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Current progress in structural biology is the result of the efforts of crystallographers and NMR spectroscopists who continuously submit new models to the Protein Data Bank (PDB) (Berman et al., 2000). As a consequence of these efforts, alternative structural models are made available for an increasing number of proteins. Alternative models are usually obtained from a single NMR experiment, from the presence of non-crystallographic symmetry in X-ray crystallography, or from several independent structural determinations of the same protein. These alternative models represent the protein in different complexes, interacting with different ligands or they result from different physicochemical or experimental conditions. Most importantly, they illustrate conformational changes at the backbone and at the side-chain level, that are associated with protein function. Therefore, it is of great interest to characterize the differences and similarities between these models.
The backbone conformational changes associated with ligand binding and with catalysis have been characterized for many proteins. The studies in transferrins and protein kinases provide some examples (Jeffrey et al., 1998; Taylor et al., 2004). In addition, the molecular mechanisms of protein function have been investigated by detailed comparison of the structure of catalytic, ligand binding or protein binding sites upon binding different ligands, substrates, substrate analogues and inhibitors, or in different mutant forms. Studies in 
-amylase, transferrin and PKA provide many examples (Akamine et al., 2003, 2004; Machius et al., 1996; Madhusudan et al., 2002; Nurizzo et al., 2001; Wu et al., 2005). Structural analysis of specific protein sites also play an essential role in the investigation of new enzyme inhibitors and protein–protein interaction inhibitors of medical relevance. Previous work in HCV NS5B and in IL - 2/IL2R provides some examples (Arkin et al., 2003; Biswal et al., 2006; Thanos et al., 2006).
Several established methods are currently available for pairwise and multiple comparison of the protein backbone structure. Some of these have been previously reviewed (Sierk and Kleywegt, 2004), and new methods have been proposed since then (Birzele et al., 2007; Ilyin et al., 2004; Shatsky et al., 2004; Ye and Godzik, 2005; Zhang and Skolnick, 2005). In general, these methods search for an alignment defined via a set of equivalent residues, which maximizes a measure of structure similarity. Methods can be distinguished by the use of different measures of structure similarity and of different alignment search algorithms. Some methods provide multiple solutions, some are optimized for fast computation, others take flexibility explicitly into account. In principle, these methods can be used to compare alternative models of the same protein. Nevertheless, the comparison of different protein structures is not the same as the comparison of alternative models of the same protein; these are different problems that require different solutions.
Finding the set of structurally equivalent residues and generating an alignment is not the goal in the comparison of alternative models. The alignment is predefined in this case because the residue positions in the models correspond to positions in the same protein sequence. In the comparison of different proteins, structurally dissimilar regions are reported as gaps, and in general they are ignored in the computation of the measure of structure similarity. In the comparison of alternative structures these variable regions are generally aligned, and correspond to different conformations. The identification of the location and extent of the conformational change, as well as the identification of the structurally conserved invariant regions are of major interest in the comparison of alternative models.
A number of approaches have been developed for the characterization of alternative structures. These approaches tend to rely on clustering methods (Kaufman and Rousseeuw, 2005), distance matrices (Phillips, 1970), difference distance matrices (Nishikawa et al., 1972) and variance matrices (Kelley et al., 1997). Clustering approaches have been proposed for identifying the representative structures in NMR ensembles (Kelley et al., 1996) and for grouping the alternative models available for each protein (Domingues et al., 2004). A local backbone-superposition method has been proposed for locating and visualizing variable regions (Lema and Echave, 2005). NMRCORE (Kelley et al., 1997) has been proposed for identifying invariant regions (also called local structural domains) in NMR ensembles by clustering, based on the C atomic distance variances. ESCET computes differences of C atomic distance for visualizing the structural similarities and differences between alternative structures and for identifying the invariant regions (Schneider, 2000, 2002, 2004).
We describe STRuster, a method for characterizing alternative models of a given protein at two levels: backbone conformation and side-chain conformation. There are several stages in the analysis: grouping of models, analysis of variation and comparison. The C atoms are used to analyse the backbone conformation. In the first stage the models are grouped according to backbone structural similarity using clustering methods. This first stage has been previously described (Domingues et al., 2004), but all other functionality is new. In the second stage, the structural variation is analysed. In particular, the location and extent of the backbone similarities and differences are identified. The structural differences can be the result of flexibility resulting from thermal motion, or alternatively from collective motions or from triggered conformational changes (Petsko and Ringe, 2003). Thermal and collective motions are usually associated with a continuous distribution of the different conformations, but triggered conformational changes can result in distinct conformational states. Estimates of coordinate uncertainty are indicators of thermal motion. The variance of atomic distances provides a measure of collective motions and of triggered conformational changes. In the third stage, distinct conformational states are identified by comparison of subsets of alternative models. Of particular interest is the identification of invariant and variable parts of the protein. The relative geometry is preserved in the invariant regions, but not in the variable parts. A similar three-stage analysis (grouping, variation analysis and comparison) is performed to compare side-chain conformations in order to characterize in detail specific sites in the protein.
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