基于深度学习的蛋白质⁃ATP结合位点预测

doi:10.13229/j.cnki.jdxbgxb20200723

摘要/Abstract

摘要：

为了提高识别蛋白质-ATP结合位点预测精度，提出了基于Inception架构的深度网络模型Inception_base，同时对网络模型和训练策略进行优化和改进，提出了新的网络模型Inception_evolution。通过两组数据集在该模型上测试，获得AUC分别为0.885和0.918，均优于其他对比机器学习方法。实验结果表明，深度学习方法可以应用于蛋白质-ATP结合位点预测问题中，该模型能够更精确预测蛋白质-ATP结合位点。

关键词: 生物信息学, 蛋白质?ATP结合位点预测, 特征提取, 深度学习, Inception网络模型

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

中图分类号:

TP399

刘桂霞,裴志尧,宋佳智. 基于深度学习的蛋白质⁃ATP结合位点预测[J]. 吉林大学学报(工学版), 2022, 52(1): 187-194.

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