Identification of Intrinsically Disordered Protein Regions Based on Deep Neural Network-VGG16

• Main Authors: Pengchang Xu, Jiaxiang Zhao, Jie Zhang
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Algorithms
• Subjects: Intrinsically disordered proteins; VGG16; deep neural network
• Online Access: https://www.mdpi.com/1999-4893/14/4/107

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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>C</mi><mi>C</mi></mrow></semantics></math></inline-formula> value of our trained deep neural network is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mo>.</mo><mn>5132</mn></mrow></semantics></math></inline-formula> on the test set DIS166, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mo>.</mo><mn>5270</mn></mrow></semantics></math></inline-formula> on the blind test set R80 and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0</mn><mo>.</mo><mn>4577</mn></mrow></semantics></math></inline-formula> on the blind test set MXD494. All of these <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>M</mi><mi>C</mi><mi>C</mi></mrow></semantics></math></inline-formula> values of our trained deep neural network exceed the corresponding values of existing prediction methods.