Large-Scale AI-Based Structure and Activity Prediction Analysis of ShK Domain Peptides from Sea Anemones in the South China Sea
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Marine Drugs |
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| Online Access: | https://www.mdpi.com/1660-3397/23/2/85 |
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| author | Ziqiang Hua Limin Lin Wanting Yang Linlin Ma Meiling Huang Bingmiao Gao |
| author_facet | Ziqiang Hua Limin Lin Wanting Yang Linlin Ma Meiling Huang Bingmiao Gao |
| author_sort | Ziqiang Hua |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-276a5e6c53a543f384b5a6d85bc680b8 |
| institution | OA Journals |
| issn | 1660-3397 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Marine Drugs |
| spelling | doaj-art-276a5e6c53a543f384b5a6d85bc680b82025-08-20T02:03:25ZengMDPI AGMarine Drugs1660-33972025-02-012328510.3390/md23020085Large-Scale AI-Based Structure and Activity Prediction Analysis of ShK Domain Peptides from Sea Anemones in the South China SeaZiqiang Hua0Limin Lin1Wanting Yang2Linlin Ma3Meiling Huang4Bingmiao Gao5Engineering 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, ChinaEngineering 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, ChinaEngineering 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, ChinaGriffith Institute for Drug Discovery (GRIDD), School of Environment and Science, Griffith University, Nathan, QLD 4111, AustraliaEngineering 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, ChinaEngineering 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, ChinaSea 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.https://www.mdpi.com/1660-3397/23/2/85AlphaFoldsea anemone peptidesKv1.3 channelmodelingsimulation |
| spellingShingle | Ziqiang Hua Limin Lin Wanting Yang Linlin Ma Meiling Huang Bingmiao Gao Large-Scale AI-Based Structure and Activity Prediction Analysis of ShK Domain Peptides from Sea Anemones in the South China Sea Marine Drugs AlphaFold sea anemone peptides Kv1.3 channel modeling simulation |
| title | Large-Scale AI-Based Structure and Activity Prediction Analysis of ShK Domain Peptides from Sea Anemones in the South China Sea |
| title_full | Large-Scale AI-Based Structure and Activity Prediction Analysis of ShK Domain Peptides from Sea Anemones in the South China Sea |
| title_fullStr | Large-Scale AI-Based Structure and Activity Prediction Analysis of ShK Domain Peptides from Sea Anemones in the South China Sea |
| title_full_unstemmed | Large-Scale AI-Based Structure and Activity Prediction Analysis of ShK Domain Peptides from Sea Anemones in the South China Sea |
| title_short | Large-Scale AI-Based Structure and Activity Prediction Analysis of ShK Domain Peptides from Sea Anemones in the South China Sea |
| title_sort | large scale ai based structure and activity prediction analysis of shk domain peptides from sea anemones in the south china sea |
| topic | AlphaFold sea anemone peptides Kv1.3 channel modeling simulation |
| url | https://www.mdpi.com/1660-3397/23/2/85 |
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