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

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

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


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.

Original languageEnglish
Article number1950005
JournalJournal of Bioinformatics and Computational Biology
Issue number1
Publication statusPublished - Feb 1 2019
Externally publishedYes


  • deep learning
  • GTP binding site
  • nucleotide binding prediction
  • position-specific scoring matrix
  • Rab protein
  • vesicle membrane trafficking

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications


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