Browsing by Subject "Sequence Alignment"
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Item Classification and Differentiation of Homologs and Structural Analogs(2007-08-08) Cheng, Hua; Grishin, NickIt is both meaningful and useful to study protein sequence, structure, and function in the context of evolution. In divergent evolution, homologs, or proteins having descended from a common ancestor, usually share sequence, structure, and functional properties, and an unknown protein's structure and function can be hypothesized from its experimentally characterized homologs. In convergent evolution, proteins from distinct evolutionary lineages converge to similar structures or functions, and these proteins are called "analogs". To classify proteins into evolutionary families, it is necessary to differentiate these two opposite scenarios. Statistically significant sequence similarity is commonly accepted as adequate evidence for homology. Yet in the absence of significant sequence similarity, discrimination between homology and analogy frequently requires manual work. This dissertation describes an effort in developing an automatic tool to differentiate remote homologs and structural analogs.Item Procain: Protein Profile Comparison with Assisting Information(2009-06-19) Wang, Yong; Grishin, NickDetection of remote sequence homology is essential for the accurate inference of protein structure, function, and evolution. The most sensitive detection methods involve the comparison of evolutionary patterns reflected in multiple sequence alignments of protein families. We present PROCAIN, a new method for MSA comparison based on the combination of 'vertical' MSA context (substitution constraints at individual sequence positions) and 'horizontal' context (patterns of residue content at multiple positions). Based on a simple and tractable profile methodology and primitive measures for the similarity of horizontal MSA patterns, the method achieves the quality of homology detection comparable to a more complex advanced method employing hidden Markov models and secondary structure prediction. Adding secondary structure information further improves PROCAIN performance beyond the capabilities of current state-of-the-art tools. The potential value of the method for structure/function predictions is illustrated by the detection of subtle homology between evolutionary distant yet structurally similar protein domains. ProCAIn, relevant databases and tools can be downloaded from http://prodata.swmed.edu/procain/download. The web server can be accessed at http://prodata.swmed.edu/procain/procain.php.