Protein Subcellular and Secreted Localization Prediction Using Deep Learning

Hamza Zidoum, Mennatollah Magdy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Predicting the protein structure and discovering its function according to its location in the cell is crucial for understanding the cellular translocation process and has direct applications in drug discovery. Computational prediction of protein localization is alternative to the time consuming experimental counterpart approach. We use deep learning approach to enhance the prediction accuracy while reducing the time in predicting uncharacterized protein sequence localization site. Our approach is based on general biological features of the protein sequence, and compartment specific features to which we added the physico-chemical sequence features. We collected the protein sequences from UniProt1/SWISS-PROT, then we collected the features for each protein. We consider five locations in the dataset, namely cytoplasm (CP), inner membrane (IM), outer membrane (OM), periplasm (PE) and secreted (SEC). We choose the protein sequences to be at least 100 amino-Acid-length and a maximum length of 1000 amino acids. Each location contains 500 protein sequences. We propose a deep learning prediction method for bacteria taxonomy that combines a one-versus-one and one-versus all models along with feature selec-Tion using linear SVM ranking, and deep auto-encoders to initialize the weights. The method achieves overall accuracy of 97.81% using 10-fold cross-validation on our data. Our approach outperforms the current state of the art computational methods in protein subcellular localization on the selected dataset.

Original languageEnglish
Title of host publication2018 International Conference on Computing Sciences and Engineering, ICCSE 2018 - Proceedings
EditorsHazem Raafat, Mostafa Abd-El-Barr, Muhammad Sarfraz, Paul Manuel
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538646809
DOIs
Publication statusPublished - Jun 5 2018
Event2nd International Conference on Computing Sciences and Engineering, ICCSE 2018 - Kuwait City, Kuwait
Duration: Mar 11 2018Mar 13 2018

Other

Other2nd International Conference on Computing Sciences and Engineering, ICCSE 2018
CountryKuwait
CityKuwait City
Period3/11/183/13/18

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Modelling and Simulation

Cite this

Zidoum, H., & Magdy, M. (2018). Protein Subcellular and Secreted Localization Prediction Using Deep Learning. In H. Raafat, M. Abd-El-Barr, M. Sarfraz, & P. Manuel (Eds.), 2018 International Conference on Computing Sciences and Engineering, ICCSE 2018 - Proceedings (pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCSE1.2018.8374220