DeepRCI: Predicting ATP-Binding Proteins Using the Residue-Residue Contact Information

卷积神经网络 计算机科学 超参数 残留物(化学) 三磷酸腺苷 结合位点 人工智能 数据挖掘 化学 模式识别(心理学) 生物系统 生物化学 生物
作者
Zhaoxi Zhang,Yulan Zhao,Juan Wang,Maozu Guo
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:26 (6): 2822-2829 被引量:2
标识
DOI:10.1109/jbhi.2021.3137840
摘要

Adenine-5'-triphosphate (ATP) is a direct energy source for various activities of tissues and cells in the body. The release of ATP energies requires the assistance of ATP-binding proteins. Therefore, the identification of ATP-binding proteins is of great significance for the research on organisms. So far, there are several methods for predicting ATP-binding proteins. However, the accuracies of these methods are so low that the predicted proteins are inaccurate. Here, we designed a novel method, called as DeepRCI (based on Deep convolutional neural network and Residue-residue Contact Information), for predicting ATP-binding proteins. In order to maximize the performance of our method, we experimented with different hyperparameters and finally chose a 12-depth-512-filters deep convolutional neural network with an input size of 448*448. By using this model, DeepRCI achieved an accuracy of 93.61% on the test set which means a significant improvement of 11.78% over the state-of-the-art methods. We also compared the performance of residue-residue contact information datasets with different noise levels which are mainly due to gaps in the multiple sequence alignment. Compared with the low-noise dataset, the prediction accuracy on the high-noise dataset is reduced by 6.78%, which affects the performance of DeepRCI to a certain extent. We believe that with the increase of sequence data, this problem will eventually be solved. Finally, we provide a web service of DeepRCI which link can be obtained in Data Availability.
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