Prediction of ATP-binding sites in membrane proteins using a two-dimensional convolutional neural network

Trinh Trung Duong Nguyen, Nguyen Quoc Khanh Le, Rosdyana Mangir Irawan Kusuma, Yu Yen Ou

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


Membrane proteins, the most important drug targets, account for around 30% of total proteins encoded by the genome of living organisms. An important role of these proteins is to bind adenosine triphosphate (ATP), facilitating crucial biological processes such as metabolism and cell signaling. There are several reports elucidating ATP-binding sites within proteins. However, such studies on membrane proteins are limited. Our prediction tool, DeepATP, combines evolutionary information in the form of Position Specific Scoring Matrix and two-dimensional Convolutional Neural Network to predict ATP-binding sites in membrane proteins with an MCC of 0.89 and an AUC of 99%. Compared to recently published ATP-binding site predictors and classifiers that use traditional machine learning algorithms, our approach performs significantly better. We suggest this method as a reliable tool for biologists for ATP-binding site prediction in membrane proteins.

Original languageEnglish
Pages (from-to)86-93
Number of pages8
JournalJournal of Molecular Graphics and Modelling
Publication statusPublished - Nov 1 2019
Externally publishedYes


  • Bioinformatics
  • Convolutional neural network
  • Deep learning
  • Imbalanced data
  • Membrane protein
  • Position specific scoring matrix

ASJC Scopus subject areas

  • Spectroscopy
  • Physical and Theoretical Chemistry
  • Computer Graphics and Computer-Aided Design
  • Materials Chemistry


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