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使用 EM-Fold 对冷冻电镜密度图进行从头蛋白质建模。

Ab initio protein modeling into CryoEM density maps using EM-Fold.

机构信息

Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37212, USA.

出版信息

Biopolymers. 2012 Sep;97(9):669-77. doi: 10.1002/bip.22027. Epub 2012 Feb 3.

DOI:10.1002/bip.22027
PMID:22302372
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3375387/
Abstract

EM-Fold was used to build models for nine proteins in the maps of GroEL (7.7 Å resolution) and ribosome (6.4 Å resolution) in the ab initio modeling category of the 2010 cryo-electron microscopy modeling challenge. EM-Fold assembles predicted secondary structure elements (SSEs) into regions of the density map that were identified to correspond to either α-helices or β-strands. The assembly uses a Monte Carlo algorithm where loop closure, density-SSE length agreement, and strength of connecting density between SSEs are evaluated. Top-scoring models are refined by translating, rotating, and bending SSEs to yield better agreement with the density map. EM-Fold produces models that contain backbone atoms within SSEs only. The RMSD values of the models with respect to native range from 2.4 to 3.5 Å for six of the nine proteins. EM-Fold failed to predict the correct topology in three cases. Subsequently, Rosetta was used to build loops and side chains for the very best scoring models after EM-Fold refinement. The refinement within Rosetta's force field is driven by a density agreement score that calculates a cross-correlation between a density map simulated from the model and the experimental density map. All-atom RMSDs as low as 3.4 Å are achieved in favorable cases. Values above 10.0 Å are observed for two proteins with low overall content of secondary structure and hence particularly complex loop modeling problems. RMSDs over residues in secondary structure elements range from 2.5 to 4.8 Å.

摘要

EM-Fold 被用于构建 2010 年低温电子显微镜建模挑战赛的从头建模类别中 GroEL(7.7Å 分辨率)和核糖体(6.4Å 分辨率)图谱中 9 种蛋白质的模型。EM-Fold 将预测的二级结构元件(SSE)组装到密度图中被识别为对应于α-螺旋或β-折叠的区域。组装使用蒙特卡罗算法,其中评估环闭合、密度-SSE 长度一致性以及 SSE 之间连接密度的强度。得分最高的模型通过平移、旋转和弯曲 SSE 进行细化,以更好地与密度图一致。EM-Fold 仅生成包含 SSE 中骨架原子的模型。九个蛋白质中的六个蛋白质的模型与天然结构的 RMSD 值范围为 2.4 至 3.5Å。在三种情况下,EM-Fold 未能预测正确的拓扑结构。随后,在 EM-Fold 细化之后,Rosetta 用于构建得分最高的模型的环和侧链。Rosetta 力场中的细化由密度一致性得分驱动,该得分计算模型模拟的密度图与实验密度图之间的互相关。在有利情况下可以达到低至 3.4Å 的全原子 RMSD 值。对于两种整体二级结构含量较低且因此具有特别复杂的环建模问题的蛋白质,观察到的 RMSD 值高于 10.0Å。二级结构元件中的残基 RMSD 值范围为 2.5 至 4.8Å。

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2
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Science. 2010 Aug 27;329(5995):1038-43. doi: 10.1126/science.1187433.
3
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J Chem Phys. 2020 Dec 28;153(24):240901. doi: 10.1063/5.0026025.
4
Simulation-Based Methods for Model Building and Refinement in Cryoelectron Microscopy.用于冷冻电子显微镜中模型构建与优化的基于模拟的方法。
J Chem Inf Model. 2020 May 26;60(5):2470-2483. doi: 10.1021/acs.jcim.0c00087. Epub 2020 Mar 31.
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