Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure Topologies.
Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure Topologies.
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DOI：
10.3390/molecules26227049
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发表时间：
2021-11-22
期刊：
影响因子：
--
通讯作者：
He J
中科院分区：
文献类型：
--
作者：
Alshammari M;He J
虽然原子结构已被确定直接从低温EM密度图与高分辨率，目前的结构确定方法的中等分辨率（5至10微米）的低温EM地图是有限的结构模板的可用性。二级结构迹线是从蛋白质的α-螺旋和β-链的冷冻-EM密度图检测到的线。二级结构的拓扑定义了三维空间中一组序列片段和一组二级结构迹线之间的映射。为了提高二级结构拓扑结构排序的准确性，我们探索了一种方法，该方法结合了三种信息来源：1D中的一组序列片段，2D中的一组氨基酸接触对，以及3D中二级结构水平的一组迹线。十四个案例的测试表明，预测的二级结构的准确性是至关重要的拓扑结构推导。如果二级结构预测相当准确，则使用显著的长程接触对对于丰富具有大量二级结构的蛋白质的最大匹配拓扑的秩是最有效的。据观察，在这种方法中，富集取决于初始拓扑候选者的质量。我们提供了各种情况下的详细分析，以显示结合三种信息来源时的潜力和挑战。
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. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set of sequence segments in 1D, a set of amino acid contact pairs in 2D, and a set of traces in 3D at the secondary structure level. A test of fourteen cases shows that the accuracy of predicted secondary structures is critical for deriving topologies. The use of significant long-range contact pairs is most effective at enriching the rank of the maximum-match topology for proteins with a large number of secondary structures, if the secondary structure prediction is fairly accurate. It was observed that the enrichment depends on the quality of initial topology candidates in this approach. We provide detailed analysis in various cases to show the potential and challenge when combining three sources of information.
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DOI：
10.1093/bioinformatics/btx781
发表时间：
2018-05-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Adhikari B;Hou J;Cheng J
通讯作者：
Cheng J
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：
2.9
作者：
Chan, Kwok-Yan;Trabuco, Leonardo G.;Schreiner, Eduard;Schulten, Klaus
通讯作者：
Schulten, Klaus
影响因子：
5.6
作者：
Jiang, W;Baker, ML;Chiu, W
通讯作者：
Chiu, W
影响因子：
5.8
作者：
Cuff, JA;Clamp, ME;Barton, GJ
通讯作者：
Barton, GJ