MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization.

Journal: Nucleic acids research
Published Date:

Abstract

MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which results in a real-time prediction for a large number of proteins. It takes less than three minutes to predict for 1000 sequences per PTM type. The output is presented at the amino acid level for the user-selected PTM types. The framework has been benchmarked and has demonstrated competitive performance in PTM site predictions by other researchers. In this webserver, we updated the previous framework by utilizing more advanced ensemble techniques, and providing prediction and visualization for multiple PTMs simultaneously for users to analyze potential PTM cross-talks directly. Besides prediction, users can interactively review the predicted PTM sites in the context of known PTM annotations and protein 3D structures through homology-based search. In addition, the server maintains a local database providing pre-processed PTM annotations from Uniport/Swiss-Prot for users to download. This database will be updated every three months. The MusiteDeep server is available at https://www.musite.net. The stand-alone tools for locally using MusiteDeep are available at https://github.com/duolinwang/MusiteDeep_web.

Authors

  • Duolin Wang
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.
  • Dongpeng Liu
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.
  • Jiakang Yuchi
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.
  • Fei He
    Biology Department, Brookhaven National Laboratory, Upton, New York, USA.
  • Yuexu Jiang
    Department of Electrical Engineering and Computer Science, Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211, USA.
  • Siteng Cai
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.
  • Jingyi Li
    Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology and College of Veterinary Medicine, Huazhong Agricultural University, 430070 Wuhan, PR China. Electronic address: lijingyi@mail.hzau.edu.cn.
  • Dong Xu
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.