Novel Computational Approaches in the Discovery and Identification of Bioactive Peptides: A Bioinformatics Perspective.

Journal: Journal of agricultural and food chemistry
Published Date:

Abstract

Bioactive peptides are protein molecules known for their specific biological functions, offering promising applications across various fields including medicine, food, and cosmetics. Traditional approaches to the investigation of bioactive peptides typically encompass extraction, separation, purification, identification, and experimental evaluation. However, these methodologies are frequently subject to human-related variables, which consequently lead to reduced efficiency and compromised accuracy. Bioinformatics techniques, including computer simulation screening, quantitative structure-activity relationship (QSAR) analysis, and machine learning, have emerged as powerful tools in the field of bioactive peptide research. These advanced methodologies not only enhance the efficiency of bioactive peptide screening but also provide valuable insights into the underlying mechanisms of action of these peptides. This review discusses the identification, analysis, and evaluation of bioactive peptides through innovative bioinformatics technology while also highlighting traditional techniques that have been developed and improved. This review provides robust theoretical support and valuable references for future research and applications involving bioactive peptides.

Authors

  • Mengjiao Li
    College of Science, Sichuan Agricultural University, Xin Kang Road, Yucheng District, Ya'an 625014, PR China.
  • Mengting Chen
    Department of Gastroenterology, Shanghai First People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
  • Yizhen Sun
    Key Laboratory of Agricultural Products Cold Chain Logistics, Ministry of Agriculture and Rural Affairs, Institute of Agro-Products Processing and Nuclear Agricultural Technology, Hubei Academy of Agricultural Sciences, Wuhan 430064, China.
  • Liu Shi
    Department of Psychiatry, University of Oxford, Oxford, UK.
  • Xiaojia Guo
    Key Laboratory of Agricultural Products Cold Chain Logistics, Ministry of Agriculture and Rural Affairs, Institute of Agro-Products Processing and Nuclear Agricultural Technology, Hubei Academy of Agricultural Sciences, Wuhan 430064, China.
  • Sheng Chen
    Department of Thoracic Surgery, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.
  • Lang Chen
    University of Wisconsin-Madison.
  • Guangquan Xiong
    Key Laboratory of Agricultural Products Cold Chain Logistics, Ministry of Agriculture and Rural Affairs, Institute of Agro-Products Processing and Nuclear Agricultural Technology, Hubei Academy of Agricultural Sciences, Wuhan 430064, China.
  • Weiqing Sun
    College of Life Science, Yangtze University, Jingzhou, Hubei 434023, P. R. China. Electronic address: sunweiqing@yangtzeu.edu.cn.
  • Ruichang Gao
    Qinba State Key Laboratory of Biological Resources and Ecological Environment, QinLing-Bashan Moun-tains Bioresources Comprehensive Development 2011 C. I. C, Shaanxi Province Key Laboratory of Bio-Resources, College of Bioscience and Bioengineering Shaanxi University of Technology, Hanzhong, China.
  • Liang Ke
    Hubei Monopterus albus Industry Technology Research Institute, Xiantao 433012, China.
  • Lan Wang
    The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 611731, China.
  • Wenjin Wu
    Key Laboratory of Agricultural Products Cold Chain Logistics, Ministry of Agriculture and Rural Affairs, Institute of Agro-Products Processing and Nuclear Agricultural Technology, Hubei Academy of Agricultural Sciences, Wuhan 430064, China.