AlphaDesign: A de novo protein design framework based on AlphaFold

蛋白质设计 计算生物学 蛋白质结构 蛋白质折叠 分子动力学 折叠(DSP实现) 蛋白质组 蛋白质工程 结构生物信息学 蛋白质结构预测 生物系统 计算机科学 化学 生物 生物信息学 计算化学 生物化学 工程类 电气工程
作者
Michael Jendrusch,Jan O. Korbel,S. Kashif Sadiq
标识
DOI:10.1101/2021.10.11.463937
摘要

De novo protein design is a longstanding fundamental goal of synthetic biology, but has been hindered by the difficulty in reliable prediction of accurate high-resolution protein structures from sequence. Recent advances in the accuracy of protein structure prediction methods, such as AlphaFold (AF), have facilitated proteome scale structural predictions of monomeric proteins. Here we develop AlphaDesign, a computational framework for de novo protein design that embeds AF as an oracle within an optimisable design process. Our framework enables rapid prediction of completely novel protein monomers starting from random sequences. These are shown to adopt a diverse array of folds within the known protein space. A recent and unexpected utility of AF to predict the structure of protein complexes, further allows our framework to design higher-order complexes. Subsequently a range of predictions are made for monomers, homodimers, heterodimers as well as higher-order homo-oligomers - trimers to hexamers. Our analyses also show potential for designing proteins that bind to a pre-specified target protein. Structural integrity of predicted structures is validated and confirmed by standard ab initio folding and structural analysis methods as well as more extensively by performing rigorous all-atom molecular dynamics simulations and analysing the corresponding structural flexibility, intramonomer and interfacial amino-acid contacts. These analyses demonstrate widespread maintenance of structural integrity and suggests that our framework allows for fairly accurate protein design. Strikingly, our approach also reveals the capacity of AF to predict proteins that switch conformation upon complex formation, such as involving switches from α -helices to β -sheets during amyloid filament formation. Correspondingly, when integrated into our design framework, our approach reveals de novo design of a subset of proteins that switch conformation between monomeric and oligomeric state.
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
PDF的下载单位、IP信息已删除 (2025-6-4)

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
量子星尘发布了新的文献求助30
1秒前
俏皮书白完成签到,获得积分10
2秒前
汉堡包应助zhang97采纳,获得10
3秒前
逸之狐完成签到,获得积分10
4秒前
鱼多多发布了新的文献求助10
4秒前
4秒前
5秒前
Paris完成签到 ,获得积分10
5秒前
5秒前
碧蓝白玉完成签到,获得积分10
6秒前
飞鱼完成签到,获得积分10
6秒前
6秒前
科研八戒完成签到,获得积分10
6秒前
6秒前
小四适发布了新的文献求助10
7秒前
学术智子发布了新的文献求助10
7秒前
田田田田完成签到,获得积分20
7秒前
8秒前
8秒前
NingJiao完成签到,获得积分20
8秒前
yarkye完成签到,获得积分10
8秒前
旺大财完成签到 ,获得积分10
9秒前
9秒前
9秒前
守护星星发布了新的文献求助10
9秒前
万能图书馆应助ihtw采纳,获得10
9秒前
10秒前
温婉发布了新的文献求助10
10秒前
和谐的忆之完成签到,获得积分10
10秒前
qinchuanniu发布了新的文献求助10
11秒前
打打应助SUKAAAA采纳,获得10
12秒前
key完成签到,获得积分10
13秒前
程瀚砚发布了新的文献求助10
13秒前
思源应助liuzengzhang666采纳,获得10
13秒前
852应助选民很头疼采纳,获得10
13秒前
凶凶完成签到,获得积分10
14秒前
NingJiao发布了新的文献求助10
14秒前
14秒前
14秒前
酷炫醉山发布了新的文献求助30
15秒前
高分求助中
The Mother of All Tableaux Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 2400
Ophthalmic Equipment Market by Devices(surgical: vitreorentinal,IOLs,OVDs,contact lens,RGP lens,backflush,diagnostic&monitoring:OCT,actorefractor,keratometer,tonometer,ophthalmoscpe,OVD), End User,Buying Criteria-Global Forecast to2029 2000
Optimal Transport: A Comprehensive Introduction to Modeling, Analysis, Simulation, Applications 800
Official Methods of Analysis of AOAC INTERNATIONAL 600
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 588
A new approach to the extrapolation of accelerated life test data 500
T/CIET 1202-2025 可吸收再生氧化纤维素止血材料 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 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 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3954228
求助须知:如何正确求助?哪些是违规求助? 3500273
关于积分的说明 11098748
捐赠科研通 3230782
什么是DOI,文献DOI怎么找? 1786143
邀请新用户注册赠送积分活动 869824
科研通“疑难数据库(出版商)”最低求助积分说明 801638