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...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Molecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1420-3049/30/7/1640 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 1420-3049 |