Literature DB >> 14739328

Automated selection of positions determining functional specificity of proteins by comparative analysis of orthologous groups in protein families.

Olga V Kalinina1, Andrey A Mironov, Mikhail S Gelfand, Aleksandra B Rakhmaninova.   

Abstract

The increasing volume of genomic data opens new possibilities for analysis of protein function. We introduce a method for automated selection of residues that determine the functional specificity of proteins with a common general function (the specificity-determining positions [SDP] prediction method). Such residues are assumed to be conserved within groups of orthologs (that may be assumed to have the same specificity) and to vary between paralogs. Thus, considering a multiple sequence alignment of a protein family divided into orthologous groups, one can select positions where the distribution of amino acids correlates with this division. Unlike previously published techniques, the introduced method directly takes into account nonuniformity of amino acid substitution frequencies. In addition, it does not require setting arbitrary thresholds. Instead, a formal procedure for threshold selection using the Bernoulli estimator is implemented. We tested the SDP prediction method on the LacI family of bacterial transcription factors and a sample of bacterial water and glycerol transporters belonging to the major intrinsic protein (MIP) family. In both cases, the comparison with available experimental and structural data strongly supported our predictions.

Mesh:

Substances:

Year:  2004        PMID: 14739328      PMCID: PMC2286703          DOI: 10.1110/ps.03191704

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  28 in total

1.  Analysis and prediction of functional sub-types from protein sequence alignments.

Authors:  S S Hannenhalli; R B Russell
Journal:  J Mol Biol       Date:  2000-10-13       Impact factor: 5.469

2.  Role of residue 147 in the gene regulatory function of the Escherichia coli purine repressor.

Authors:  Joy L Huffman; Fu Lu; Howard Zalkin; Richard G Brennan
Journal:  Biochemistry       Date:  2002-01-15       Impact factor: 3.162

3.  A phylogenetic framework for the aquaporin family in eukaryotes.

Authors:  R Zardoya; S Villalba
Journal:  J Mol Evol       Date:  2001-05       Impact factor: 2.395

Review 4.  Predicting functional divergence in protein evolution by site-specific rate shifts.

Authors:  Eric A Gaucher; Xun Gu; Michael M Miyamoto; Steven A Benner
Journal:  Trends Biochem Sci       Date:  2002-06       Impact factor: 13.807

5.  Functional characterization of a microbial aquaglyceroporin.

Authors:  Alexandrine Froger; Jean-Paul Rolland; Patrick Bron; Valérie Lagrée; Françoise Le Cahérec; Stéphane Deschamps; Jean-François Hubert; Isabelle Pellerin; Daniel Thomas; Christian Delamarche
Journal:  Microbiology (Reading)       Date:  2001-05       Impact factor: 2.777

6.  Predicting ligand-binding function in families of bacterial receptors.

Authors:  J M Johnson; G M Church
Journal:  Proc Natl Acad Sci U S A       Date:  2000-04-11       Impact factor: 11.205

7.  The role of lysine 55 in determining the specificity of the purine repressor for its operators through minor groove interactions.

Authors:  A Glasfeld; A N Koehler; M A Schumacher; R G Brennan
Journal:  J Mol Biol       Date:  1999-08-13       Impact factor: 5.469

8.  Structural basis of water-specific transport through the AQP1 water channel.

Authors:  H Sui; B G Han; J K Lee; P Walian; B K Jap
Journal:  Nature       Date:  2001 Dec 20-27       Impact factor: 49.962

9.  Structural determinants of water permeation through aquaporin-1.

Authors:  K Murata; K Mitsuoka; T Hirai; T Walz; P Agre; J B Heymann; A Engel; Y Fujiyoshi
Journal:  Nature       Date:  2000-10-05       Impact factor: 49.962

10.  Structure of a glycerol-conducting channel and the basis for its selectivity.

Authors:  D Fu; A Libson; L J Miercke; C Weitzman; P Nollert; J Krucinski; R M Stroud
Journal:  Science       Date:  2000-10-20       Impact factor: 47.728

View more
  57 in total

1.  SDPpred: a tool for prediction of amino acid residues that determine differences in functional specificity of homologous proteins.

Authors:  Olga V Kalinina; Pavel S Novichkov; Andrey A Mironov; Mikhail S Gelfand; Aleksandra B Rakhmaninova
Journal:  Nucleic Acids Res       Date:  2004-07-01       Impact factor: 16.971

2.  Surveying the manifold divergence of an entire protein class for statistical clues to underlying biochemical mechanisms.

Authors:  Andrew F Neuwald
Journal:  Stat Appl Genet Mol Biol       Date:  2011-08-04

3.  Rod and cone opsin families differ in spectral tuning domains but not signal transducing domains as judged by saturated evolutionary trace analysis.

Authors:  Karen L Carleton; Tyrone C Spady; Rick H Cote
Journal:  J Mol Evol       Date:  2005-06-16       Impact factor: 2.395

4.  LigProf: a simple tool for in silico prediction of ligand-binding sites.

Authors:  Grzegorz Koczyk; Lucjan S Wyrwicz; Leszek Rychlewski
Journal:  J Mol Model       Date:  2007-01-03       Impact factor: 1.810

5.  Identification of functional paralog shift mutations: conversion of Escherichia coli malate dehydrogenase to a lactate dehydrogenase.

Authors:  Yifeng Yin; Jack F Kirsch
Journal:  Proc Natl Acad Sci U S A       Date:  2007-10-18       Impact factor: 11.205

6.  Module evolution and substrate specificity of fungal nonribosomal peptide synthetases involved in siderophore biosynthesis.

Authors:  Kathryn E Bushley; Daniel R Ripoll; B Gillian Turgeon
Journal:  BMC Evol Biol       Date:  2008-12-03       Impact factor: 3.260

7.  Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure.

Authors:  John A Capra; Roman A Laskowski; Janet M Thornton; Mona Singh; Thomas A Funkhouser
Journal:  PLoS Comput Biol       Date:  2009-12-04       Impact factor: 4.475

8.  INTREPID: a web server for prediction of functionally important residues by evolutionary analysis.

Authors:  Sriram Sankararaman; Bryan Kolaczkowski; Kimmen Sjölander
Journal:  Nucleic Acids Res       Date:  2009-05-13       Impact factor: 16.971

9.  Ensemble approach to predict specificity determinants: benchmarking and validation.

Authors:  Saikat Chakrabarti; Anna R Panchenko
Journal:  BMC Bioinformatics       Date:  2009-07-02       Impact factor: 3.169

10.  Characterization and prediction of residues determining protein functional specificity.

Authors:  John A Capra; Mona Singh
Journal:  Bioinformatics       Date:  2008-05-01       Impact factor: 6.937

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.