Visualizing Sequence Similarity of Protein Families

Abstract

Classification of proteins into families is one of the main goals of functional analysis. Proteins are usually assigned to a family on the basis of the presence of family-specific patterns, domains, or structural elements. Whereas proteins belonging to the same family are generally similar to each other, the extent of similarity varies widely across families. Some families are characterized by short, well-defined motifs, whereas others contain longer, less-specific motifs. We present a simple method for visualizing such differences. We applied our method to the Arabidopsis thaliana families listed at The Arabidopsis Information Resource (TAIR) Web site and for 76% of the nontrivial families (families with more than one member), our method identifies simple similarity measures that are necessary and sufficient to cluster members of the family together. Our visualization method can be used as part of an annotation pipeline to identify potentially incorrectly defined families. We also describe how our method can be extended to identify novel families and to assign unclassified proteins into known families.Genome projects (Bernal et al. 2001) are generating sequence data at a much faster rate than can be effectively analyzed. The goal of functional genomics is to determine the function of proteins predicted by these sequencing projects (Bork et al. 1998;Eisenberg et al. 2000;Tsoka and Ouzounis 2000). Because experimental evidence about individual proteins is difficult to obtain, a common strategy is to classify proteins into families on the basis of the presence of shared features or by clustering using some similarity measure. The underlying assumption is that members of the same family may possess similar or identical biochemical functions (Hegyi and Gerstein 1999) and that one can assign the functions of well-characterized members of a family to other members whose functions are not known or not well understood (Heger and Holm 2000).The simplest methods for clustering proteins into families rely on sequence-similarity measures, such as those obtained by BLAST (Altschul et al. 1990). More sophisticated approaches detect domains using domain databases (Bateman et al. 2002;Servant et al. 2002;Mulder et al. 2003), optionally use the order of domains as a fingerprint for the protein, and classify proteins into families on the basis of the presence of shared domains or similar domain architecture (Geer et al. 2002). Classification of proteins into families using structural similarities (Holm and Sander 1996) is, at present, limited by the relatively small number of structures available in PDB ( Similarity-based clustering is a two-step process-one first needs to determine pairwise similarities between all pairs of proteins and then apply a clustering method that uses the similarity matrix to group proteins into clusters. However, methods that quantify similarity by using some attribute of the best BLAST hit and use single-linkage clustering are not always successful. One problem such methods face is the detection of th...

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