Selection and identification of a specific peptide binding to ovarian cancer cells from a phage-displayed peptide library.
Molecular imaging diagnosis
Ovarian cancer
Phage displayed peptide library
Targeting peptide
Journal
Biotechnology letters
ISSN: 1573-6776
Titre abrégé: Biotechnol Lett
Pays: Netherlands
ID NLM: 8008051
Informations de publication
Date de publication:
Aug 2022
Aug 2022
Historique:
received:
09
11
2021
accepted:
03
05
2022
pubmed:
1
7
2022
medline:
10
8
2022
entrez:
30
6
2022
Statut:
ppublish
Résumé
Ovarian cancer is one of the most fatal gynecological malignancies. It is emergently needed to select a novel molecular fragment as a targeting element for the future development of molecular imaging diagnosis and targeting chemotherapy to ovarian cancer. After five rounds of biopanning, a total of 44 positive phage clones were selected from final phage displayed peptide library. Nine consensus sequences were found based on the assay of sequencing results, then one clone of each consensus group was characterized and identified further by immunofluorescence assay. The result showed the phage clone R20 presents best targeting capacity. Then we synthesized peptide (OSP2) clone R20 displayed, it was characterized with high specificity and sensitivity binding to human ovarian cancer by a tissue chip assay. The target of OSP2 was predicted and docked as human carbonic anhydrase XII (CA12), an important protein usually deregulated in cancer. Taken together, OSP2 and its target indicate a novel investigation way in future to develop novel agent or drug delivery formulation for molecular imaging diagnosis and targeting chemotherapy of ovarian cancer.
Identifiants
pubmed: 35771408
doi: 10.1007/s10529-022-03263-w
pii: 10.1007/s10529-022-03263-w
doi:
Substances chimiques
Peptide Library
0
Peptides
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
951-960Subventions
Organisme : Shaanxi Province Postdoctoral Science Foundation
ID : 2022JQ-219
Organisme : Guangxi Innovation-Driven Development Project
ID : Guike AA20302013
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature B.V.
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