AcrNET: predicting anti-CRISPR with deep learning.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria (i.e. CRISPR-Cas), offering promise for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging due to their high variability and fast evolution. Existing biological studies rely on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number. Computational methods struggle with prediction performance. To address these issues, we propose a novel deep neural network for anti-CRISPR analysis (AcrNET), which achieves significant performance.

Authors

  • Yunxiang Li
    The Key Lab of RF Circuits and Systems of Ministry of Education, Microelectronics CAD Center, Hangzhou Dianzi University, Hangzhou, 310018, China.
  • Yumeng Wei
    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR 999077, China.
  • Sheng Xu
    School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing, 211200, China.
  • Qingxiong Tan
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, Hong Kong.
  • Licheng Zong
    Department of Computer Science and Engineering (CSE), The Chinese University of Hong Kong (CUHK), 999077, Hong Kong SAR, China.
  • Jiuming Wang
    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR 999077, China.
  • Yixuan Wang
    Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Jiayang Chen
    Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
  • Liang Hong
    Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR 999077, China.
  • Yu Li
    Department of Public Health, Shihezi University School of Medicine, 832000, China.