Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods

Abstract

DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF), is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.

Special Protein Molecules Computational Identification

“…DNA binding protein is a kind of special protein molecule, whose identification is one of the most important tasks in studying the function of proteins. In this regard, many computational predictors have been proposed [ 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. In a special issue, Zhang et al [ 22 ] proposed a new approach to extract evolutionary information from the Position Specific Frequency Matrix (PSFM) and incorporate the evolutionary information, and a computational predictor was proposed for DNA binding protein identification.…”

HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection

“…These methods primarily focus on the following two aspects: (1) the construction of encoding schemes for protein sequences and (2) the application of classification algorithms. Many machine learning techniques have been adopted to perform the prediction of DBPs, including support vector machine (SVM) [5][6][7], random forest (RF) [8][9][10], naive Bayes classifier [4], ensemble classifiers [11][12][13], and deep learning [14][15][16]. Among these algorithms, SVM and RF have been widely used because of their excellent performance.…”

PredDBP-Stack: Prediction of DNA-Binding Proteins from HMM Profiles using a Stacked Ensemble Method

“…Prediction of DBPs is usually formulated as a supervised learning problem. In recent years, many classification algorithms have been adopted to solve this problem, including support vector machine (SVM) [24][25][26], random forest (RF) [27,28], naive Bayes classifier [3], ensemble classifiers [29][30][31], and deep learning [32][33][34]. Among these models, stacked generalization (or stacking) is an ensemble learning technique that takes the outputs of base classifiers as input and attempts to find the optimal combination of the base learners to make a better prediction [35].…”