›› 2010, Vol. 25 ›› Issue (1): 82-94.

• Special Issue on Computational Challenges from Modern Molecular Biology • Previous Articles     Next Articles

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

Gloria Rendon1,2, Mao-Feng Ger2,3, Ruth Kantorovitz1,4, Shreedhar Natarajan5, Jeffrey Tilson6, and Eric Jakobsson1,2,3, Fellow, APS   

  1. 1National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, U.S.A.
    2Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, U.S.A.
    3Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, U.S.A.
    4Department of Mathematics, University of Illinois at Urbana-Champaign, U.S.A.
    5Department of Biology, University of Pennsylvania, Philadelphia, U.S.A.
    6Renaissance Computing Institute, Chapel Hill, North Carolina 27517, U.S.A.
  • Received:2009-10-05 Revised:2009-12-16 Online:2010-01-05 Published:2010-01-05
  • About author:
    Jeffrey Tilson is currently a senior research scientist in Renaissance Computing Institute, USA. He received the Ph.D. degree in physical chemistry from Michigan State University in 1992. His research interests are parallel processing, development of parallel algorithms, performance models, computational chemistry, and computational biology.
    Eric Jakobsson is the director of the National Center for Design of Biomimetic Nanoconductors and is professor in the Department of Molecular and Integrative Physiology at the University of Illinois at Urbana-Champaign. He also has appointments at the National Center for Supercomputing Applications (NCSA) and the Beckman Institute for Advanced Science and Technology. His lab works on computational studies of membrane biophysics and organization, ion channel function, and ion channel evolution. His research interests are bioinformatics, cell physiology, computational biology, ion transport, protein dynamics, protein structure
  • Supported by:

    This work is supported by NSF of USA under Grant Nos. 0835718 and 0235792, NIH under Grant Nos. 5PN2EY016570-06 and 5R01NS063405-02, the Beckman Institute for Advanced Science and Technology, the National Center for Supercomputing Applications, and the Renaissance Computing Institute.

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.

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