An Ensemble Deep Learning based Predictor for Simultaneously Identifying Protein Ubiquitylation and SUMOylation Sites.

Journal: BMC bioinformatics
PMID: 34689734

Abstract

BACKGROUND: Several computational tools for predicting protein Ubiquitylation and SUMOylation sites have been proposed to study their regulatory roles in gene location, gene expression, and genome replication. However, existing methods generally rely on feature engineering, and ignore the natural similarity between the two types of protein translational modification. This study is the first all-in-one deep network to predict protein Ubiquitylation and SUMOylation sites from protein sequences as well as their crosstalk sites simultaneously. Our deep learning architecture integrates several meta classifiers that apply deep neural networks to protein sequence information and physico-chemical properties, which were trained on multi-label classification mode for simultaneously identifying protein Ubiquitylation and SUMOylation as well as their crosstalk sites.

Authors

  • Fei He
    Biology Department, Brookhaven National Laboratory, Upton, New York, 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.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Xiaowei Zhao
    School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China. Electronic address: zhaoxw303@nenu.edu.cn.
  • Ye Han
    School of Information Technology, Jilin Agricultural University, Changchun, China.

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