Pabuwal Vagmita, Li Zhijun
Departments of Chemistry and Biochemistry, University of Sciences in Philadelphia, Philadelphia, PA 19104, USA.
Protein Eng Des Sel. 2008 Jan;21(1):55-64. doi: 10.1093/protein/gzm059. Epub 2008 Jan 3.
De novo protein structure prediction plays an important role in studies of helical membrane proteins as well as structure-based drug design efforts. Developing an accurate scoring function for protein structure discrimination and validation remains a current challenge. Network approaches based on overall network patterns of residue packing have proven useful in soluble protein structure discrimination. It is thus of interest to apply similar approaches to the studies of residue packing in membrane proteins. In this work, we first carried out such analysis on a set of diverse, non-redundant and high-resolution membrane protein structures. Next, we applied the same approach to three test sets. The first set includes nine structures of membrane proteins with the resolution worse than 2.5 A; the other two sets include a total of 101 G-protein coupled receptor models, constructed using either de novo or homology modeling techniques. Results of analyses indicate the two criteria derived from studying high-resolution membrane protein structures are good indicators of a high-quality native fold and the approach is very effective for discriminating native membrane protein folds from less-native ones. These findings should be of help for the investigation of the fundamental problem of membrane protein structure prediction.
从头蛋白质结构预测在螺旋膜蛋白研究以及基于结构的药物设计工作中发挥着重要作用。开发一种准确的评分函数用于蛋白质结构辨别和验证仍然是当前的一项挑战。基于残基堆积的整体网络模式的网络方法已被证明在可溶性蛋白质结构辨别中有用。因此，将类似方法应用于膜蛋白中残基堆积的研究是很有意义的。在这项工作中，我们首先对一组多样、非冗余且高分辨率的膜蛋白结构进行了此类分析。接下来，我们将相同方法应用于三个测试集。第一组包括九个分辨率低于2.5埃的膜蛋白结构；另外两组总共包括101个使用从头或同源建模技术构建的G蛋白偶联受体模型。分析结果表明，从研究高分辨率膜蛋白结构得出的两个标准是高质量天然折叠的良好指标，并且该方法对于区分天然膜蛋白折叠与非天然折叠非常有效。这些发现应该有助于研究膜蛋白结构预测的基本问题。
