Abstract
Predicting the protein sequence information of enzymes and non-enzymes is an important but a very challenging task. Existing methods use protein geometric structures only or protein sequences alone to predict enzymatic functions. Thus, their prediction results are unsatisfactory. In this paper, we propose a novel approach for predicting the amino acid sequences of enzymes and non-enzymes via Convolutional Neural Network (CNN). In CNN, the roles of enzymes are predicted from multiple sides of biological information, including information on sequences and structures. We propose the use of two-dimensional data via 2DCNN to predict the proteins of enzymes and non-enzymes by using the same fivefold cross-validation function. We also use an independent dataset to test the performance of our model, and the results demonstrate that we are able to solve the overfitting problem. We used the CNN model proposed herein to demonstrate the superiority of our model for classifying an entire set of filters, such as 32, 64, and 128 parameters, with the fivefold validation test set as the independent classification. Via the Dipeptide Deviation from Expected Mean (DDE) matrix, mutation information is extracted from amino acid sequences and structural information with the distance and angle of amino acids is conveyed. The derived feature maps are then encoded in DDE exploitation. The independent datasets are then compared with other two methods, namely, GRU and XGBOOST. All analyses were conducted using 32, 64 and 128 filters on our proposed CNN method. The cross-validation datasets achieved an accuracy score of 0.8762%, whereas the accuracy of independent datasets was 0.7621%. Additional variables were derived on the basis of ROC AUC with fivefold cross-validation was achieved score is 0.95%. The performance of our model and that of other models in terms of sensitivity (0.9028%) and specificity (0.8497%) was compared. The overall accuracy of our model was 0.9133% compared with 0.8310% for the other model.
Highlights
Enzymes are at the core of biological processes because their reactions are vital biological activities
We introduce the Convolutional Neural Network (CNN) model classification for the predicting the functions of enzymatic protein sequences
We used the proposed CNN model to show a superior model for the entire set of filters, such as 32, 64, and 128 parameters, by using a fivefold validation test set as independent classification
Summary
Enzymes are at the core of biological processes because their reactions are vital biological activities. Enzymes catalyze and spread to all parts of organisms and are involved in all biochemical reactions. Enzymes are important because living organisms cannot survive without enzymatic reactions and biotechnological industries cannot generate products without enzymatic support. An automated measurement method for evaluating protein function based on its sequence is one of the key problems in bioinformatics. This task is a time-consuming, and a faster classification method is obviously needed. Structural protein groups are closely associated with the composition of amino acids. Aside from amino acid compositions, prediction accuracy is high when the sequence order is considered along the primary protein structure
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