In search for more accurate alignments in the twilight zone

Authors

  • Lukasz Jaroszewski,

    1. Program in Bioinformatics and Biological Complexity, The Burnham Institute, La Jolla, California 92037, USA
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    • Present address: Bioinformatics Core of Joint Center of Structural Genomics, University of California San Diego, La Jolla, California 92093-0527, USA.

  • Weizhong Li,

    1. Program in Bioinformatics and Biological Complexity, The Burnham Institute, La Jolla, California 92037, USA
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    • Present address: Quorex Pharmaceuticals, Carlsbad, California 92008, USA.

  • Adam Godzik

    Corresponding author
    1. Program in Bioinformatics and Biological Complexity, The Burnham Institute, La Jolla, California 92037, USA
    • Program in Bioinformatics and Biological Complexity, The Burnham Institute, 10901 N. Torrey Pines Road, La Jolla, CA 92037, USA; fax: (858) 646-3171.
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Abstract

A major bottleneck in comparative modeling is the alignment quality; this is especially true for proteins whose distant relationships could be reliably recognized only by recent advances in fold recognition. The best algorithms excel in recognizing distant homologs but often produce incorrect alignments for over 50% of protein pairs in large fold-prediction benchmarks. The alignments obtained by sequence–sequence or sequence–structure matching algorithms differ significantly from the structural alignments. To study this problem, we developed a simplified method to explicitly enumerate all possible alignments for a pair of proteins. This allowed us to estimate the number of significantly different alignments for a given scoring method that score better than the structural alignment. Using several examples of distantly related proteins, we show that for standard sequence–sequence alignment methods, the number of significantly different alignments is usually large, often about 1010 alternatives. This distance decreases when the alignment method is improved, but the number is still too large for the brute force enumeration approach. More effective strategies were needed, so we evaluated and compared two well-known approaches for searching the space of suboptimal alignments. We combined their best features and produced a hybrid method, which yielded alignments that surpassed the original alignments for about 50% of protein pairs with minimal computational effort.

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