Using two-dimensional convolutional neural networks for identifying GTP binding sites in Rab proteins

Nguyen Quoc Khanh Le, Quang Thai Ho, Yu Yen Ou

研究成果: 雜誌貢獻文章同行評審

9 引文 斯高帕斯(Scopus)

摘要

Deep learning has been increasingly and widely used to solve numerous problems in various fields with state-of-the-art performance. It can also be applied in bioinformatics to reduce the requirement for feature extraction and reach high performance. This study attempts to use deep learning to predict GTP binding sites in Rab proteins, which is one of the most vital molecular functions in life science. A functional loss of GTP binding sites in Rab proteins has been implicated in a variety of human diseases (choroideremia, intellectual disability, cancer, Parkinson's disease). Therefore, creating a precise model to identify their functions is a crucial problem for understanding these diseases and designing the drug targets. Our deep learning model with two-dimensional convolutional neural network and position-specific scoring matrix profiles could identify GTP binding residues with achieved sensitivity of 92.3%, specificity of 99.8%, accuracy of 99.5%, and MCC of 0.92 for independent dataset. Compared with other published works, this approach achieved a significant improvement. Throughout the proposed study, we provide an effective model for predicting GTP binding sites in Rab proteins and a basis for further research that can apply deep learning in bioinformatics, especially in nucleotide binding site prediction.
原文英語
文章編號1950005
期刊Journal of Bioinformatics and Computational Biology
17
發行號1
DOIs
出版狀態已發佈 - 2月 1 2019
對外發佈

ASJC Scopus subject areas

  • 生物化學
  • 分子生物學
  • 電腦科學應用

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