On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach

Abstract

DNA-binding proteins play pivotal roles in alternative splicing, RNA editing, methylating and many other biological functions for both eukaryotic and prokaryotic proteomes. Predicting the functions of these proteins from primary amino acids sequences is becoming one of the major challenges in functional annotations of genomes. Traditional prediction methods often devote themselves to extracting physiochemical features from sequences but ignoring motif information and location information between motifs. Meanwhile, the small scale of data volumes and large noises in training data result in lower accuracy and reliability of predictions. In this paper, we propose a deep learning based method to identify DNA-binding proteins from primary sequences alone. It utilizes two stages of convolutional neutral network to detect the function domains of protein sequences, and the long short-term memory neural network to identify their long term dependencies, an binary cross entropy to evaluate the quality of the neural networks. When the proposed method is tested with a realistic DNA binding protein dataset, it achieves a prediction accuracy of 94.2% at the Matthew’s correlation coefficient of 0.961. Compared with the LibSVM on the arabidopsis and yeast datasets via independent tests, the accuracy raises by 9% and 4% respectively. Comparative experiments using different feature extraction methods show that our model performs similar accuracy with the best of others, but its values of sensitivity, specificity and AUC increase by 27.83%, 1.31% and 16.21% respectively. Those results suggest that our method is a promising tool for identifying DNA-binding proteins.

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].…”

Deep Neural Network Based Predictions of Protein Interactions Using Primary Sequences

“…Pan et al adopted a CNN and deep belief network (DBN) to discover RNA–protein binding motifs using different groups of features, that is, sequence, structure, clip-cobinding, region type, and motif features [ 33 ]. In our previous work, we developed a deep learning model for predicting DNA binding proteins only from sequences, in which the features were automatically learned by the networks themselves [ 34 ]. In this study, we further investigated the capability of auto feature engineering in the deep learning framework for predicting PPIs.…”