A deep learning method to predict bacterial ADP-ribosyltransferase toxins.

Journal: Bioinformatics (Oxford, England)
Published Date: Jul 1, 2024

Abstract

MOTIVATION: ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members.

Authors

  • Dandan Zheng
    Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, USA.
  • Siyu Zhou
    Department of Orthopaedics, Peking University Third Hospital, Beijing, China.
  • Lihong Chen
    NHC Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100176, China.
  • Guansong Pang
    Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA 5005, Australia.
  • Jian Yang
    Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, SK, Canada.

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