Identification of Anticancer Peptides from the Genome of Candida albicans: in Silico Screening, in Vitro and in Vivo Validations.
J Chem Inf Model
; 64(15): 6174-6189, 2024 Aug 12.
Article
in En
| MEDLINE
| ID: mdl-39008832
ABSTRACT
Anticancer peptides (ACPs) are promising future therapeutics, but their experimental discovery remains time-consuming and costly. To accelerate the discovery process, we propose a computational screening workflow to identify, filter, and prioritize peptide sequences based on predicted class probability, antitumor activity, and toxicity. The workflow was applied to identify novel ACPs with potent activity against colorectal cancer from the genome sequences of Candida albicans. As a result, four candidates were identified and validated in the HCT116 colon cancer cell line. Among them, PCa1 and PCa2 emerged as the most potent, displaying IC50 values of 3.75 and 56.06 µM, respectively, and demonstrating a 4-fold selectivity for cancer cells over normal cells. In the colon xenograft nude mice model, the administration of both peptides resulted in substantial inhibition of tumor growth without causing significant adverse effects. In conclusion, this work not only contributes a proven computational workflow for ACP discovery but also introduces two peptides, PCa1 and PCa2, as promising candidates poised for further development as targeted therapies for colon cancer. The method as a web service is available at https//app.cbbio.online/acpep/home and the source code at https//github.com/cartercheong/AcPEP_classification.git.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Peptides
/
Candida albicans
/
Antineoplastic Agents
Limits:
Animals
/
Humans
Language:
En
Journal:
J Chem Inf Model
Journal subject:
INFORMATICA MEDICA
/
QUIMICA
Year:
2024
Document type:
Article
Affiliation country:
China
Country of publication:
Estados Unidos