当前位置: X-MOL 学术Science › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Accurate prediction of protein structures and interactions using a three-track neural network
Science ( IF 56.9 ) Pub Date : 2021-08-20 , DOI: 10.1126/science.abj8754
Minkyung Baek 1, 2 , Frank DiMaio 1, 2 , Ivan Anishchenko 1, 2 , Justas Dauparas 1, 2 , Sergey Ovchinnikov 3, 4 , Gyu Rie Lee 1, 2 , Jue Wang 1, 2 , Qian Cong 5, 6 , Lisa N Kinch 7 , R Dustin Schaeffer 6 , Claudia Millán 8 , Hahnbeom Park 1, 2 , Carson Adams 1, 2 , Caleb R Glassman 9, 10, 11 , Andy DeGiovanni 12 , Jose H Pereira 12 , Andria V Rodrigues 12 , Alberdina A van Dijk 13 , Ana C Ebrecht 13 , Diederik J Opperman 14 , Theo Sagmeister 15 , Christoph Buhlheller 15, 16 , Tea Pavkov-Keller 15, 17 , Manoj K Rathinaswamy 18 , Udit Dalwadi 19 , Calvin K Yip 19 , John E Burke 18 , K Christopher Garcia 9, 10, 11, 20 , Nick V Grishin 6, 7, 21 , Paul D Adams 12, 22 , Randy J Read 8 , David Baker 1, 2, 23
Affiliation  

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo–electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.



中文翻译:

使用三轨神经网络准确预测蛋白质结构和相互作用

DeepMind 在最近的第 14 届结构预测批判评估 (CASP14) 会议上提出了非常准确的预测。我们探索了融合相关思想的网络架构,并通过三轨网络获得了最佳性能,其中一维(1D)序列级别、2D距离图级别和3D坐标级别的信息依次转换和集成。三轨网络产生的结构预测精度接近 DeepMind 在 CASP14 中的精度,能够快速解决具有挑战性的 X 射线晶体学和冷冻电子显微镜结构建模问题,并提供对当前未知结构的蛋白质功能的见解。该网络还能够仅根据序列信息快速生成准确的蛋白质-蛋白质复合物模型,从而缩短了需要对单个亚基建模然后进行对接的传统方法。我们将该方法提供给科学界以加速生物学研究。

更新日期:2021-08-20
down
wechat
bug