# Identification of Intrinsically Disordered Protein Regions Based on Deep Neural Network-VGG16
## 1 Introduction  
The intrinsically disordered proteins (IDPs) have at least one region that do not have stable three-dimensional structure [1, 2, 3], and they are widespread in nature [4]. IDPs are involved in key biological processes, such as the folding of nucleic acids, transcription and translation, protein-protein interactions, protein-RNA interactions, etc. [5, 6, 7]. Besides, IDPs are related to many human diseases, such as cancer, Alzheimer's disease, cardiovascular disease, and various genetic diseases [8, 9]. Therefore, the accurate identification of IDPs is crucial for the study of protein structure, function, and drug design. In the past few decades, various experimental methods have been proposed to identify IDPs, such as nuclear magnetic resonance (NMR), X-ray crystallography, and circular dichroism (CD) [10, 11]. However, it is expensive and time-consuming to identify disordered proteins through experiments, so numerous computational methods have been proposed to predict disordered proteins.  
The available computational methods can be broadly classified into three categories: (1) scoring function-based methods. Methods in this category use a function to predict the propensity for disorder. Furthermore, they commonly employ the physiochemical properties of individual amino acids in the protein chain as its input. Example methods include IsUnstruct [12], GlobPlot [13], FoldUnfold [14], and IUPred [15], etc. (2) Machine learning-based methods. In this category, predictive models that are generated by machine learning algorithms (such as neural networks and support vector machines) are used to calculate the disorder tendencies. Typically, these predictive models use physiochemical properties of amino acid, evolutionary information, and solvent accessibility as inputs. These include RFPR-IDP [16], IDP-Seq2 Seq [17], SPOT-Disorder [18], SPOT-Disorder2 [19], DISOPRED3 [20], SPINE-D [21], and ESpritz [22]. (3) Meta methods. They include MFDp [23], MetaDisorder [24], and MetaPrDOS [25]. These methods combine the results of multiple predictors to obtain the optimal prediction of IDPs.  
Because traditional machine learning-based methods do not efficiently capture local patterns between amino-acids, we chose to use CNN as our network structure, which isknown to be relatively lighter than other neural networks (e.g., recurrent neural networks), and it is effective in capturing local patterns [26; 27]. In this paper, to capture the patterns of features in more detail, we develop a deep neural network that is comprised of ten identical deep neural structures, with each of them being derived from the original VGG16 model [28] and a MLP network, as shown in Figure 1. Each of these ten identical deep neural architectures is identical to the VGG16 model with no the fully-connected (FC) layer. The outputs of these structures are then used as the inputs of our MLP network for classification, where our MLP consists of two hidden layers with 512 perceptrons in each layer and uses cross entropy as its loss function. Subsequently, we randomly subdivide the dataset DIS1616 [29] into two disjoint subsets with one containing 1450 proteins for training and the other DIS166 having the remaining 166 proteins for testing. The ten-fold cross validation is performed on the training dataset DIS1450. On the test set DIS166 and blind test sets R80 [30] and MXD494 [31], the performance of our trained deep neural network is compared to two recent prediction methods that claim to have the best performances, i.e., RFPR-IDP and SPOT-Disorder 2. The \(MCC\) value of our trained deep neural network is 0.5132 on the test set DIS166, 0.5270 on the blind test set R80 and 0.4577 on the blind test set MXD494. All of these \(MCC\) values from our trained deep neural network exceed the corresponding values of RFPR-IDP and SPOT-Disorder2.