Zhao Jieling, Dundas Joe, Kachalo Sema, Ouyang Zheng, Liang Jie
Department of Bioengineering, University of Illinois at Chicago, 851 S. Morgan Street, Room 218, MC-063, Chicago, IL 60607, USA.
J Struct Funct Genomics. 2011 Jul;12(2):97-107. doi: 10.1007/s10969-011-9109-z. Epub 2011 May 4.
Identification and characterization of protein functional surfaces are important for predicting protein function, understanding enzyme mechanism, and docking small compounds to proteins. As the rapid speed of accumulation of protein sequence information far exceeds that of structures, constructing accurate models of protein functional surfaces and identify their key elements become increasingly important. A promising approach is to build comparative models from sequences using known structural templates such as those obtained from structural genome projects. Here we assess how well this approach works in modeling binding surfaces. By systematically building three-dimensional comparative models of proteins using MODELLER: , we determine how well functional surfaces can be accurately reproduced. We use an alpha shape based pocket algorithm to compute all pockets on the modeled structures, and conduct a large-scale computation of similarity measurements (pocket RMSD and fraction of functional atoms captured) for 26,590 modeled enzyme protein structures. Overall, we find that when the sequence fragment of the binding surfaces has more than 45% identity to that of the template protein, the modeled surfaces have on average an RMSD of 0.5 Å, and contain 48% or more of the binding surface atoms, with nearly all of the important atoms in the signatures of binding pockets captured.
蛋白质功能表面的识别与表征对于预测蛋白质功能、理解酶作用机制以及将小分子化合物与蛋白质对接至关重要。由于蛋白质序列信息的积累速度远远超过结构信息的积累速度，构建准确的蛋白质功能表面模型并识别其关键要素变得越来越重要。一种有前景的方法是利用已知的结构模板（如从结构基因组计划中获得的模板）从序列构建比较模型。在此，我们评估这种方法在建模结合表面方面的效果如何。通过使用MODELLER系统地构建蛋白质的三维比较模型，我们确定功能表面能够被准确重现的程度。我们使用基于α形状的口袋算法来计算建模结构上的所有口袋，并对26590个建模的酶蛋白结构进行大规模的相似性测量计算（口袋均方根偏差和捕获的功能原子分数）。总体而言，我们发现当结合表面的序列片段与模板蛋白的序列片段具有超过45%的同一性时，建模表面的平均均方根偏差为0.5 Å，并且包含48%或更多的结合表面原子，几乎所有结合口袋特征中的重要原子都被捕获。
