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

Joe Dundas,Larisa Adamian,Jie Liang

doi:10.1016/j.jmb.2010.12.005

Abstract

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.

Highlights

A widely used method for inferring protein function is to transfer functional information based on homology analysis of shared characteristics between proteins
We study general issues of how to obtain signature pockets automatically and how they can be used for detecting binding surface and for predicting enzyme function
Depending on the identity of the Z residue in a conserved catalytic motif (HEXXHXXGXXHZ), metzincins are further divided into 6 subclasses: serralysins, astacins, adamalysins, snapalysins, leishmanolysins, and matrix metalloproteinase (MMPs)[41]

Summary

Introduction

A widely used method for inferring protein function is to transfer functional information based on homology analysis of shared characteristics between proteins. If a protein shares a high level of sequence identity to a well characterized family of proteins, frequently the biological functions of the family can frequently be accurately transferred onto that protein[1,2,3]. Limitations to sequence-based homology transfer for function prediction arise when sequence identity between a pair of proteins is less than 60%4. An alternative to sequence analysis is to infer protein functions based on structural similarity, as protein structure and protein function are strongly correlated[5]. It is well known that protein structures are much more conserved than protein sequences, and proteins with little sequence identity often fold into similar three-dimensional structures[6,7]. Comparison of global structural fold may offer insight into remote and complex evolutionary relationships[8], the overall similarity in three dimensional structure is a poor predictor of protein function[9,10,11,12,13], as the structure-function relationship of proteins may be continuous and not restricted by overall fold[14]

Methods

Results

Conclusion