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A permissive secondary structure-guided superposition tool for clustering of protein fragments toward protein structure prediction via fragment assembly

  1. Author:
    Wainreb, G.
    Haspel, N.
    Wolfson, H. J.
    Nussinov, R.

  2. Author Address

    Tel Aviv Univ, Sackler Fac Med, Dept Human Genet, Sackler Inst Mol Med, IL-69978 Tel Aviv, Israel. Tel Aviv Univ, Fac Exact Sci, Sch Comp Sci, Sackler Inst Mol Med, IL-69978 Tel Aviv, Israel. NCI, SAIC Frederick Inc, Basic Res Program, Lab Expt & Computat Biol, Frederick, MD 21702 USA.;Nussinov, R, Tel Aviv Univ, Sackler Fac Med, Dept Human Genet, Sackler Inst Mol Med, IL-69978 Tel Aviv, Israel.;ruthn@ncifcrf.gov
    1. Year: 2006
    2. Date: Jun
  2. Journal: Bioinformatics
    1. 22
    2. 11
    3. Pages: 1343-1352
  4. Type of Article: Article
  5. ISSN: 1367-4803
  1. Abstract:

    Motivation: Secondary-Structure Guided Superposition tool (SSGS) is a permissive secondary structure-based algorithm for matching of protein structures and in particular their fragments. The algorithm was developed towards protein structure prediction via fragment assembly. Results: In a fragment-based structural prediction scheme, a protein sequence is cut into building blocks (BBs). The BBs are assembled to predict their relative 3D arrangement. Finally, the assemblies are refined. To implement this prediction scheme, a clustered structural library representing sequence patterns for protein fragments is essential. To create a library, BBs generated by cutting proteins from the PDB are compared and structurally similar BBs are clustered. To allow structural comparison and clustering of the BBs, which are often relatively short with flexible loops, we have devised SSGS. SSGS maintains high similarity between cluster members and is highly efficient. When it comes to comparing BBs for clustering purposes, the algorithm obtains better results than other, non-secondary structure guided protein superimposition algorithms.

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External Sources

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  2. DOI: 10.1093/bioinformatics/bt/098
  3. View record in Web of Science®
  4. WOS: 000238356700009

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