# The Prediction of Intrinsically Disordered Proteins Based on Feature Selection
## 1 Introduction  
The intrinsically disordered proteins (IDPs) have at least one region lacking a unique 3D structure [1]. They exist as conformational ensembles without equilibrium positions for their atom positions and bond angles [2]. Their mobile flexibility and structural instability are encoded by their amino acid sequences [3]. They play a crucial role in a variety of important biological functions [4]. It is confirmed that some of these IDPs are related to many important regulatory functions in the cell [5], which have great impact on diseases such as Parkinson's disease, Alzheimer's disease and certain types of cancers [6]. Thus, accurately identifying IDPs is vital to obtaining more effective drug designs, better protein expressions and functional annotations. However, it is often difficult to purify and crystallize the disordered protein regions [7], which makes the identification of IDPs usually both expensive and time-consuming with experimental approaches [6]. Thus, it is essential to predict IDPs through the computational approaches. For the past few decades, many computational schemes for predicting IDPs have been proposed. These schemes can be roughly classified into three categories: (1) employ the amino acid physicochemical properties and propensity scales to predict IDPs, such as FoldIndex [8], GlobPlot [9], IUPred [10], FoldUnfold [11] and IsUnstruct [12]. These schemes are based on the scale of charge/hydropathy, the relative propensity of an amino acid residue, the inter-residue contacts, and statistical physics, respectively. In particular, IsUnstruct performs well in predicting, which is an approximation of the Ising model that replaces the interaction term between neighbors with a penalty for changing between ordered and disordered states, so it is selected as a scheme of comparison in this paper. (2) Exploit machine learning techniques for the prediction of IDPs. In this category, theprediction of IDPs is considered as a binary classification problem based on many machine learning algorithms, such as artificial neural network, support vector machine and so on. The examples of this category include PONDRs [13], RONN [14], DISOPRED3 [15], BVDEA [6], DisPSSMP [16], SPINE-D [17], ESpritz [18], and so on. In this paper, we choose ESpritz as the other scheme of comparison, which is based on a bidirectional recursive neural network and also performs well in predicting. (3) Meta-approaches, such as MFDp [19], MetaPrDOS [20] and Meta-Disorder predictor [21], which use several different predictors and their trade-off to yield an optimal prediction for IDPs. In this paper, we develop a scheme for predicting intrinsically disordered proteins which requires computing three types of features including eight structural properties, seven physicochemical properties and 20 pieces of evolutionary information. Without sacrificing our prediction accuracy of IDPs, we use a preprocessing procedure which can greatly reduce the input features and hence dramatically simplify as well as speed up identification of IDPs. Furthermore, we use two different windows to preprocess for each residue, which highlight the properties of the target residue and the surrounding around it, respectively. Thus, there are just 70 features for each residue. This number is far fewer than that of most other methods [16; 17]. For training our scheme, we introduce the Adam algorithm [22] for the back propagation together with the dropout algorithm to avoid overfitting [23] in the multi-layer perceptron (MLP) neural network. The dataset DIS803 from the latest DisProt [24] database is used for training as well as testing our procedure. The dataset R80 collected by [PERSON] et al. in [14] is used as the blind test set. Using the same input features, we compare the prediction performance of the MLP with support vector machine (SVM) [25] and Fisher discriminant [26]. The predictive results express that MLP is more appropriate to train our scheme.