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Structural class is an important attribute of protein, which plays an important role in both the experiment and theoretical research of protein science. Prediction of protein structural classes has been a challenging task, specifically for low-similarity datasets. In this paper, we develop a feature extraction method PSSS-SOMA-PSSM by incorporating two different modes of evolutionary information into Chou's general pseudo amino acid composition (PseAAC) based on predicted secondary structure sequence (PSSS) and position-specific scoring matrix (PSSM). We construct a 170-dimensional (170D) feature vector for each protein sequence sample, which contains 10D PSSS features that reflect content, alternating word frequency and novel position information, and contains 160D PSSM features that are calculated by second-order moving average (SOMA) algorithm. The SVM classifier with RBF kernel function and the jackknife test are used to predict and evaluate on 1189, 25PDB and 640 datasets with sequence similarity lower than 40%, 25%, and 25%, respectively. Comparison of our results with other methods shows that the proposed method provides the state-of-the-art performance and a cost-effective alternative to structural classes prediction for low-similarity datasets. [ABSTRACT FROM AUTHOR]

RNA 5-hydroxymethylcytosine (5hmC) modification is the basis of the translation of genetic information and the biological evolution. The study of its distribution in transcriptome is fundamentally crucial to reveal the biological significance of 5hmC. Biochemical experiments can use a variety of sequencing-based technologies to achieve high-throughput identification of 5hmC; however, they are labor-intensive, time-consuming, as well as expensive. Therefore, it is urgent to develop more effective and feasible computational methods. In this paper, a novel and powerful model called iR5hmcSC is designed for identifying 5hmC. Firstly, we extract the different features by K-mer, Pseudo Structure Status Composition and One-Hot encoding. Subsequently, the combination of chi-square test and logistic regression is utilized as the feature selection method to select the optimal feature sets. And then stacking learning, an ensemble learning method including random forest (RF), extra trees (EX), AdaBoost (Ada), gradient boosting decision tree (GBDT), and support vector machine (SVM), is used to recognize 5hmC and non-5hmC. Finally, 10-fold cross-validation test is performed to evaluate the model. The accuracy reaches 85.27% and 79.92% on benchmark dataset and independent dataset, respectively. The result is better than the state-of-the-art methods, which indicates that our model is a feasible tool to identify 5hmC. The datasets and source code are freely available at https://github.com/HongyanShi026/iR5hmcSC. [Display omitted] • A new model named iR5hmcSC was proposed to predict RNA 5hmC sites. • K-mer, Pseudo Structure Status Composition and One-Hot encoding are applied to extract features from the dataset. • A new method combining chi-square test and logistic regression is used to reduce the dimensions of data. • The stacking learning is adopted to classify the model. [ABSTRACT FROM AUTHOR]