Review Article | Open Access
Volume 2022 |Article ID 9895270 | https://doi.org/10.34133/2022/9895270

High-Throughput Prediction and Design of Novel Conopeptides for Biomedical Research and Development

Bingmiao Gao iD ,1 Yu HuangiD ,2 Chao Peng,2,3 Bo Lin,4 Yanling Liao,1 Chao Bian,2 Jiaan Yang,5 Qiong Shi iD 2,3

1Key Laboratory of Tropical Translational Medicine of Ministry of Education, School of Pharmacy, Hainan Medical University, Haikou, Hainan 570102, China
2Shenzhen Key Lab of Marine Genomics, Guangdong Provincial Key Lab of Molecular Breeding in Marine Economic Animals, BGI Academy of Marine Sciences, BGI Marine, Shenzhen, Guangdong 518081, China
3BGI-Marine Research Institute for Biomedical Technology, Shenzhen Huahong Marine Biomedicine Co. Ltd., Shenzhen, Guangdong 518119, China
4Hainan Provincial Key Laboratory of Carcinogenesis and Intervention, Hainan Medical University, Haikou, Hainan 570102, China
5Research and Development Department, Micro Pharmtech Ltd., Wuhan, Hubei 430075, China

Received 
24 Apr 2022
Accepted 
23 Jul 2022
Published
07 Nov 2022

Abstract

Cone snail venoms have been considered a valuable treasure for international scientists and businessmen, mainly due to their pharmacological applications in development of marine drugs for treatment of various human diseases. To date, around 800 Conus species are recorded, and each of them produces over 1,000 venom peptides (termed as conopeptides or conotoxins). This reflects the high diversity and complexity of cone snails, although most of their venoms are still uncharacterized. Advanced multiomics (such as genomics, transcriptomics, and proteomics) approaches have been recently developed to mine diverse Conus venom samples, with the main aim to predict and identify potentially interesting conopeptides in an efficient way. Some bioinformatics techniques have been applied to predict and design novel conopeptide sequences, related targets, and their binding modes. This review provides an overview of current knowledge on the high diversity of conopeptides and multiomics advances in high-throughput prediction of novel conopeptide sequences, as well as molecular modeling and design of potential drugs based on the predicted or validated interactions between these toxins and their molecular targets.

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