Structural Probing, Screening and Structure-Based Drug Repositioning Insights into the Identification of Potential Cox-2 Inhibitors from Selective Coxibs

Interdisciplinary Sciences: Computational Life Sciences(2017)

引用 10|浏览0
暂无评分
摘要
The rate-limiting enzyme cyclooxygenase-2 (COX-2) is considered as an insightful prognostic target for non-small cell lung cancer (NSCLC) therapy. Now, administration and prolonged utilization of selective COX-2 inhibitors (COXIBs) towards moderating the NSCLC has been associated with different side effects. In the present study, we focused on the structure-based drug repositioning approaches for predicting therapeutic potential de novo candidates for human COX-2. Due to discrepancies in the eminence of x-ray diffraction structures, creates a big barrier in drug discovery approach. Hence, the adaptable COX-2 structure was investigated using multi-template modeling method. Next, a dataset of twenty-six celebrex-associated optimized scaffolds were screened from ZINC database. Comparative docking approaches were then utilized to identify five compounds as best binders to the active site of COX-2 structures and strongly agree with enormous experimental consequences. MD simulations of regarded protein–ligand complexes reveals that lead molecules were stabilized dynamically in inside the cyclooxygenase site by forming potential salt bridges with Tyr 348 , Tyr 385 and Ser 530 residues. These significant results revealed that, identified druggables could prevent the tyrosyl radicals and prostaglandin production that reduces NSCLC progression. Furthermore, pharmacokinetics assets of respected ligands were analyzed, which incorporates similarity ensemble approach, druglikeness and ADMET properties. Finally, the identified novel candidates could serve as COX-2 inhibitors for NSCLC therapy, and coxibs are the best choices for designing new scaffolds to treat cyclooxygenases regard disorders.
更多
查看译文
关键词
COX-2,Optimized modeling,Celebrex,Comparative docking,Molecular dynamics and GBIS simulations,ADME-Tox
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要