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结合冷冻电镜密度图和残基接触进行蛋白质结构预测——一个案例研究。

Combine Cryo-EM Density Map and Residue Contact for Protein Structure Prediction - A Case Study.

作者信息

Alshammari Maytha, He Jing

机构信息

Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.

出版信息

ACM BCB. 2020 Sep;2020. doi: 10.1145/3388440.3414708.

DOI:10.1145/3388440.3414708
PMID:35838376
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9279007/
Abstract

Cryo-electron microscopy is a major structure determination technique for large molecular machines and membrane-associated complexes. Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. When combined with secondary structure sequence segments predicted from a protein sequence, it is possible to generate a set of likely topologies of α-traces and β-sheet traces. A topology describes the overall folding relationship among secondary structures; it is a critical piece of information for deriving the corresponding atomic structure. We propose a method for protein structure prediction that combines three sources of information: the secondary structure traces detected from the cryo-EM density map, predicted secondary structure sequence segments, and amino acid contact pairs predicted using MULTICOM. A case study shows that using amino acid contact prediction from MULTICOM improves the ranking of the true topology. Our observations convey that using a small set of highly voted secondary structure contact pairs enhances the ranking in all experiments conducted for this case.

摘要

冷冻电子显微镜是用于确定大型分子机器和膜相关复合物结构的主要技术。尽管已直接从具有高分辨率的冷冻电镜密度图确定了原子结构,但当前用于中等分辨率(5至10埃)冷冻电镜图的结构确定方法受到结构模板可用性的限制。二级结构迹线是从蛋白质的α螺旋和β链的冷冻电镜密度图中检测到的线条。当与从蛋白质序列预测的二级结构序列片段相结合时,就有可能生成一组可能的α迹线和β片层迹线拓扑结构。拓扑结构描述了二级结构之间的整体折叠关系;它是推导相应原子结构的关键信息。我们提出了一种蛋白质结构预测方法,该方法结合了三种信息来源:从冷冻电镜密度图中检测到的二级结构迹线、预测的二级结构序列片段以及使用MULTICOM预测的氨基酸接触对。一个案例研究表明,使用来自MULTICOM的氨基酸接触预测可以提高真实拓扑结构的排名。我们的观察结果表明,在针对此案例进行的所有实验中,使用一小部分高投票率的二级结构接触对可以提高排名。

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本文引用的文献

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Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning.利用深度学习在中等分辨率冷冻电镜图谱中检测蛋白质二级结构。
Nat Methods. 2019 Sep;16(9):911-917. doi: 10.1038/s41592-019-0500-1. Epub 2019 Jul 29.
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Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks.通过将残差二维双向长短期记忆与卷积神经网络相结合,准确预测蛋白质接触图。
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Deep Convolutional Neural Networks for Detecting Secondary Structures in Protein Density Maps from Cryo-Electron Microscopy.
用于检测冷冻电子显微镜蛋白质密度图中二级结构的深度卷积神经网络
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DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.DNCON2:使用两级深度卷积神经网络改进蛋白质接触预测。
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High-Resolution Cryo-EM Maps and Models: A Crystallographer's Perspective.高分辨率冷冻电镜图谱与模型:一位晶体学家的视角
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An Effective Computational Method Incorporating Multiple Secondary Structure Predictions in Topology Determination for Cryo-EM Images.一种在冷冻电镜图像拓扑结构确定中结合多个二级结构预测的有效计算方法。
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2.9 Å Resolution Cryo-EM 3D Reconstruction of Close-Packed Virus Particles.紧密堆积病毒颗粒的2.9埃分辨率冷冻电镜三维重建
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IEEE/ACM Trans Comput Biol Bioinform. 2014 Mar-Apr;11(2):419-30. doi: 10.1109/TCBB.2014.2302803.
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