MASS: multiple structural alignment by secondary structures

We present a novel method for multiple alignment of protein structures and detection of structural motifs. To date, only a few methods are available for addressing this task. Most of them are based on a series of pairwise comparisons. In contrast, MASS (Multiple Alignment by Secondary Structures) co...

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Veröffentlicht in:Bioinformatics 2003-07, Vol.19 (suppl-1), p.i95-i104
Hauptverfasser: Dror, O., Benyamini, H., Nussinov, R., Wolfson, H.
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Sprache:eng
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Zusammenfassung:We present a novel method for multiple alignment of protein structures and detection of structural motifs. To date, only a few methods are available for addressing this task. Most of them are based on a series of pairwise comparisons. In contrast, MASS (Multiple Alignment by Secondary Structures) considers all the given structures at the same time. Exploiting the secondary structure representation aids in filtering out noisy results and in making the method highly efficient and robust. MASS disregards the sequence order of the secondary structure elements. Thus, it can find non-sequential and even non-topological structural motifs. An important novel feature of MASS is subset alignment detection: It does not require that all the input molecules be aligned. Rather, MASS is capable of detecting structural motifs shared only by a subset of the molecules. Given its high efficiency and capability of detecting subset alignments, MASS is suitable for a broad range of challenging applications: It can handle large-scale protein ensembles (on the order of tens) that may be heterogeneous, noisy, topologically unrelated and contain structures of low resolution. Keywords: Structural Bioinformatics, Subset alignment, Non-sequential alignment, Non-topological motif, Supersecondary structural motif Availability: MASS program and more information about it are available on http://bioinfo3d.cs.tau.ac.il/MASS Contact: oranit@post.tau.ac.il * To whom correspondence should be addressed.
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btg1012