lei Xu; Lijun Zhang; Xi Su

doi:10.3389/fcell.2022.845622

The Characterization of Structure and Prediction for Aquaporin in Tumour Progression by Machine Learning

Front Cell Dev Biol. 2022 Feb 1:10:845622. doi: 10.3389/fcell.2022.845622. eCollection 2022.

Authors

Zheng Chen^{1

2}, Shihu Jiao³, Da Zhao^{1

2}, Quan Zou^{2

3}, Lei Xu⁴, Lijun Zhang¹, Xi Su⁵

Affiliations

¹ School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China.
² Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
³ Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
⁴ School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China.
⁵ Foshan Maternal and Child Health Hospital, Foshan, China.

Abstract

Recurrence and new cases of cancer constitute a challenging human health problem. Aquaporins (AQPs) can be expressed in many types of tumours, including the brain, breast, pancreas, colon, skin, ovaries, and lungs, and the histological grade of cancer is positively correlated with AQP expression. Therefore, the identification of aquaporins is an area to explore. Computational tools play an important role in aquaporin identification. In this research, we propose reliable, accurate and automated sequence predictor iAQPs-RF to identify AQPs. In this study, the feature extraction method was 188D (global protein sequence descriptor, GPSD). Six common classifiers, including random forest (RF), NaiveBayes (NB), support vector machine (SVM), XGBoost, logistic regression (LR) and decision tree (DT), were used for AQP classification. The classification results show that the random forest (RF) algorithm is the most suitable machine learning algorithm, and the accuracy was 97.689%. Analysis of Variance (ANOVA) was used to analyse these characteristics. Feature rank based on the ANOVA method and IFS strategy was applied to search for the optimal features. The classification results suggest that the 26th feature (neutral/hydrophobic) and 21st feature (hydrophobic) are the two most powerful and informative features that distinguish AQPs from non-AQPs. Previous studies reported that plasma membrane proteins have hydrophobic characteristics. Aquaporin subcellular localization prediction showed that all aquaporins were plasma membrane proteins with highly conserved transmembrane structures. In addition, the 3D structure of aquaporins was consistent with the localization results. Therefore, these studies confirmed that aquaporins possess hydrophobic properties. Although aquaporins are highly conserved transmembrane structures, the phylogenetic tree shows the diversity of aquaporins during evolution. The PCA showed that positive and negative samples were well separated by 54D features, indicating that the 54D feature can effectively classify aquaporins. The online prediction server is accessible at http://lab.malab.cn/∼acy/iAQP.

Keywords: 3D structure; anova; cancer; machine learning; random forest.