Abstract
RNA binding protein (RBP) plays an important role in cellular processes. Identifying RBPs by computation and experiment are both essential. Recently, an RBP predictor, RBPPred, is proposed in our group to predict RBPs. However, RBPPred is too slow for that it needs to generate PSSM matrix as its feature. Herein, based on the protein feature of RBPPred and Convolutional Neural Network (CNN), we develop a deep learning model called Deep-RBPPred. With the balance and imbalance training set, we obtain Deep-RBPPred-balance and Deep-RBPPred-imbalance models. Deep-RBPPred has three advantages comparing to previous methods. (1) Deep-RBPPred only needs few physicochemical properties based on protein sequences. (2) Deep-RBPPred runs much faster. (3) Deep-RBPPred has a good generalization ability. In the meantime, Deep-RBPPred is still as good as the state-of-the-art method. Testing in A. thaliana, S. cerevisiae and H. sapiens proteomes, MCC values are 0.82 (0.82), 0.65 (0.69) and 0.85 (0.80) for balance model (imbalance model) when the score cutoff is set to 0.5, respectively. In the same testing dataset, different machine learning algorithms (CNN and SVM) are also compared. The results show that CNN-based model can identify more RBPs than SVM-based. In comparing the balance and imbalance model, both CNN-base and SVM-based tend to favor the majority class in the imbalance set. Deep-RBPPred forecasts 280 (balance model) and 265 (imbalance model) of 299 new RBP. The sensitivity of balance model is about 7% higher than the state-of-the-art method. We also apply deep-RBPPred to 30 eukaryotes and 109 bacteria proteomes downloaded from Uniprot to estimate all possible RBPs. The estimating result shows that rates of RBPs in eukaryote proteomes are much higher than bacteria proteomes.
Highlights
RNA binding proteins (RBPs) play important functions in many cellular processes, such as post-transcriptional gene regulation, RNA subcellular localization and alternative splicing
For the predicting results with the balance model and imbalance model, we found an interesting phenomenon that the rate of RBPs in eukaryotes proteome is higher than bacteria
We develop two RBPs predicting models based on Convolutional Neural Network (CNN) which only need hydrophobicity, normalized van der waals volume, polarity and polarizability, charge and polarity of side chain of protein sequence
Summary
We randomly select 2780 non-RBPs to generate the balance dataset together with 2780 RBPs. For testing our deep learning model, we used the testing set from RBPPred[22]. The testing and training set are mixed together to be clustered by CD-HIT with sequence identity cutoff 30%. The protein is encoded to a feature vector by the approach described in RBPPred[37]. Adam optimizer is employed to minimize final loss consisting of cross-entropy between the label and probability score and L2 regularization loss of neurons. In this architecture, the number of trainable variable is 480,930. The learning rate is set to 0.0001
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