Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates
Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates
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DOI：
10.1093/bioinformatics/btr350
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发表时间：
2011-08-01
期刊：
影响因子：
5.8
通讯作者：
Zhou, Yaoqi
中科院分区：
文献类型：
--
作者：
Yang, Yuedong;Faraggi, Eshel;Zhou, Yaoqi
动机：近年来，单一方法折叠识别服务器的发展落后于共识和多模板技术。然而，一个好的共识预测依赖于单个方法的准确性。本文报道了我们通过改变比对评分函数并结合SPINE-X技术来进一步改进称为SPARKS的单一方法折叠识别技术的努力，该技术改进了二级结构、主链扭转角和溶剂可及表面积的预测。新方法SPARKS-X用SALIGN基准测试比对精度，Lindahl和SCOP基准测试折叠识别，CASP 9盲试法进行结构预测。该方法相比，几个国家的最先进的技术，如HHPRED和BoostThreader。实验结果表明，SPARKS-X是最好的单一方法褶皱识别技术之一。我们进一步注意到，在模型构建中引入多个模板和改进可能会进一步改进SPARKS-X。
Motivation: In recent years, development of a single-method fold-recognition server lags behind consensus and multiple template techniques. However, a good consensus prediction relies on the accuracy of individual methods. This article reports our efforts to further improve a single-method fold recognition technique called SPARKS by changing the alignment scoring function and incorporating the SPINE-X techniques that make improved prediction of secondary structure, backbone torsion angle and solvent accessible surface area.Results: The new method called SPARKS-X was tested with the SALIGN benchmark for alignment accuracy, Lindahl and SCOP benchmarks for fold recognition, and CASP 9 blind test for structure prediction. The method is compared to several state-of-the-art techniques such as HHPRED and BoostThreader. Results show that SPARKS-X is one of the best single-method fold recognition techniques. We further note that incorporating multiple templates and refinement in model building will likely further improve SPARKS-X.