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基于人工智能的南海海葵ShK结构域肽的大规模结构与活性预测分析

Large-Scale AI-Based Structure and Activity Prediction Analysis of ShK Domain Peptides from Sea Anemones in the South China Sea.

作者信息

Hua Ziqiang, Lin Limin, Yang Wanting, Ma Linlin, Huang Meiling, Gao Bingmiao

机构信息

Engineering Research Center of Tropical Medicine Innovation and Transformation of Ministry of Education, Hainan Key Laboratory for Research and Development of Tropical Herbs, International Joint Research Center of Human-Machine Intelligent Collaborative for Tumor Precision Diagnosis and Treatment of Hainan Province, School of Pharmacy, Hainan Medical University, Haikou 571199, China.

Griffith Institute for Drug Discovery (GRIDD), School of Environment and Science, Griffith University, Nathan, QLD 4111, Australia.

出版信息

Mar Drugs. 2025 Feb 16;23(2):85. doi: 10.3390/md23020085.

Abstract

Sea anemone peptides represent a valuable class of biomolecules in the marine toxin library due to their various structures and functions. Among these, ShK domain peptides are particularly notable for their selective inhibition of the Kv1.3 channel, holding great potential for applications in immune regulation and the treatment of metabolic disorders. However, these peptides' structural complexity and diversity have posed challenges for functional prediction. In this study, we compared 36 ShK domain peptides from four species of sea anemone in the South China Sea and explored their binding ability with Kv1.3 channels by combining molecular docking and dynamics simulation studies. Our findings highlight that variations in loop length, residue composition, and charge distribution among ShK domain peptides affect their binding stability and specificity. This work presents an efficient strategy for large-scale peptide structure prediction and activity screening, providing a valuable foundation for future pharmacological research.

摘要

海葵肽因其多样的结构和功能,在海洋毒素库中是一类有价值的生物分子。其中,ShK结构域肽因其对Kv1.3通道的选择性抑制作用而尤为显著,在免疫调节和代谢紊乱治疗方面具有巨大的应用潜力。然而,这些肽的结构复杂性和多样性给功能预测带来了挑战。在本研究中,我们比较了来自中国南海四种海葵的36种ShK结构域肽,并通过结合分子对接和动力学模拟研究,探索了它们与Kv1.3通道的结合能力。我们的研究结果表明,ShK结构域肽的环长度、残基组成和电荷分布的变化会影响其结合稳定性和特异性。这项工作提出了一种大规模肽结构预测和活性筛选的有效策略,为未来的药理学研究提供了有价值的基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/794a/11857629/64e96f44b5ca/marinedrugs-23-00085-g001.jpg

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