Today, we introduce WafaaWardah et al. of the University of the South Pacific, who published an article in the Journal of Economics, "Predicting Protein-Peptide Conjugates with Convolutional Neural Networks".Prediction of protein-peptide binding sites plays an important role in disease prevention and drug development.However, the existing prediction methods did not show good results in actual prediction, especially the sensitivity did not even reach 50%.This paper presents a method for predicting protein-peptide binding sites using CNN framework to process "visualized" protein feature data.The authors innovatively introduced the "sliding window method" to transform the initial protein feature data into "visualized" matrix information, and then input the CNN framework for training.Finally, the prediction results are output through fully connected network, and the Bayesian optimization method is embedded in the CNN framework to deal with the hyper-parameters, so that the model achieves excellent results on the test set.
I. Research background
Studying the interaction between protein and peptide is of great significance in the field of bioinformatics, and the interaction between protein molecule and peptide can be analyzed by studying its complex structure.But we know that such complex structures account for only a small fraction, and it is not only costly but also ineffective to analyze their interactions by doing biological experiments.Therefore, predicting the binding region of proteins and peptides by computer will bring great help to experimental research.
Existing methods for predicting protein-peptide binding sites perform well on experimental data sets, but perform poorly on the accuracy and sensitivity of binding residues in actual prediction.To address this problem, the authors used "visualization" within the CNN framework.
2. Models and methods
Fig. 1 Framework of protein-peptide binding site prediction model based on CNN
2.1 Feature selection and preprocessing.
In terms of feature selection, the authors used several groups of features with good discrimination in predicting protein-peptide binding sites, such as hemispherical structure (HSE), secondary structure (SS), auxiliary surface area (ASA), PSSM, etc.Then, the authors extended these sets of features into a set of numerical matrices ([1, 38]), representing all the above features with 38 values.
2.2 "Visualization" feature transformation.
In order to input the features of proteins into the CNN framework, it is necessary to transform their features into "visual" matrix information.The authors used the "sliding window" method to represent each residue in the protein chain as a sequence containing three neighbors on the left and three neighbors on the right with a fixed size window (in this paper, the size is 7, the window is similar to the matrix of [1, 7]), which is equivalent to representing one residue in the middle with a characteristic matrix of seven residues (each residue is represented by a matrix of [7, 38]).
Figure 2 Sliding window method
2.3 Model training.
The CNN framework generally includes two convolution layer...
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