Structural Signatures of Enzyme Binding Pockets from Order-Independent Surface Alignment: A Study of Metalloendopeptidase and NAD Binding Proteins

摘要

Detecting similarities between local binding surfaces can facilitate identification of enzyme binding sites, prediction of enzyme functions, as well as aid in our understanding of enzyme mechanisms. A challenging task is to construct a template of local surface characteristics for a specific enzyme function or binding activity, as the size and shape of binding surfaces of a biochemical function often varies. Here we introduce the concept of signature binding pockets, which captures information about preserved and varied atomic positions at multi-resolution levels. For proteins with complex enzyme binding and activity, multiple signatures arise naturally in our model, which form a signature basis set that characterize this class of proteins. Both signatures and signature basis set 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 (MSFA) to facilitate interpretation of computed signatures. For studying a family of evolutionary 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 enzyme functions, and to identify the specific binding surfaces. For studying unrelated proteins which have evolved to bind to the same NAD co-factor, signatures of NAD binding pockets can be constructed and can be used 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 important for binding affinity and specificity. In both cases, we show that signatures and signature basis set reveal significant biological insight.