Machine learning-based rational design for efficient discovery of allatostatin analogs as promising lead candidates for novel IGRs.

Journal: Pest management science
PMID: 39513221

Abstract

BACKGROUND: Insect neuropeptide allatostatins (ASTs) play a vital role in regulating insect growth, development, and reproduction, making them potential candidates for new insect growth regulators (IGRs). However, the practical use of natural ASTs in pest management is constrained by their long sequences and high production costs, thus the development of AST analogs with shorter sequences and reduced cost is essential. Traditional methods for designing AST analogs are often time-consuming and resource-intensive. This study aims to employ new computational methodologies to understand the structure-activity relationship and efficiently discover potent AST analogs.

Authors

  • Yi-Meng Zhang
    Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing, P. R. China.
  • Qi He
    Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China.
  • Jia-Lin Cui
    Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China.
  • Yan Liu
    Department of Clinical Microbiology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, People's Republic of China.
  • Mei-Zi Wang
    Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing, P. R. China.
  • Xing-Xing Lu
    Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing, P. R. China.
  • Shi-Xiang Pan
    Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing, P. R. China.
  • Chandni Iqbal
    Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing, P. R. China.
  • De-Xing Ye
    Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing, P. R. China.
  • Wen-Yu Sun
    Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing, P. R. China.
  • Xin-Yuan Zhang
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
  • Zhen-Peng Kai
    School of Chemical and Environmental Engineering, Shanghai Institute of Technology, Shanghai, P. R. China.
  • Li Zhang
    Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
  • Xin-Ling Yang
    Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing, P. R. China.

Keywords

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