基于深度学习的蛋白质⁃ATP结合位点预测

doi:10.13229/j.cnki.jdxbgxb20200723

Abstract

Abstract:

Accurately identifying protein-ATP binding site is important for the research of protein function and the disease drug design. In order to improve the prediction accuracy of identifying protein-ATP binding site， first， a deep neural network model， Inceptiobase， based on inception architecture was proposed. Then， the network model and training strategy were optimized and improved through large number of experimental tests， and an upgraded deep neural network model， Inception_evolution， was proposed. Through two sets of data sets tested on the model， the AUCs are 0.885 and 0.918， respectively， which are better than other comparative machine learning methods. The experimental results show that the deep learning method can be applied to the protein-ATP binding site prediction problem， and the model Inception_evolution can predict the protein-ATP binding site more accurately.

Key words: bioinformatics, protein-ATP binding site prediction, feature extraction, deep learning, Inception neural network model

CLC Number:

TP399

Gui-xia LIU,Zhi-yao PEI,Jia-zhi SONG. Prediction of protein-ATP binding site based on deep learning[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 187-194.

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方法	ACC	SE	SP	MCC	AUC
CT	0.967	0.189	0.995	0.324	-
ATPint	0.655	0.512	0.660	0.066	0.606
ATPsite	0.969	0.367	0.991	0.451	0.868
NsitePred	0.967	0.460	0.985	0.476	0.875
TATPsite	0.972	0.458	0.991	0.530	0.882
TATP	0.969	0.489	0.989	0.542	0.912
TNUCs	0.975	0.516	0.992	0.584	-
Inc_base	0.968	0.625	0.979	0.548	0.906

方法	ACC	SE	SP	MCC	AUC
CT	0.964	0.239	0.998	0.451	-
NsitePred	0.954	0.467	0.977	0.456	0.852
TATPsite	0.968	0.413	0.995	0.559	0.853
TNUCs	0.972	0.469	0.997	0.627	0.856
ATPseq	0.972	0.545	0.993	0.639	0.878
Inc_base	0.976	0.432	0.989	0.577	0.882

方法	ACC	SE	SP	MCC	AUC
CT	0.967	0.189	0.995	0.324	-
ATPint	0.655	0.512	0.660	0.066	0.606
ATPsite	0.969	0.367	0.991	0.451	0.868
NsitePred	0.967	0.460	0.985	0.476	0.875
TATPsite	0.972	0.458	0.991	0.530	0.882
TATP	0.969	0.489	0.989	0.542	0.912
TNUCs	0.975	0.516	0.992	0.584	-
Inc_evo	0.981	0.536	0.989	0.569	0.918

方法	ACC	SE	SP	MCC	AUC
CT	0.964	0.239	0.998	0.451	-
NsitePred	0.954	0.467	0.977	0.456	0.852
TATPsite	0.968	0.413	0.995	0.559	0.853
TNUCs	0.972	0.469	0.997	0.627	0.856
ATPseq	0.972	0.545	0.993	0.639	0.878
Inc_evo	0.984	0.452	0.995	0.588	0.885

Prediction of protein-ATP binding site based on deep learning

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