Scalable emulation of protein equilibrium ensembles with generative deep learning

仿真 可扩展性 生成语法 计算机科学 人工智能 深度学习 生成模型 机器学习 心理学 社会心理学 数据库
作者
Sarah Lewis,Tim Hempel,José Jiménez-Luna,Michael Gastegger,Yu Xie,Andrew Y. K. Foong,Víctor García Satorras,Osama Abdin,Bastiaan S. Veeling,Iryna Zaporozhets,Yaoyi Chen,Soojung Yang,Arne Schneuing,Jigyasa Nigam,Federico Barbero,Vincent Stimper,Andrew M. Campbell,Jason Yim,Marten Lienen,Yu Shi
标识
DOI:10.1101/2024.12.05.626885
摘要

Following the sequence and structure revolutions, predicting the dynamical mechanisms of proteins that implement biological function remains an outstanding scientific challenge. Several experimental techniques and molecular dynamics (MD) simulations can, in principle, determine conformational states, binding configurations and their probabilities, but suffer from low throughput. Here we develop a Biomolecular Emulator (BioEmu), a generative deep learning system that can generate thousands of statistically independent samples from the protein structure ensemble per hour on a single graphical processing unit. By leveraging novel training methods and vast data of protein structures, over 200 milliseconds of MD simulation, and experimental protein stabilities, BioEmu's protein ensembles represent equilibrium in a range of challenging and practically relevant metrics. Qualitatively, BioEmu samples many functionally relevant conformational changes, ranging from formation of cryptic pockets, over unfolding of specific protein regions, to large-scale domain rearrangements. Quantitatively, BioEmu samples protein conformations with relative free energy errors around 1 kcal/mol, as validated against millisecond-timescale MD simulation and experimentally-measured protein stabilities. By simultaneously emulating structural ensembles and thermodynamic properties, BioEmu reveals mechanistic insights, such as the causes for fold destabilization of mutants, and can efficiently provide experimentally-testable hypotheses.
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
寒冷的发箍完成签到,获得积分10
1秒前
1秒前
1秒前
2秒前
wu完成签到,获得积分10
2秒前
orixero应助张大英采纳,获得10
2秒前
4秒前
heli发布了新的文献求助10
4秒前
5秒前
猪猪hero发布了新的文献求助10
5秒前
打打应助哈哈鹿采纳,获得10
6秒前
skye完成签到,获得积分10
6秒前
CipherSage应助义气如萱采纳,获得10
6秒前
zty发布了新的文献求助30
6秒前
SS发布了新的文献求助10
6秒前
戴岱发布了新的文献求助10
8秒前
10秒前
量子星尘发布了新的文献求助10
12秒前
13秒前
张大英发布了新的文献求助10
15秒前
马er完成签到,获得积分20
15秒前
奮斗完成签到,获得积分10
15秒前
戴岱完成签到,获得积分10
16秒前
哈哈鹿发布了新的文献求助10
17秒前
17秒前
黎明森完成签到,获得积分10
17秒前
Monica应助朴素若枫采纳,获得30
18秒前
19秒前
科研通AI5应助科研通管家采纳,获得10
19秒前
SYLH应助科研通管家采纳,获得30
19秒前
19秒前
czh应助科研通管家采纳,获得10
19秒前
科研通AI2S应助科研通管家采纳,获得10
19秒前
19秒前
今后应助科研通管家采纳,获得10
19秒前
顾矜应助科研通管家采纳,获得10
19秒前
19秒前
丘比特应助科研通管家采纳,获得10
19秒前
赘婿应助科研通管家采纳,获得10
19秒前
19秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
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 3989242
求助须知:如何正确求助?哪些是违规求助? 3531393
关于积分的说明 11253753
捐赠科研通 3270010
什么是DOI,文献DOI怎么找? 1804868
邀请新用户注册赠送积分活动 882084
科研通“疑难数据库(出版商)”最低求助积分说明 809136