Literature DB >> 11292355

Three-dimensional cluster analysis identifies interfaces and functional residue clusters in proteins.

R Landgraf1, I Xenarios, D Eisenberg.   

Abstract

Three-dimensional cluster analysis offers a method for the prediction of functional residue clusters in proteins. This method requires a representative structure and a multiple sequence alignment as input data. Individual residues are represented in terms of regional alignments that reflect both their structural environment and their evolutionary variation, as defined by the alignment of homologous sequences. From the overall (global) and the residue-specific (regional) alignments, we calculate the global and regional similarity matrices, containing scores for all pairwise sequence comparisons in the respective alignments. Comparing the matrices yields two scores for each residue. The regional conservation score (C(R)(x)) defines the conservation of each residue x and its neighbors in 3D space relative to the protein as a whole. The similarity deviation score (S(x)) detects residue clusters with sequence similarities that deviate from the similarities suggested by the full-length sequences. We evaluated 3D cluster analysis on a set of 35 families of proteins with available cocrystal structures, showing small ligand interfaces, nucleic acid interfaces and two types of protein-protein interfaces (transient and stable). We present two examples in detail: fructose-1,6-bisphosphate aldolase and the mitogen-activated protein kinase ERK2. We found that the regional conservation score (C(R)(x)) identifies functional residue clusters better than a scoring scheme that does not take 3D information into account. C(R)(x) is particularly useful for the prediction of poorly conserved, transient protein-protein interfaces. Many of the proteins studied contained residue clusters with elevated similarity deviation scores. These residue clusters correlate with specificity-conferring regions: 3D cluster analysis therefore represents an easily applied method for the prediction of functionally relevant spatial clusters of residues in proteins. Copyright 2001 Academic Press.

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Year:  2001        PMID: 11292355     DOI: 10.1006/jmbi.2001.4540

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  73 in total

1.  A likelihood ratio test for evolutionary rate shifts and functional divergence among proteins.

Authors:  B Knudsen; M M Miyamoto
Journal:  Proc Natl Acad Sci U S A       Date:  2001-12-04       Impact factor: 11.205

2.  Structural similarity to link sequence space: new potential superfamilies and implications for structural genomics.

Authors:  Patrick Aloy; Baldomero Oliva; Enrique Querol; Francesc X Aviles; Robert B Russell
Journal:  Protein Sci       Date:  2002-05       Impact factor: 6.725

3.  Development of unified statistical potentials describing protein-protein interactions.

Authors:  Hui Lu; Long Lu; Jeffrey Skolnick
Journal:  Biophys J       Date:  2003-03       Impact factor: 4.033

Review 4.  Accurate and scalable identification of functional sites by evolutionary tracing.

Authors:  Olivier Lichtarge; Hui Yao; David M Kristensen; Srinivasan Madabushi; Ivana Mihalek
Journal:  J Struct Funct Genomics       Date:  2003

5.  Are protein-protein interfaces more conserved in sequence than the rest of the protein surface?

Authors:  Daniel R Caffrey; Shyamal Somaroo; Jason D Hughes; Julian Mintseris; Enoch S Huang
Journal:  Protein Sci       Date:  2004-01       Impact factor: 6.725

6.  Structure of HrcQB-C, a conserved component of the bacterial type III secretion systems.

Authors:  Vasiliki E Fadouloglou; Anastasia P Tampakaki; Nicholas M Glykos; Marina N Bastaki; Jonathan M Hadden; Simon E Phillips; Nicholas J Panopoulos; Michael Kokkinidis
Journal:  Proc Natl Acad Sci U S A       Date:  2003-12-23       Impact factor: 11.205

7.  Using electrostatic potentials to predict DNA-binding sites on DNA-binding proteins.

Authors:  Susan Jones; Hugh P Shanahan; Helen M Berman; Janet M Thornton
Journal:  Nucleic Acids Res       Date:  2003-12-15       Impact factor: 16.971

Review 8.  Structural genomics: computational methods for structure analysis.

Authors:  Sharon Goldsmith-Fischman; Barry Honig
Journal:  Protein Sci       Date:  2003-09       Impact factor: 6.725

9.  Using evolutionary rates to investigate protein functional divergence and conservation. A case study of the carbonic anhydrases.

Authors:  Bjarne Knudsen; Michael M Miyamoto; Philip J Laipis; David N Silverman
Journal:  Genetics       Date:  2003-08       Impact factor: 4.562

10.  Prediction of functional sites by analysis of sequence and structure conservation.

Authors:  Anna R Panchenko; Fyodor Kondrashov; Stephen Bryant
Journal:  Protein Sci       Date:  2004-03-09       Impact factor: 6.725

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