Understanding the “Horizontal Dimension” of Molecular Evolution to Annotate, Classify, and Discover Proteins with Functional Domains

Protein evolution proceeds by two distinct processes: 1) individual mutation and selection for adaptive mutations and 2) rearrangement of entire domains within proteins into novel combinations, producing new protein families that combine functional properties in ways that previously did not exist. D...

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Veröffentlicht in:Journal of computer science and technology 2010, Vol.25 (1), p.82-94
Hauptverfasser: Rendon, Gloria, Ger, Mao-Feng, Kantorovitz, Ruth, Natarajan, Shreedhar, Tilson, Jeffrey, Jakobsson, Eric
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Sprache:eng
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Zusammenfassung:Protein evolution proceeds by two distinct processes: 1) individual mutation and selection for adaptive mutations and 2) rearrangement of entire domains within proteins into novel combinations, producing new protein families that combine functional properties in ways that previously did not exist. Domain rearrangement poses a challenge to sequence alignment-based search methods, such as BLAST, in predicting homology since the methodology implicitly assumes that related proteins primarily differ from each other by individual mutations. Moreover, there is ample evidence that the evolutionary process has used (and continues to use) domains as building blocks, therefore, it seems fit to utilize computational, domain-based methods to reconstruct that process. A challenge and opportunity for computational biology is how to use knowledge of evolutionary domain recombination to characterize families of proteins whose evolutionary history includes such recombination, to discover novel proteins, and to infer protein-protein interactions. In this paper we review techniques and databases that exploit our growing knowledge of “horizontal” protein evolution, and suggest possible areas of future development. We illustrate the power of the domain-based methods and the possible directions of future development by a case history in progress aiming at facilitating a particular approach to understanding microbial pathogenicity.
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-010-9307-3