Literature DB >> 15919194

The limits of protein sequence comparison?

William R Pearson1, Michael L Sierk.   

Abstract

Modern sequence alignment algorithms are used routinely to identify homologous proteins, proteins that share a common ancestor. Homologous proteins always share similar structures and often have similar functions. Over the past 20 years, sequence comparison has become both more sensitive, largely because of profile-based methods, and more reliable, because of more accurate statistical estimates. As sequence and structure databases become larger, and comparison methods become more powerful, reliable statistical estimates will become even more important for distinguishing similarities that are due to homology from those that are due to analogy (convergence). The newest sequence alignment methods are more sensitive than older methods, but more accurate statistical estimates are needed for their full power to be realized.

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Year:  2005        PMID: 15919194      PMCID: PMC2845305          DOI: 10.1016/j.sbi.2005.05.005

Source DB:  PubMed          Journal:  Curr Opin Struct Biol        ISSN: 0959-440X            Impact factor:   6.809


  53 in total

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Authors:  Yi-Kuo Yu; John C Wootton; Stephen F Altschul
Journal:  Proc Natl Acad Sci U S A       Date:  2003-12-08       Impact factor: 11.205

2.  A comparison of profile hidden Markov model procedures for remote homology detection.

Authors:  Martin Madera; Julian Gough
Journal:  Nucleic Acids Res       Date:  2002-10-01       Impact factor: 16.971

3.  Sequence comparisons using multiple sequences detect three times as many remote homologues as pairwise methods.

Authors:  J Park; K Karplus; C Barrett; R Hughey; D Haussler; T Hubbard; C Chothia
Journal:  J Mol Biol       Date:  1998-12-11       Impact factor: 5.469

4.  Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships.

Authors:  S E Brenner; C Chothia; T J Hubbard
Journal:  Proc Natl Acad Sci U S A       Date:  1998-05-26       Impact factor: 11.205

5.  A unified statistical framework for sequence comparison and structure comparison.

Authors:  M Levitt; M Gerstein
Journal:  Proc Natl Acad Sci U S A       Date:  1998-05-26       Impact factor: 11.205

6.  Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes.

Authors:  S Karlin; S F Altschul
Journal:  Proc Natl Acad Sci U S A       Date:  1990-03       Impact factor: 11.205

7.  Improved tools for biological sequence comparison.

Authors:  W R Pearson; D J Lipman
Journal:  Proc Natl Acad Sci U S A       Date:  1988-04       Impact factor: 11.205

8.  Profile analysis: detection of distantly related proteins.

Authors:  M Gribskov; A D McLachlan; D Eisenberg
Journal:  Proc Natl Acad Sci U S A       Date:  1987-07       Impact factor: 11.205

9.  Hidden Markov models in computational biology. Applications to protein modeling.

Authors:  A Krogh; M Brown; I S Mian; K Sjölander; D Haussler
Journal:  J Mol Biol       Date:  1994-02-04       Impact factor: 5.469

10.  Profile-profile comparisons by COMPASS predict intricate homologies between protein families.

Authors:  Ruslan I Sadreyev; David Baker; Nick V Grishin
Journal:  Protein Sci       Date:  2003-10       Impact factor: 6.725

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  31 in total

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Authors:  Ying A Wang; Xiong Yu; Sandy Y M Ng; Ken F Jarrell; Edward H Egelman
Journal:  J Mol Biol       Date:  2008-06-12       Impact factor: 5.469

2.  Novel protein folds and their nonsequential structural analogs.

Authors:  Aysam Guerler; Ernst-Walter Knapp
Journal:  Protein Sci       Date:  2008-06-26       Impact factor: 6.725

3.  Globally, unrelated protein sequences appear random.

Authors:  Daniel T Lavelle; William R Pearson
Journal:  Bioinformatics       Date:  2009-11-30       Impact factor: 6.937

Review 4.  Molecular Phylogenetics and the Perennial Problem of Homology.

Authors:  S Andrew Inkpen; W Ford Doolittle
Journal:  J Mol Evol       Date:  2016-11-21       Impact factor: 2.395

5.  Biophysical constraints on the evolution of tissue structure and function.

Authors:  P J Hunter; B de Bono
Journal:  J Physiol       Date:  2014-06-01       Impact factor: 5.182

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Journal:  Plant Physiol       Date:  2016-05       Impact factor: 8.340

7.  ProteinWorldDB: querying radical pairwise alignments among protein sets from complete genomes.

Authors:  Thomas Dan Otto; Marcos Catanho; Cristian Tristão; Márcia Bezerra; Renan Mathias Fernandes; Guilherme Steinberger Elias; Alexandre Capeletto Scaglia; Bill Bovermann; Viktors Berstis; Sergio Lifschitz; Antonio Basílio de Miranda; Wim Degrave
Journal:  Bioinformatics       Date:  2010-01-19       Impact factor: 6.937

8.  Threshold Average Precision (TAP-k): a measure of retrieval designed for bioinformatics.

Authors:  Hyrum D Carroll; Maricel G Kann; Sergey L Sheetlin; John L Spouge
Journal:  Bioinformatics       Date:  2010-05-26       Impact factor: 6.937

9.  A combinatorial approach to detect coevolved amino acid networks in protein families of variable divergence.

Authors:  Julie Baussand; Alessandra Carbone
Journal:  PLoS Comput Biol       Date:  2009-09-04       Impact factor: 4.475

10.  Homologous over-extension: a challenge for iterative similarity searches.

Authors:  Mileidy W Gonzalez; William R Pearson
Journal:  Nucleic Acids Res       Date:  2010-01-11       Impact factor: 16.971

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