CryoFold: determining protein structures and data-guided ensembles from cryo-EM density maps.
CryoFold: determining protein structures and data-guided ensembles from cryo-EM density maps.
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CryoFold：从cryo-EM密度图确定蛋白质结构和数据引导的集合。
DOI：
10.1016/j.matt.2021.09.004
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发表时间：
2021-10-06
期刊：
影响因子：
18.9
通讯作者：
Singharoy, Abhishek
中科院分区：
文献类型：
--
作者：
Shekhar, Mrinal;Terashi, Genki;Gupta, Chitrak;Sarkar, Daipayan;Debussche, Gaspard;Sisco, Nicholas J.;Nguyen, Jonathan;Mondal, Arup;Vant, John;Fromme, Petra;Van Horn, Wade D.;Tajkhorshid, Emad;Kihara, Daisuke;Dill, Ken;Perez, Alberto;Singharoy, Abhishek
低温电子显微镜(EM)需要分子建模来从数据中提炼结构细节。系综模型得到了低自由能的分子结构，但计算成本很高，而且仅限于解析不能用冷冻-EM解析的小蛋白质。在这里，我们介绍了CryoFold-一种分子动力学模拟管道，通过将3-5？分辨率的不同稀疏性的密度数据与蛋白质折叠的粗粒度拓扑知识相结合，直接从序列确定蛋白质结构的系综。我们提供了六个例子，展示了它对72到2000个残基的折叠蛋白的广泛适用性，包括大的膜和多结构域系统，以及两个EMDB比赛的结果。在来自单一状态的数据的推动下，CryoFold发现了常见低能模型的集合以及罕见的低概率结构，这些结构捕获了受密度图约束的蛋白质的平衡分布。许多这些传统方法看不到的构象，经过实验验证，在功能上是相关的。我们得出了一组使用Python图形用户界面控制的数据引导蛋白质折叠的最佳实践。
Cryo-electron microscopy (EM) requires molecular modeling to refine structural details from data. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins that cannot be resolved by cryo-EM. Here, we introduce CryoFold - a pipeline of molecular dynamics simulations that determines ensembles of protein structures directly from sequence by integrating density data of varying sparsity at 3–5 Å resolution with coarse-grained topological knowledge of the protein folds. We present six examples showing its broad applicability for folding proteins between 72 to 2000 residues, including large membrane and multi-domain systems, and results from two EMDB competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations, unseen by traditional methods, are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python GUI.
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影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
影响因子：
48
作者：
Barad BA;Echols N;Wang RY;Cheng Y;DiMaio F;Adams PD;Fraser JS
通讯作者：
Fraser JS
影响因子：
13.6
作者：
Bonomi M;Camilloni C;Cavalli A;Vendruscolo M
通讯作者：
Vendruscolo M
影响因子：
48
作者：
Kucukelbir, Alp;Sigworth, Fred J.;Tagare, Hemant D.
通讯作者：
Tagare, Hemant D.
影响因子：
5.5
作者：
Morrone, Joseph A.;Perez, Alberto;Dill, Ken A.
通讯作者：
Dill, Ken A.