Abstract
The accurate of i identificationntrinsically disordered proteins or protein regions is of great importance, as they are involved in critical biological process and related to various human diseases. In this paper, we develop a deep neural network that is based on the well-known VGG16. Our deep neural network is then trained through using 1450 proteins from the dataset DIS1616 and the trained neural network is tested on the remaining 166 proteins. Our trained neural network is also tested on the blind test set R80 and MXD494 to further demonstrate the performance of our model. 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 of our trained deep neural network exceed the corresponding values of existing prediction methods.
Highlights
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]
R80 and 0.4577 on the blind test set MXD494. All of these Matthews correlation coefficient (MCC) values of our trained deep neural network exceed the corresponding values of existing prediction methods
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
Summary
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]. 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]. It is expensive and time-consuming to identify disordered proteins through experiments, so numerous computational methods have been proposed to predict disordered proteins
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