Department of Bioengineering, University of Illinois at Chicago, 835 South Wolcott, Chicago, IL 60612, USA.
J Mol Biol. 2011 Mar 11;406(5):713-29. doi: 10.1016/j.jmb.2010.12.005. Epub 2010 Dec 9.
Detecting similarities between local binding surfaces can facilitate identification of enzyme binding sites and prediction of enzyme functions, and aid in our understanding of enzyme mechanisms. Constructing a template of local surface characteristics for a specific enzyme function or binding activity is a challenging task, as the size and shape of the binding surfaces of a biochemical function often vary. Here we introduce the concept of signature binding pockets, which captures information on preserved and varied atomic positions at multiresolution levels. For proteins with complex enzyme binding and activity, multiple signatures arise naturally in our model, forming a signature basis set that characterizes this class of proteins. Both signatures and signature basis sets can be automatically constructed by a method called SOLAR (Signature Of Local Active Regions). This method is based on a sequence-order-independent alignment of computed binding surface pockets. SOLAR also provides a structure-based multiple sequence fragment alignment to facilitate the interpretation of computed signatures. By studying a family of evolutionarily related proteins, we show that for metzincin metalloendopeptidase, which has a broad spectrum of substrate binding, signature and basis set pockets can be used to discriminate metzincins from other enzymes, to predict the subclass of metzincins functions, and to identify specific binding surfaces. Studying unrelated proteins that have evolved to bind to the same NAD cofactor, we constructed signatures of NAD binding pockets and used them to predict NAD binding proteins and to locate NAD binding pockets. By measuring preservation ratio and location variation, our method can identify residues and atoms that are important for binding affinity and specificity. In both cases, we show that signatures and signature basis set reveal significant biological insight.
检测局部结合表面之间的相似性可以促进酶结合位点的识别和酶功能的预测,并有助于我们理解酶的机制。为特定的酶功能或结合活性构建局部表面特征的模板是一项具有挑战性的任务,因为生化功能的结合表面的大小和形状通常会有所不同。在这里,我们引入了特征结合口袋的概念,该概念捕获了多分辨率水平上保留和变化的原子位置的信息。对于具有复杂酶结合和活性的蛋白质,我们的模型中自然会出现多个特征,形成一个特征基组,用于描述这一类蛋白质。特征和特征基组都可以通过一种称为 SOLAR(局部活性区域的特征)的方法自动构建。该方法基于计算结合表面口袋的序列无关对齐。SOLAR 还提供了基于结构的多重序列片段对齐,以方便解释计算出的特征。通过研究一组进化相关的蛋白质,我们表明,对于具有广泛底物结合的金属内肽酶,特征和基组口袋可用于区分金属内肽酶和其他酶,预测金属内肽酶功能的亚类,并识别特定的结合表面。研究进化到结合相同 NAD 辅助因子的不相关蛋白质,我们构建了 NAD 结合口袋的特征,并使用它们来预测 NAD 结合蛋白和定位 NAD 结合口袋。通过测量保留率和位置变化,我们的方法可以识别对结合亲和力和特异性重要的残基和原子。在这两种情况下,我们都表明特征和特征基组揭示了重要的生物学见解。