1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
Investigation of neuropeptide databases to identify and predict important Neuropeptides for pest control management
Paper ID : 1313-ICB10
Authors:
Mehrbanoo Kazemi Alamuti1, Mohammad Majdi2, Mohammad Reza Ghaffari *3, Ghasem Hossini Salekdeh4
1Agricultural Biotechnology Research Institute of Iran, (ABRII)
2University of Kurdistan
3Agricultural Biotechnology Research Institute of Iran (ABRII)
4Macquarie University
Abstract:
Pest insects can have unfavorable and negative impacts on agricultural production and the food supply. The damage caused by agricultural pests is estimated at an 18-20% loss in annual and $ 470 billion on a global scale. Although insecticides have helped minimize the impact of insect pests, chemical control entails economic, health, and environmental costs. Neuropeptides and G-protein coupled receptors are essential signaling molecules in multicellular organisms, and they regulate various physiological processes, such as reproduction, osmoregulation, growth, and development. Besides, these small peptides and their target receptors have been potent and promising targets for pest control and new environmentally friendly insecticidal agents. This study examines the features and applications of NeuroPep, NeuroPID, DINeR, and NeuroPIpred databases to identify and predict new neuropeptides in pests. Likewise, the prediction models will develop using input features like amino acids and dipeptide compositions, binary profiles, and implementing different machine learning techniques. We believe that research on neuropeptides and G-protein coupled receptors will provide more information for pest insect management.
Keywords:
Neuropeptides, Insecticide, Database, Machine Learning
Status : Paper Accepted (Poster Presentation)