ET-GRU

Using multi-layer gated recurrent units to identify electron transport proteins

Nguyen Quoc Khanh Le, Edward Kien Yee Yapp, Hui Yuan Yeh

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Background: Electron transport chain is a series of protein complexes embedded in the process of cellular respiration, which is an important process to transfer electrons and other macromolecules throughout the cell. It is also the major process to extract energy via redox reactions in the case of oxidation of sugars. Many studies have determined that the electron transport protein has been implicated in a variety of human diseases, i.e. diabetes, Parkinson, Alzheimer's disease and so on. Few bioinformatics studies have been conducted to identify the electron transport proteins with high accuracy, however, their performance results require a lot of improvements. Here, we present a novel deep neural network architecture to address this problem. Results: Most of the previous studies could not use the original position specific scoring matrix (PSSM) profiles to feed into neural networks, leading to a lack of information and the neural networks consequently could not achieve the best results. In this paper, we present a novel approach by using deep gated recurrent units (GRU) on full PSSMs to resolve this problem. Our approach can precisely predict the electron transporters with the cross-validation and independent test accuracy of 93.5 and 92.3%, respectively. Our approach demonstrates superior performance to all of the state-of-the-art predictors on electron transport proteins. Conclusions: Through the proposed study, we provide ET-GRU, a web server for discriminating electron transport proteins in particular and other protein functions in general. Also, our achievement could promote the use of GRU in computational biology, especially in protein function prediction.

Original languageEnglish
Article number377
JournalBMC Bioinformatics
Volume20
Issue number1
DOIs
Publication statusPublished - Jul 6 2019
Externally publishedYes

Fingerprint

Electron Transport
Multilayer
Carrier Proteins
Protein
Unit
Computational Biology
Proteins
Position-Specific Scoring Matrices
Neural Networks
Electrons
Cell Respiration
Neural networks
Information Services
Redox reactions
Bioinformatics
Medical problems
Network architecture
Macromolecules
Sugars
Oxidation-Reduction

Keywords

  • Cellular respiration
  • Convolutional neural network
  • Deep learning
  • Electron transport chain
  • Gated recurrent units
  • Position specific scoring matrix
  • Protein function prediction
  • Recurrent neural network
  • Transport protein
  • Web server

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Cite this

ET-GRU : Using multi-layer gated recurrent units to identify electron transport proteins. / Le, Nguyen Quoc Khanh; Yapp, Edward Kien Yee; Yeh, Hui Yuan.

In: BMC Bioinformatics, Vol. 20, No. 1, 377, 06.07.2019.

Research output: Contribution to journalArticle

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