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SCOPmap ( 13) is an approach that achieves roughly 95% accuracy when classifying proteins into the superfamily level of the SCOP hierarchy. Their features include secondary structure elements predicted by the Stride program ( 12), the sequence length, and the percentage of observed helices. For example, ProtClass ( 11) uses a nearest-neighbor-based classification scheme and several structural features to classify proteins at the fold level of the SCOP hierarchy. In order to achieve a high level of agreement with other clustering schemes, some algorithms that use a multi-criterion approach (weighted combination of different scoring schemes), are initially trained on labeled data from an existing structural hierarchy (SCOP or CATH) and use cross-validation (or similar methods) to select the best parameters for their classifiers. They applied their method to predicting membership of proteins in CATH and achieved 95% accuracy at all levels of the classification hierarchy. They introduced the SGM (Scaled Gauss Metric), which is a metric derived from knot theoretical ideas to cluster proteins according to their structural topologies. An approach for structural comparisons, fundamentally different from those using RMSD, was proposed by Rogen and Fain ( 10). These two filters are introduced in order to identify both large and small (but significant) backbone conformational changes by reducing the influence in local large distances (only distances below 14.0 Å are considered) and also to restrict the analysis to significant structural differences (the distances above 1.0 Å). In addition to the traditional RMSD measure, the STRuster method uses two filters to define the final scoring metric called dissimilarity measure M ( 9). models that correspond to different structure determination experiments). The authors of the STRuster ( 9) method explore the calculation of root mean square deviations (RMSD) and use their algorithm to cluster alternative structural models from the PDB (i.e. After performing all-against-all comparisons of protein chains from the PDB, resulting CE Z-score values of 4.5 and above are used to discriminate at the family level, values between 4.0 and 4.5 at the superfamily and/or fold levels, and values between 3.5 and 4.0 are presumed to indicate possible biologically interesting similarities. A similar approach is used in CE ( 8) classification. ( 2) explain ‘despite the advances in sequence comparison methods, remote homologs in the “Midnight Zone” of sequence similarity ( 2) can be used as an operational definition of folds. Thus, one of the most important improvements in protein classification would be protein homology/analogy identification at very low levels of sequence similarity ( 1). But even when sequence similarity between two proteins is low, structure similarity can be high. Most protein annotation and classification approaches depend heavily on the degree of observed amino acid sequence similarity to other related proteins. We also provide a web server at for selecting protein structures, calculating structurally conserved regions and performing automated clustering. In our approach, called STRALCP (STRucture ALignment-based Clustering of Proteins), we generate detailed information about global and local similarities between pairs of protein structures, identify fragments (spans) that are structurally conserved among proteins, and use these spans to group the structures accordingly. We present an algorithm that identifies regions of structural similarity within a given set of protein structures, and uses those regions for clustering.
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In this article, we attempt to determine whether structure information alone is sufficient to adequately classify protein structures. Clustering of protein domains based on their structural similarities provides valuable information for protein classification schemes. Research towards analysis of sequence–structure correspondences is critical for better understanding of a protein's structure, function, and its interaction with other molecules. Protein structural annotation and classification is an important and challenging problem in bioinformatics.
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