Academic Journal
Discovering potential anti-skin-aging peptides in collagen: computer-assisted rapid screening and structure–activity relationships.
| Τίτλος: | Discovering potential anti-skin-aging peptides in collagen: computer-assisted rapid screening and structure–activity relationships. |
|---|---|
| Συγγραφείς: | Zhang, Ruihao, Li, Yang, Li, Yonghui, Zhang, Hui |
| Πηγή: | Collagen & Leather; 9/1/2025, Vol. 7 Issue 1, p1-20, 20p |
| Θεματικοί όροι: | Skin aging, Peptides, Enzyme inhibitors, Structure-activity relationships, Collagen, Data analysis, Dietary bioactive peptides, Machine learning |
| Περίληψη: | The application of peptides as inhibitors of skin aging is a promising area of research. Previous researches have predominantly focused on extracting anti-aging peptides from the collagen of specific animals, while large-scale rapid screening and analysis of the structure–activity relationships of these peptides have been scarcely reported. In the present investigation, we developed a machine learning model for screening potential anti-skin-aging peptides (PASAPs), achieving a Matthews correlation coefficient (MCC) of 0.927 ± 0.044 and balanced accuracy (BACC) of 0.963 ± 0.022. These metrics surpassed those of the existing PeptideRanker model, which is widely used in bioactive peptide studies. Based on in silico screening, we identified and synthesized six novel PASAPs derived from tilapia collagen: KKHVWFGE, NGTPGAMGPR, PGAAGLKGDR, DGAPGPKGDR, TGPVGMPGAR, and GAPGGAGGVGEPGR. In vitro assays revealed that all six peptides exhibited significant inhibitory activity against aging-related enzymes, with the most pronounced effects on elastase and collagenase. A comprehensive analysis of the C-terminal amino acid residues indicated that the presence of arginine (R) at the C-terminus notably enhanced peptide binding to aging-related enzymes. This enhancement was attributed to an increased number of hydrogen bonds and stronger chemical interactions, which augmented the aging-related enzyme inhibitory activity of the peptides. In summary, this study proposed an effective strategy for discovering PASAPs from collagen and validated the machine learning model through experimental evidence. Structure–activity relationship insights can guide the synthesis of bioactive peptides and the selection of proteases for bioactive peptide production. [ABSTRACT FROM AUTHOR] |
| Copyright of Collagen & Leather is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1186/s42825-025-00215-8 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Header | DbId: edb DbLabel: Complementary Index An: 187671978 RelevancyScore: 1023 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1023.08752441406 |
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| Items | – Name: Title Label: Title Group: Ti Data: Discovering potential anti-skin-aging peptides in collagen: computer-assisted rapid screening and structure–activity relationships. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Ruihao%22">Zhang, Ruihao</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Yang%22">Li, Yang</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Yonghui%22">Li, Yonghui</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Hui%22">Zhang, Hui</searchLink> – Name: TitleSource Label: Source Group: Src Data: Collagen & Leather; 9/1/2025, Vol. 7 Issue 1, p1-20, 20p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Skin+aging%22">Skin aging</searchLink><br /><searchLink fieldCode="DE" term="%22Peptides%22">Peptides</searchLink><br /><searchLink fieldCode="DE" term="%22Enzyme+inhibitors%22">Enzyme inhibitors</searchLink><br /><searchLink fieldCode="DE" term="%22Structure-activity+relationships%22">Structure-activity relationships</searchLink><br /><searchLink fieldCode="DE" term="%22Collagen%22">Collagen</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Dietary+bioactive+peptides%22">Dietary bioactive peptides</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The application of peptides as inhibitors of skin aging is a promising area of research. Previous researches have predominantly focused on extracting anti-aging peptides from the collagen of specific animals, while large-scale rapid screening and analysis of the structure–activity relationships of these peptides have been scarcely reported. In the present investigation, we developed a machine learning model for screening potential anti-skin-aging peptides (PASAPs), achieving a Matthews correlation coefficient (MCC) of 0.927 ± 0.044 and balanced accuracy (BACC) of 0.963 ± 0.022. These metrics surpassed those of the existing PeptideRanker model, which is widely used in bioactive peptide studies. Based on in silico screening, we identified and synthesized six novel PASAPs derived from tilapia collagen: KKHVWFGE, NGTPGAMGPR, PGAAGLKGDR, DGAPGPKGDR, TGPVGMPGAR, and GAPGGAGGVGEPGR. In vitro assays revealed that all six peptides exhibited significant inhibitory activity against aging-related enzymes, with the most pronounced effects on elastase and collagenase. A comprehensive analysis of the C-terminal amino acid residues indicated that the presence of arginine (R) at the C-terminus notably enhanced peptide binding to aging-related enzymes. This enhancement was attributed to an increased number of hydrogen bonds and stronger chemical interactions, which augmented the aging-related enzyme inhibitory activity of the peptides. In summary, this study proposed an effective strategy for discovering PASAPs from collagen and validated the machine learning model through experimental evidence. Structure–activity relationship insights can guide the synthesis of bioactive peptides and the selection of proteases for bioactive peptide production. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Collagen & Leather is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1186/s42825-025-00215-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1 Subjects: – SubjectFull: Skin aging Type: general – SubjectFull: Peptides Type: general – SubjectFull: Enzyme inhibitors Type: general – SubjectFull: Structure-activity relationships Type: general – SubjectFull: Collagen Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Dietary bioactive peptides Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Discovering potential anti-skin-aging peptides in collagen: computer-assisted rapid screening and structure–activity relationships. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Ruihao – PersonEntity: Name: NameFull: Li, Yang – PersonEntity: Name: NameFull: Li, Yonghui – PersonEntity: Name: NameFull: Zhang, Hui IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: 9/1/2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 27316998 Numbering: – Type: volume Value: 7 – Type: issue Value: 1 Titles: – TitleFull: Collagen & Leather Type: main |
| ResultId | 1 |