Predicting protein crystallization propensity from protein sequence.
Predicting protein crystallization propensity from protein sequence.
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DOI：
10.1007/s10969-010-9080-0
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发表时间：
2010-03
期刊：
影响因子：
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通讯作者：
Joachimiak, Andrzej
中科院分区：
文献类型：
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作者：
Babnigg, Gyorgy;Joachimiak, Andrzej
由结构基因组学计划开发的高通量结构确定管道为数据挖掘提供了独特的机会。一个重要的问题是如何从一级序列中获得的蛋白质性质与蛋白质产生X射线质量晶体（结晶性）和3D X射线结构的倾向相关。计算了超过1，300种表达良好但不溶的蛋白质的一组蛋白质特性，以及约720种独特的蛋白质，这些蛋白质导致X射线结构。分析了蛋白质的等电点和总平均亲水性（GRAVY）与蛋白质靶的全长和结构域构建体的结晶性的相关性。在第二步中，添加并评估了可以从蛋白质序列计算的几个附加特性。使用统计分析，我们已经确定了一组与蛋白质的倾向结晶的属性，并实现了支持向量机（SVM）分类器基于这些。我们已经创建了应用程序来分析和提供查询序列的最佳边界信息，并可视化数据。这些工具可通过网站http://bioinformatics.anl.gov/cgi-bin/tools/pdpredictor获得。
The high-throughput structure determination pipelines developed by structural genomics programs offer a unique opportunity for data mining. One important question is how protein properties derived from a primary sequence correlate with the protein’s propensity to yield X-ray quality crystals (crystallizability) and 3D X-ray structures. A set of protein properties were computed for over 1,300 proteins that expressed well but were insoluble, and for ~720 unique proteins that resulted in X-ray structures. The correlation of the protein’s iso-electric point and grand average hydropathy (GRAVY) with crystallizability was analyzed for full length and domain constructs of protein targets. In a second step, several additional properties that can be calculated from the protein sequence were added and evaluated. Using statistical analyses we have identified a set of the attributes correlating with a protein’s propensity to crystallize and implemented a Support Vector Machine (SVM) classifier based on these. We have created applications to analyze and provide optimal boundary information for query sequences and to visualize the data. These tools are available via the web site http://bioinformatics.anl.gov/cgi-bin/tools/pdpredictor.