Investigation of Anticancer Peptides Derived from <i>Arca</i> Species Using In Silico Analysis
This study employed an integrated in silico approach to identify and characterize anticancer peptides (ACPs) derived from <i>Arca</i> species. Using a comprehensive bioinformatics pipeline (BIOPEP, ToxinPred, ProtParam, ChemDraw, SwissTargetPrediction, and I-TASSER), we screened hydrolyz...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/30/7/1640 |
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| author | Jixu Wu Xiuhua Zhang Yuting Jin Man Zhang Rongmin Yu Liyan Song Fei Liu Jianhua Zhu |
| author_facet | Jixu Wu Xiuhua Zhang Yuting Jin Man Zhang Rongmin Yu Liyan Song Fei Liu Jianhua Zhu |
| author_sort | Jixu Wu |
| collection | DOAJ |
| description | This study employed an integrated in silico approach to identify and characterize anticancer peptides (ACPs) derived from <i>Arca</i> species. Using a comprehensive bioinformatics pipeline (BIOPEP, ToxinPred, ProtParam, ChemDraw, SwissTargetPrediction, and I-TASSER), we screened hydrolyzed bioactive peptides from <i>Arca</i> species, identifying seventeen novel peptide candidates. Subsequent in vitro validation revealed three peptides (KW, WQIWYK, KGKWQIWYKSL) with significant anticancer activity, demonstrating both high biosafety and clinical potential. Our findings highlight <i>Arca</i> species proteins as a valuable source of therapeutic ACPs and establish bioinformatics as an efficient strategy for rapid discovery of bioactive peptides. This approach combines computational prediction with experimental validation, offering a robust framework for developing novel peptide-based therapeutics. |
| format | Article |
| id | doaj-art-9ff505903f554c5da7e0793a421a3709 |
| institution | DOAJ |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj-art-9ff505903f554c5da7e0793a421a37092025-08-20T03:03:24ZengMDPI AGMolecules1420-30492025-04-01307164010.3390/molecules30071640Investigation of Anticancer Peptides Derived from <i>Arca</i> Species Using In Silico AnalysisJixu Wu0Xiuhua Zhang1Yuting Jin2Man Zhang3Rongmin Yu4Liyan Song5Fei Liu6Jianhua Zhu7Biotechnological Institute of Chinese Materia Medica, Jinan University, Guangzhou 510632, ChinaShandong Engineering Research Center for Efficient Preparation and Application of Sugar and Sugar Complex, Shandong Academy of Pharmaceutical Science, Jinan 250101, ChinaBiotechnological Institute of Chinese Materia Medica, Jinan University, Guangzhou 510632, ChinaBiotechnological Institute of Chinese Materia Medica, Jinan University, Guangzhou 510632, ChinaBiotechnological Institute of Chinese Materia Medica, Jinan University, Guangzhou 510632, ChinaBiotechnological Institute of Chinese Materia Medica, Jinan University, Guangzhou 510632, ChinaShandong Engineering Research Center for Efficient Preparation and Application of Sugar and Sugar Complex, Shandong Academy of Pharmaceutical Science, Jinan 250101, ChinaBiotechnological Institute of Chinese Materia Medica, Jinan University, Guangzhou 510632, ChinaThis study employed an integrated in silico approach to identify and characterize anticancer peptides (ACPs) derived from <i>Arca</i> species. Using a comprehensive bioinformatics pipeline (BIOPEP, ToxinPred, ProtParam, ChemDraw, SwissTargetPrediction, and I-TASSER), we screened hydrolyzed bioactive peptides from <i>Arca</i> species, identifying seventeen novel peptide candidates. Subsequent in vitro validation revealed three peptides (KW, WQIWYK, KGKWQIWYKSL) with significant anticancer activity, demonstrating both high biosafety and clinical potential. Our findings highlight <i>Arca</i> species proteins as a valuable source of therapeutic ACPs and establish bioinformatics as an efficient strategy for rapid discovery of bioactive peptides. This approach combines computational prediction with experimental validation, offering a robust framework for developing novel peptide-based therapeutics.https://www.mdpi.com/1420-3049/30/7/1640proteins from <i>Arca</i> speciesin silico analysisbioactive peptidesanticancer |
| spellingShingle | Jixu Wu Xiuhua Zhang Yuting Jin Man Zhang Rongmin Yu Liyan Song Fei Liu Jianhua Zhu Investigation of Anticancer Peptides Derived from <i>Arca</i> Species Using In Silico Analysis Molecules proteins from <i>Arca</i> species in silico analysis bioactive peptides anticancer |
| title | Investigation of Anticancer Peptides Derived from <i>Arca</i> Species Using In Silico Analysis |
| title_full | Investigation of Anticancer Peptides Derived from <i>Arca</i> Species Using In Silico Analysis |
| title_fullStr | Investigation of Anticancer Peptides Derived from <i>Arca</i> Species Using In Silico Analysis |
| title_full_unstemmed | Investigation of Anticancer Peptides Derived from <i>Arca</i> Species Using In Silico Analysis |
| title_short | Investigation of Anticancer Peptides Derived from <i>Arca</i> Species Using In Silico Analysis |
| title_sort | investigation of anticancer peptides derived from i arca i species using in silico analysis |
| topic | proteins from <i>Arca</i> species in silico analysis bioactive peptides anticancer |
| url | https://www.mdpi.com/1420-3049/30/7/1640 |
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