Importance of Three-Body Problems and Protein–Protein Interactions in Proteolysis-Targeting Chimera Modeling: Insights from Molecular Dynamics Simulations

嵌合体(遗传学) 蛋白质水解 分子动力学 计算生物学 化学 计算化学 生物 生物化学 基因
作者
Wenqing Li,Jiabin Zhang,Li Guo,Qiantao Wang
出处
期刊:Journal of Chemical Information and Modeling [American Chemical Society]
卷期号:62 (3): 523-532 被引量:39
标识
DOI:10.1021/acs.jcim.1c01150
摘要

Proteolysis-targeting chimeras (PROTACs) are a class of bifunctional molecules that can induce the ubiquitin degradation of its target protein by hijacking the E3 ligase to form a target protein-PROTAC-E3 ligase ternary complex. Its underlying principle has inspired the development of a wide range of protein degraders that are similar to or beyond PROTACs in recent years. The formation of the ternary complexes is the key to the success of PROTAC-induced protein degradation. Nevertheless, the lack of effective ternary complex modeling techniques has limited the application of computer-aided drug discovery tools to this emerging and fast developing new land in drug industry. Thus, in this study, we explored the application of the more physically sound molecular dynamics simulation and the molecular mechanics combined with the generalized Born and surface area continuum solvation (MM/GBSA) method to solve the underlying three-body problem in PROTAC modeling. We first verified the accuracy of our approach using a series of known Brd4 BD2 degraders. The calculated binding energy showed a good correlation with the experimental Kd values. The modeling of a unique property, namely, the α value, for PROTACs was also first and accurately performed to our best knowledge. The results also demonstrated the importance of PROTAC-induced protein–protein interactions in its modeling, either qualitatively or quantitatively. Finally, by standing on the success of earlier docking-based approaches, our protocol was also applied as a rescoring function in pose prediction. The results showed a notable improvement in reranking the initial poses generated from a modified Rosetta method, which was reportedly one of the best among a handful of PROTAC modeling approaches available in this field. We hope this work could provide a practical protocol and more insights to study the binding and the design of PROTACs and other protein degraders.

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
YiXianCoA完成签到 ,获得积分10
1秒前
1秒前
1秒前
qqq完成签到 ,获得积分20
1秒前
缓慢弼发布了新的文献求助10
1秒前
曹中明发布了新的文献求助30
1秒前
小曾发布了新的文献求助10
2秒前
2秒前
Hilda007应助LinCheng采纳,获得10
2秒前
平淡黑裤完成签到,获得积分20
3秒前
泡泡完成签到,获得积分10
3秒前
完美世界应助专注寻菱采纳,获得10
3秒前
YUYUYU完成签到,获得积分10
4秒前
江南刀王发布了新的文献求助10
4秒前
濛嘻嘻发布了新的文献求助10
4秒前
小夏发布了新的文献求助10
5秒前
5秒前
专注的雪完成签到 ,获得积分10
5秒前
白瑾发布了新的文献求助10
5秒前
无花果应助灵巧安青采纳,获得10
5秒前
稻草人完成签到 ,获得积分10
6秒前
我是老大应助高大笙采纳,获得10
6秒前
所所应助飘逸易文采纳,获得10
6秒前
坚强白凝完成签到,获得积分10
7秒前
科研通AI6应助刘逸飞采纳,获得30
7秒前
Jasper应助谦让谷菱采纳,获得10
7秒前
prove发布了新的文献求助10
7秒前
刻苦牛马完成签到 ,获得积分10
7秒前
lalala发布了新的文献求助10
7秒前
追寻的夏波应助永毅采纳,获得10
7秒前
年轻的背包完成签到,获得积分10
8秒前
无聊的可冥发布了新的文献求助100
8秒前
杨承武完成签到,获得积分10
8秒前
刘海清完成签到,获得积分10
9秒前
9秒前
研友_ndka5L发布了新的文献求助10
9秒前
10秒前
QQQ完成签到,获得积分10
10秒前
Shannon完成签到,获得积分10
10秒前
xinxin完成签到,获得积分10
10秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Basic And Clinical Science Course 2025-2026 3000
Encyclopedia of Agriculture and Food Systems Third Edition 2000
人脑智能与人工智能 1000
花の香りの秘密―遺伝子情報から機能性まで 800
Principles of Plasma Discharges and Materials Processing, 3rd Edition 400
Pharmacology for Chemists: Drug Discovery in Context 400
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5609460
求助须知:如何正确求助?哪些是违规求助? 4694074
关于积分的说明 14880935
捐赠科研通 4719643
什么是DOI,文献DOI怎么找? 2544750
邀请新用户注册赠送积分活动 1509658
关于科研通互助平台的介绍 1472950