![]() ![]() Statistically significant sequence similarity, as detected by sequence search tools such as PSI-BLAST, 4 is generally accepted as adequate evidence for homology. Judging if two structurally similar proteins are homologous or analogous remains a difficult task. In convergent evolution, proteins from distinct evolutionary lineages arrive independently at similar structures due to a limited number of energetically favorable ways to pack secondary structural elements (SSEs), 1– 3 and such proteins are called analogs. In divergent evolution, homologs inherit similar structures from their common ancestor. Three-dimensional structural similarities among proteins are explained by either divergence or convergence. More importantly, we also detect and discuss interesting homologous relationships between SCOP domains from different superfamilies, folds, and even classes. The SVM classifier recovers 76% of the remote homologs defined as domains in the same SCOP superfamily but from different families. We apply our classifier to a representative set from the expert-constructed database, Structural Classification of Proteins (SCOP). We observe that although both structure scores and sequence scores contribute to SVM performance, profile sequence scores computed based on structural alignments are the best discriminators between remote homologs and structural analogs. The classifier uses a number of well-known similarity scores. ![]() In this study, we compare these two data sets and develop a support vector machine (SVM)-based classifier to discriminate between homologs and analogs. Using novel strategies, we previously assembled two reliable databases of homologs and analogs. Homologs inherit similarities from their common ancestor, while analogs converge to similar structures due to a limited number of energetically favorable ways to pack secondary structural elements. ![]() A natural way to study protein sequence, structure, and function is to put them in the context of evolution. ![]()
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