Identification of a Selective YTHDF1 Inhibitor Targeting the m6A Recognition Domain for Breast Cancer

鉴定(生物学) 乳腺癌 癌症 领域(数学分析) 癌症研究 医学 计算生物学 内科学 生物 数学 植物 数学分析
作者
Yongya Wu,Guotai Feng,Shuai Wen,Xiao Yang,Xiaoli Pan,Chunyan Zhu,Aoxue Wang,Qiu Sun,Guan Wang,Liang Ouyang
出处
期刊:Angewandte Chemie [Wiley]
卷期号:64 (41): e202509316-e202509316 被引量:2
标识
DOI:10.1002/anie.202509316
摘要

As a key N6-methyladenosine (m6A) reader, YTH domain-containing family protein 1 (YTHDF1) promotes protein synthesis by recognizing m6A-modified mRNA, and its abnormal expression is closely related to breast cancer (BC) progression. To date, the scarce reported YTHDF1 inhibitors suffer from poor selectivity and limited potency, primarily due to the high homology of the YTH domain within the YTHDF family, which poses significant challenges for the discovery of subtype-selective inhibitors. Here, we report SKLB-Y13, the first small-molecule inhibitor achieving exclusive targeting of the YTHDF1 m6A-binding pocket (IC50 = 0.76 µM), via structural optimization of a novel 4,5,6,7-tetrahydrothieno[2,3-c]pyridine scaffold. Uniquely, SKLB-Y13 interacts with YTHDF1-specific residues Tyr397 and Trp470, as confirmed by site-directed mutagenesis, and demonstrates improved selectivity for YTHDF1 over YTH family proteins. Cellular and in vivo studies reveal that SKLB-Y13 disrupts YTHDF1-PRPF6 mRNA interaction in an m6A-dependent manner, thereby impairing the translation of PRPF6 and inhibiting BC proliferation while promoting apoptosis. Chemical proteomics profiling confirms its good target specificity, while pharmacokinetic analysis shows favorable in vivo properties. This study introduces the first selective YTHDF1 inhibitor, serving as a novel chemical probe to elucidate m6A-dependent oncogenesis and a promising starting point for developing precision therapies against YTHDF1-overexpressing BC.
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
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
theThreeMagi完成签到,获得积分10
1秒前
1秒前
11发布了新的文献求助10
1秒前
热情无心完成签到,获得积分10
2秒前
科研通AI6.2应助sdj采纳,获得10
3秒前
Hello应助震动的白秋采纳,获得10
3秒前
济川佃农发布了新的文献求助10
5秒前
6秒前
mofadaoshi发布了新的文献求助10
7秒前
10秒前
碧蓝邪欢完成签到,获得积分10
10秒前
倩Q完成签到,获得积分10
11秒前
FCC完成签到 ,获得积分10
12秒前
古月方源完成签到 ,获得积分10
13秒前
14秒前
zfk发布了新的文献求助10
14秒前
今后应助长情胡萝卜采纳,获得10
15秒前
yuyu发布了新的文献求助10
15秒前
wanci应助5433采纳,获得10
17秒前
18秒前
旧雪映月发布了新的文献求助10
18秒前
19秒前
19秒前
PhD_HanWu完成签到,获得积分10
22秒前
甜美冰旋发布了新的文献求助10
22秒前
墨子梓墨完成签到,获得积分10
22秒前
chaning发布了新的文献求助10
23秒前
橘子发布了新的文献求助10
24秒前
orixero应助三木子采纳,获得10
25秒前
25秒前
羊肉沫完成签到,获得积分10
25秒前
25秒前
26秒前
27秒前
27秒前
善良静竹完成签到 ,获得积分10
28秒前
Ava应助旧雪映月采纳,获得10
28秒前
羊肉沫发布了新的文献求助10
29秒前
汪洋完成签到,获得积分10
30秒前
科研通AI6.4应助黑猫采纳,获得10
31秒前
高分求助中
Principles of Economics, 11th Edition 10000
Prescott's Microbiology: 2026 Release ISE 10000
University Physics with Modern Physics, 16th edition 10000
Cronologia da história de Macau 5000
Environmental Leverage in Times of Climate Crisis: Product Standards, Carbon Border Measures and Preferential Trade Agreements 1000
Interactions of Vowel Quality and Prosody in East Slavic 1000
Matrix Methods in Data Mining and Pattern Recognition 510
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7158701
求助须知:如何正确求助?哪些是违规求助? 8802752
关于积分的说明 18602124
捐赠科研通 6761299
什么是DOI,文献DOI怎么找? 3162531
关于科研通互助平台的介绍 2298158
邀请新用户注册赠送积分活动 2137145