In silico Analysis of Anti-cervical Cancer Drug Off-Target Effects on Diverse Protein Isoforms for Enhanced Therapeutic Strategies

Azhar Iqbal,Faisal Ali, Shanza Choudhary, Adiba Qayyum,Fiza Arshad, Sara Ashraf, Moawaz Aziz, Asad Ullah Shakil, Momina Hussain,Muhammad Sajid,Sheikh Arslan Sehgal

Innovative Biosystems and Bioengineering(2023)

引用 0|浏览2
暂无评分
摘要
Background. Cervical cancer is a serious medical condition that affects hundreds of thousands of individuals worldwide annually. The selection and analysis of suitable gene targets in the early stages of drug design are crucial for combating this disease. However, overlooking the presence of various protein isoforms may result in unwanted therapeutic or harmful side effects. Objective. This study aimed to provide a computational analysis of the interactions between cervical cancer drugs and their targets, influenced by alternative splicing. Methods. Using open-access databases, we targeted 45 FDA-approved cervical cancer drugs that target various genes having more than two distinct protein-coding isoforms. To check the conservation of binding pocket in isoforms of the genes, multiple sequence analysis was performed. To better understand the associations between proteins and FDA-approved drugs at the isoform level, we conducted molecular docking analysis. Results. The study reveals that many drugs lack potential targets at the isoform level. Further examination of various isoforms of the same gene revealed distinct ligand-binding pocket configurations, including differences in size, shape, electrostatic characteristics, and structure. Conclusions. This study highlights the potential risks of focusing solely on the canonical isoform, and ignoring the impact of cervical cancer drugs on- and off-target effects at the isoform level. These findings emphasize the importance of considering interactions between drugs and their targets at the isoform level to promote effective treatment outcomes.
更多
查看译文
关键词
cervical cancer,isoforms,molecular docking,interaction analysis,bioinformatics approaches
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