RUPEE: Scalable protein structure search using run position encoded residue descriptors
RUPEE: Scalable protein structure search using run position encoded residue descriptors
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DOI：
10.1109/bibm.2017.8217627
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发表时间：
2017-11
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影响因子：
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通讯作者：
Ronald Ayoub;Yugyung Lee
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文献类型：
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作者：
Ronald Ayoub;Yugyung Lee
我们开发了一种快速、可扩展、纯几何结构搜索，将信息检索和大数据技术与扭转角序列编码的新颖方法相结合。在此过程中，我们引入了一种新的扭转角图，该图不会中断连续性，同时仍保持传统的扭转角范围，以帮助识别扭转角的可分离区域。随后，我们引入了一种新的启发式方法，称为运行位置编码，用于处理包含重复运行的字符序列中项目缺乏特异性的问题。将我们的结果与 CATH 结构扫描的输出进行比较，响应时间以秒为单位测量，而不是以分钟为单位，并且平均 RMSD 和 TM 分数更好。我们的方法是朝着可扩展至数百万个条目的蛋白质结构综合索引迈出的一步。代码和数据可在 https://github.com/rayoub/rupee 获取
We have developed a fast, scalable, and purely geometric structure search combining techniques from information retrieval and big data with a novel approach to encoding sequences of torsion angles. Along the way, we introduce a new torsion angle plot without breaks in continuity while still maintaining traditional torsion angle ranges, to assist in identifying separable regions of torsion angles. Subsequently, we introduce a new heuristic we call run position encoding, for handling the lack of specificity of items within character sequences containing runs of repeats. Comparing our results to the output of the CATH structural scan, response times are measured in seconds as opposed to minutes and average RMSDs and TM-scores are better. Our approach is a step towards a comprehensive indexing of protein structures scalable to millions of entries. Code and data are available at https://github.com/rayoub/rupee