Integrated virtual screening and MD simulation approaches toward discovering potential inhibitors for targeting BRPF1 bromodomain in hepatocellular carcinoma.
J Mol Graph Model
; 126: 108642, 2024 01.
Article
in En
| MEDLINE
| ID: mdl-37797430
ABSTRACT
Hepatocellular carcinoma (HCC) is one of the most aggressive and life-threatening cancers. Although multiple treatment options are available, the prognosis of HCC patients is poor due to metastasis and drug resistance. Hence, discovering novel targets is essential for better therapeutic development for HCC. In this study, we used the cancer genome atlas (TCGA) dataset to analyze the expression of bromodomain-containing proteins in HCC, as bromodomains are emerging attractive therapeutic targets. Our analysis identified BRPF1 as the most highly upregulated gene in HCC among the 43 bromodomain-containing genes. Upregulation of BRPF1 was significantly associated with poorer patient survival. Therefore, targeting BRPF1 may be an approach for HCC treatment. Previously, several potential inhibitors of BRPF1 bromodomain have been discovered. However, due to the limited clinical success of the current inhibitors, we aim to search for new inhibitors with high affinity and specificity for the BRPF1 bromodomain. In this study, we utilized high-throughput virtual screening methods to screen synthetic and natural compound databases against the BRPF1 bromodomain. In addition, we used machine learning-based QSAR modeling to predict the IC50 values of the selected BRPF1 bromodomain inhibitors. Extensive MD simulations were used to calculate the binding free energies of BRPF1 bromodomain and inhibitor complexes. Using this approach, we identified four lead scaffolds with a similar or better binding affinity towards the BRPF1 bromodomain than the previously reported inhibitors. Overall, this study discovered some promising compounds that have the potential to act as potent BRPF1 bromodomain inhibitors.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Carcinoma, Hepatocellular
/
Liver Neoplasms
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Limits:
Humans
Language:
En
Journal:
J Mol Graph Model
Journal subject:
BIOLOGIA MOLECULAR
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: