Generation and analysis of a protein–protein interface data set with similar chemical and spatial patterns of interactions

Authors

  • Shira Mintz,

    1. Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
    Search for more papers by this author
    • This work was performed in partial fulfillment of the requirements for an M. Sc. degree.

  • Alexandra Shulman-Peleg,

    1. School of Computer Science, Raymond and Beverly Sackler, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel
    Search for more papers by this author
  • Haim J. Wolfson,

    1. School of Computer Science, Raymond and Beverly Sackler, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv, Israel
    Search for more papers by this author
  • Ruth Nussinov

    Corresponding author
    1. Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
    2. Basic Research Program, SAIC-Frederick, Inc., Laboratory of Experimental and Computational Biology, Frederick, Maryland
    • Bldg. 469, Rm 151, NCI-Frederick, Frederick, MD 21702
    Search for more papers by this author

  • The content of this publication does not necessarily reflect the view or policies of the Department of Health and Human Services, nor does the mention of trade names, commercial products, or organization imply endorsement by the U.S. Government. The publisher or recipient acknowledges the right of the U.S. Government to retain a nonexclusive, royalty-free license in and to any copyright covering the article.

Abstract

Protein–protein interfaces are regions between 2 polypeptide chains that are not covalently connected. Here, we have created a nonredundant interface data set generated from all 2-chain interfaces in the Protein Data Bank. This data set is unique, since it contains clusters of interfaces with similar shapes and spatial organization of chemical functional groups. The data set allows statistical investigation of similar interfaces, as well as the identification and analysis of the chemical forces that account for the protein–protein associations. Toward this goal, we have developed I2I-SiteEngine (Interface-to-Interface SiteEngine) [Data set available at http://bioinfo3d.cs.tau.ac.il/Interfaces; Web server: http://bioinfo3d.cs.tau.ac.il/I2I-SiteEngine]. The algorithm recognizes similarities between protein–protein binding surfaces. I2I-SiteEngine is independent of the sequence or the fold of the proteins that comprise the interfaces. In addition to geometry, the method takes into account both the backbone and the side-chain physicochemical properties of the interacting atom groups. Its high efficiency makes it suitable for large-scale database searches and classifications. Below, we briefly describe the I2I-SiteEngine method. We focus on the classification process and the obtained nonredundant protein–protein interface data set. In particular, we analyze the biological significance of the clusters and present examples which illustrate that given constellations of chemical groups in protein–protein binding sites may be preferred, and are observed in proteins with different structures and different functions. We expect that these would yield further information regarding the forces stabilizing protein–protein interactions. Proteins 2005. 2005 Wiley-Liss, Inc.

Ancillary