泛素
重新调整用途
药物重新定位
药物开发
药品
Nexus(标准)
抗癌药
药理学
药物发现
生物
癌症研究
化学
生物化学
生态学
基因
计算机科学
嵌入式系统
作者
Saad Ali Alshehri,Abdulrhman Alsayari,Mohammad Abohassan,Mohammad Ali Abdullah Almoyad,Shadma Wahab
标识
DOI:10.1080/10799893.2025.2548246
摘要
Ubiquitin-specific peptidase 22 (USP22) has emerged as a promising target in cancer research because of its pivotal role in tumor progression, metastasis, and therapy resistance. USP22 is frequently overexpressed in multiple malignancies and facilitates essential cellular processes, including DNA repair, cell cycle regulation, and cancer stem cell (CSC) maintenance. The strength of these attributes makes it an attractive candidate for therapeutic intervention. Despite the advances in conventional cancer treatment, recurrent and resistant USP22-overexpressing tumors demand novel treatment strategies. Drug repurposing is a cost-effective and efficient approach to overcome this challenge by taking advantage of FDA-approved drugs, wherein the safety profiles of used drugs are known for different therapeutic uses. To identify potential repurposed USP22 inhibitors, this study used an integrated computational workflow consisting of molecular docking and molecular dynamics (MD) simulations. The virtual screening of FDA-approved compounds from DrugBank revealed that Ergotamine showed high binding affinities and specific interactions with the USP22 binding pocket. Pharmacokinetic evaluations demonstrate that Ergotamine has an appropriate drug profile and biological activities in anticancer interventions. The stability and conformational dynamics of the USP22-Ergotamine complex were investigated by all-atom MD simulations for 300 ns. The robustness of these interactions was verified in these simulations and MM/PBSA, and insights into the molecular mechanisms that underlie their ability to inhibit USP22 were provided. Our findings reveal a potentially promising role for Ergotamine as a repurposed USP22 inhibitor that would be worthwhile to validate experimentally for therapeutic development against cancer.
科研通智能强力驱动
Strongly Powered by AbleSci AI