A critical evaluation of in silico methods for detection of membrane protein intrinsic disorder.
A critical evaluation of in silico methods for detection of membrane protein intrinsic disorder.
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DOI：
10.1016/j.bpj.2014.02.025
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发表时间：
2014-04
影响因子：
3.4
通讯作者：
Edward E. Pryor;M. Wiener
中科院分区：
文献类型：
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作者：
Edward E. Pryor;M. Wiener
蛋白质中固有的无序区域具有重要的生物学作用，包括转录调节、分子识别和为翻译后修饰提供位点。在可溶性蛋白和膜蛋白的三维结晶过程中，为了获得具有足够长程有序结构的晶体，往往需要对无序区进行识别和去除。无序区域可以通过实验识别，使用有限的蛋白质分解和质谱学等技术，或通过计算，使用无序预测程序，其中许多是可用的。虽然这些程序使用不同的方法从蛋白质的初级序列预测无序性，但它们都是利用来自可溶蛋白质结构的信息开发的。因此，它们应用于完整膜蛋白时的性能和准确性仍然是一个悬而未决的问题。我们评估了13个无序预测程序在一个包含343个膜蛋白的数据集上的性能，以及在只包含α-螺旋或β-Barrel蛋白的子数据集上的性能。这些程序使用多个指标进行排名，包括专门为膜蛋白创建的指标。对这些数据的分析表明，准确预测膜蛋白无序区域的程序和执行情况较差的程序之间存在明显的区别，并允许将无序预测稳健地整合到我们的PSI：生物膜蛋白结构基因组学管道中。
Intrinsically disordered regions in proteins possess important biological roles including transcriptional regulation, molecular recognition, and provision of sites for posttranslational modification. In three-dimensional crystallization of both soluble and membrane proteins, identification and removal of disordered regions is often necessary for obtaining crystals possessing sufficient long-range order for structure determination. Disordered regions can be identified experimentally, with techniques such as limited proteolysis coupled with mass spectrometry, or computationally, by using disorder prediction programs, of which many are available. Although these programs use various methods to predict disorder from a protein's primary sequence, they all were developed using information derived from soluble protein structures. Therefore, their performance and accuracy when applied to integral membrane proteins remained an open question. We evaluated the performance of 13 disorder prediction programs on a dataset containing 343 membrane proteins, and upon subdatasets containing onlyα-helical orβ-barrel proteins. These programs were ranked using multiple metrics, including metrics specifically created for membrane proteins. Analysis of these data shows a clear distinction between programs that accurately predict disordered regions in membrane proteins and programs which perform poorly, and allows for the robust integration of in silico disorder prediction into our PSI:Biology membrane protein structural genomics pipeline.