Literature DB >> 16247798

Survey of the geometric association of domain-domain interfaces.

Wan Kyu Kim1, Jon C Ison.   

Abstract

Considering the limited success of the most sophisticated docking methods available and the amount of computation required for systematic docking, cataloging all the known interfaces may be an alternative basis for the prediction of protein tertiary and quaternary structures. We classify domain interfaces according to the geometry of domain-domain association. By applying a simple and efficient method called "interface tag clustering," more than 4,000 distinct types of domain interfaces are collected from Protein Quaternary Structure Server and Protein Data Bank. Given a pair of interacting domains, we define "face" as the set of interacting residues in each single domain and the pair of interacting faces as an "interface." We investigate how the geometry of interfaces relates to a network of interacting protein families, such as how many different binding orientations are possible between two families or whether a family uses distinct surfaces or the same surface when the family has diverse interaction partners from various families. We show there are, on average, 1.2-1.9 different types of interfaces between interacting domains and a significant number of family pairs associate in multiple orientations. In general, a family tends to use distinct faces for each partner when the family has diverse interaction partners. Each face is highly specific to its interaction partner and the binding orientation. The relative positions of interface residues are generally well conserved within the same type of interface even between remote homologs. The classification result is available at http://www.biotec.tu-dresden.de/~wkim/supplement. Proteins 2005. 2005 Wiley-Liss, Inc.

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Substances:

Year:  2005        PMID: 16247798     DOI: 10.1002/prot.20693

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  14 in total

1.  Protein interface conservation across structure space.

Authors:  Qiangfeng Cliff Zhang; Donald Petrey; Raquel Norel; Barry H Honig
Journal:  Proc Natl Acad Sci U S A       Date:  2010-06-01       Impact factor: 11.205

2.  Co-evolutionary analysis of domains in interacting proteins reveals insights into domain-domain interactions mediating protein-protein interactions.

Authors:  Raja Jothi; Praveen F Cherukuri; Asba Tasneem; Teresa M Przytycka
Journal:  J Mol Biol       Date:  2006-08-01       Impact factor: 5.469

Review 3.  Computational prediction of protein-protein interactions.

Authors:  Lucy Skrabanek; Harpreet K Saini; Gary D Bader; Anton J Enright
Journal:  Mol Biotechnol       Date:  2007-08-14       Impact factor: 2.695

4.  Coevolution at protein complex interfaces can be detected by the complementarity trace with important impact for predictive docking.

Authors:  Hocine Madaoui; Raphaël Guerois
Journal:  Proc Natl Acad Sci U S A       Date:  2008-05-29       Impact factor: 11.205

5.  Exploring functional roles of multibinding protein interfaces.

Authors:  Manoj Tyagi; Benjamin A Shoemaker; Stephen H Bryant; Anna R Panchenko
Journal:  Protein Sci       Date:  2009-08       Impact factor: 6.725

6.  Entropic contributions and the influence of the hydrophobic environment in promiscuous protein-protein association.

Authors:  Chia-En A Chang; William A McLaughlin; Riccardo Baron; Wei Wang; J Andrew McCammon
Journal:  Proc Natl Acad Sci U S A       Date:  2008-05-21       Impact factor: 11.205

7.  Predicting the protein-protein interactions using primary structures with predicted protein surface.

Authors:  Darby Tien-Hao Chang; Yu-Tang Syu; Po-Chang Lin
Journal:  BMC Bioinformatics       Date:  2010-01-18       Impact factor: 3.169

8.  SCOPPI: a structural classification of protein-protein interfaces.

Authors:  Christof Winter; Andreas Henschel; Wan Kyu Kim; Michael Schroeder
Journal:  Nucleic Acids Res       Date:  2006-01-01       Impact factor: 16.971

9.  A top-down approach to infer and compare domain-domain interactions across eight model organisms.

Authors:  Chittibabu Guda; Brian R King; Lipika R Pal; Purnima Guda
Journal:  PLoS One       Date:  2009-03-31       Impact factor: 3.240

10.  Using structural motif descriptors for sequence-based binding site prediction.

Authors:  Andreas Henschel; Christof Winter; Wan Kyu Kim; Michael Schroeder
Journal:  BMC Bioinformatics       Date:  2007-05-22       Impact factor: 3.169

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