Current strategies and progress for targeting the “undruggable” transcription factors

计算生物学 转录因子 生物 药物发现 蛋白质-蛋白质相互作用 生物信息学 基因 遗传学
作者
Jingjing Zhuang,Qian Liu,Dalei Wu,Lu Tie
出处
期刊:Acta pharmacologica Sinica [Springer Nature]
卷期号:43 (10): 2474-2481 被引量:7
标识
DOI:10.1038/s41401-021-00852-9
摘要

Transcription factors (TFs) specifically bind to DNA, recruit cofactor proteins and modulate target gene expression, rendering them essential roles in the regulation of numerous biological processes. Meanwhile, mutated or dysregulated TFs are involved in a variety of human diseases. As multiple signaling pathways ultimately converge at TFs, targeting these TFs directly may prove to be more specific and cause fewer side effects, than targeting the upfront conventional targets in these pathways. All these features together endue TFs with great potential and high selectivity as therapeutic drug targets. However, TFs have been historically considered "undruggable", mainly due to their lack of structural information, especially about the appropriate ligand-binding sites and protein-protein interactions, leading to relatively limited choices in the TF-targeting drug design. In this review, we summarize the recent progress of TF-targeting drugs and highlight certain strategies used for targeting TFs, with a number of representative drugs that have been approved or in the clinical trials as examples. Various approaches in targeting TFs directly or indirectly have been developed. Common direct strategies include aiming at defined binding pockets, proteolysis-targeting chimaera (PROTAC), and mutant protein reactivation. In contrast, the indirect ones comprise inhibition of protein-protein interactions between TF and other proteins, blockade of TF expression, targeting the post-translational modifications, and targeting the TF-DNA interactions. With more comprehensive structural information about TFs revealed by the powerful cryo-electron microscopy technology and predicted by machine-learning algorithms, plus more efficient compound screening platforms and a deeper understanding of TF-disease relationships, the development of TF-targeting drugs will certainly be accelerated in the near future.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
悦耳的三毒完成签到 ,获得积分10
刚刚
宋呵呵完成签到,获得积分10
1秒前
1秒前
狄如波完成签到,获得积分10
1秒前
1秒前
科研通AI2S应助要减肥采纳,获得10
2秒前
2秒前
3秒前
4秒前
狄如波发布了新的文献求助30
5秒前
云雨完成签到 ,获得积分10
6秒前
蘑菇发布了新的文献求助10
8秒前
可乐发布了新的文献求助10
9秒前
现代的从蓉完成签到,获得积分10
10秒前
yangluyao发布了新的文献求助10
10秒前
一个晴天完成签到,获得积分10
12秒前
14秒前
航仔完成签到,获得积分10
16秒前
好的很不错完成签到,获得积分10
16秒前
19秒前
21秒前
21秒前
Orange应助Hcoojzk采纳,获得10
22秒前
WATeam完成签到,获得积分0
22秒前
23秒前
23秒前
23秒前
24秒前
slin_sjtu发布了新的文献求助10
24秒前
24秒前
24秒前
ww关注了科研通微信公众号
25秒前
shenmizhe发布了新的文献求助10
26秒前
28秒前
失眠晓霜发布了新的文献求助10
29秒前
30秒前
清脆糖豆发布了新的文献求助10
30秒前
jessicaw完成签到,获得积分10
30秒前
张二狗完成签到,获得积分10
30秒前
32秒前
高分求助中
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger Heßler, Claudia, Rud 1000
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 1000
Natural History of Mantodea 螳螂的自然史 1000
A Photographic Guide to Mantis of China 常见螳螂野外识别手册 800
Autoregulatory progressive resistance exercise: linear versus a velocity-based flexible model 500
Spatial Political Economy: Uneven Development and the Production of Nature in Chile 400
Research on managing groups and teams 300
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3329654
求助须知:如何正确求助?哪些是违规求助? 2959247
关于积分的说明 8594980
捐赠科研通 2637718
什么是DOI,文献DOI怎么找? 1443719
科研通“疑难数据库(出版商)”最低求助积分说明 668843
邀请新用户注册赠送积分活动 656278