Predicting nucleic acid binding interfaces from structural models of proteins.
Predicting nucleic acid binding interfaces from structural models of proteins.
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DOI：
10.1002/prot.23214
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发表时间：
2012-02
影响因子：
2.9
通讯作者：
Mandel-Gutfreund, Yael
中科院分区：
文献类型：
--
作者：
Dror, Iris;Shazman, Shula;Mukherjee, Srayanta;Zhang, Yang;Glaser, Fabian;Mandel-Gutfreund, Yael
DNA和RNA结合蛋白的功能可以从其结合界面的表征和准确预测中推断出来。然而，用于预测核酸结合功能的各种基于结构的方法的主要缺陷是它们都限于相对少量的蛋白质，对于这些蛋白质，高分辨率三维结构是可用的。在这项研究中，我们开发了一个管道，用于从蛋白质结构模型的表面提取功能性静电补丁，使用I-TASSER蛋白质结构预测器获得。使用patchfinder算法从蛋白质表面提取最大的正补丁。我们发现，从结构模型的合奏中提取的功能静电补丁高度重叠的补丁提取的高分辨率结构。此外，通过在一组55种已知的核酸结合蛋白上测试我们的管道，I-TASSER为这些蛋白生成了高质量的模型，我们表明该方法可以准确地识别蛋白质结构模型上的核酸结合界面。采用一个组合补丁的方法，我们表明，补丁提取的合奏模型更好地预测的真实的核酸结合界面相比，补丁从独立的模型。总的来说，这些结果表明，结合低分辨率结构模型的集合的信息可能是一个有价值的方法功能注释。我们建议，我们的方法将进一步适用于预测其他功能表面的蛋白质未知结构。
The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared to patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure.
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