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.
Highlights
For feature extraction by the 188-dimensional method (GPSD), we applied different ratios for the number of positive and negative samples (1:1, 1:2, 1:3, 1:4, 1:5, and 151: 8,994), and the results were classified by six machine learning classifiers (XGBoost, Naivebayes, logistic regression (LR), decision tree, random forest (RF) and support vector machine (SVM))
The accurate identification of aquaporins by iAQPs can greatly promote the prediction of aquaporins and research on tumour diseases
According to the F-score value obtained by Analysis of Variance (ANOVA), the 26th dimension feature and 21st dimension feature are ranked as the first and second dimension features among the 188 days features, respectively, and these two features possess neutral/hydrophobic characteristics
Summary
As one of the most widely existing molecules, is the basic requirement for the development of organisms. The sequencebased protein classification method extracts features by using the amino acid composition, amino acid number and other sequence information of the protein sequence (Liu et al, 2014). These methods are efficient and useful in predicting a large number of protein sequence datasets (Lou et al, 2014). There are various studies on the classification of protein sequences, such as using logistic regression and support vector machine (SVM) methods to predict DNA binding proteins (Shen and Zou, 2020; Liu et al, 2021a) by considering amino acid proportions, amino acid compositions, amino acid spatial asymmetric distributions and biological coding characteristics of evolutionary information (Szilágyi and Skolnick, 2006; Kumar et al, 2007). ANOVA is used to prune features without affecting the accuracy of the predictor
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