Literature DB >> 12912834

Probabilistic scoring measures for profile-profile comparison yield more accurate short seed alignments.

David Mittelman1, Ruslan Sadreyev, Nick Grishin.   

Abstract

MOTIVATION: The development of powerful automatic methods for the comparison of protein sequences has become increasingly important. Profile-to-profile comparisons allow for the use of broader information about protein families, resulting in more sensitive and accurate comparisons of distantly related sequences. A key part in the comparison of two profiles is the method for the calculation of scores for the position matches. A number of methods based on various theoretical considerations have been proposed. We implemented several previously reported scoring functions as well as our own functions, and compared them on the basis of their ability to produce accurate short ungapped alignments of a given length.
RESULTS: Our results suggest that the family of the probabilistic methods (log-odds based methods and prof_sim) may be the more appropriate choice for the generation of initial 'seeds' as the first step to produce local profile-profile alignments. The most effective scoring systems were the closely related modifications of functions previously implemented in the COMPASS and Picasso methods.

Mesh:

Year:  2003        PMID: 12912834     DOI: 10.1093/bioinformatics/btg185

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  23 in total

1.  Scoring profile-to-profile sequence alignments.

Authors:  Guoli Wang; Roland L Dunbrack
Journal:  Protein Sci       Date:  2004-06       Impact factor: 6.725

2.  An assessment of substitution scores for protein profile-profile comparison.

Authors:  Xugang Ye; Guoli Wang; Stephen F Altschul
Journal:  Bioinformatics       Date:  2011-10-13       Impact factor: 6.937

3.  Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments.

Authors:  Hongyi Zhou; Yaoqi Zhou
Journal:  Proteins       Date:  2005-02-01

4.  Detecting remotely related proteins by their interactions and sequence similarity.

Authors:  Jordi Espadaler; Ramón Aragüés; Narayanan Eswar; Marc A Marti-Renom; Enrique Querol; Francesc X Avilés; Andrej Sali; Baldomero Oliva
Journal:  Proc Natl Acad Sci U S A       Date:  2005-05-09       Impact factor: 11.205

Review 5.  Advances in homology protein structure modeling.

Authors:  Zhexin Xiang
Journal:  Curr Protein Pept Sci       Date:  2006-06       Impact factor: 3.272

6.  A new prediction strategy for long local protein structures using an original description.

Authors:  Aurélie Bornot; Catherine Etchebest; Alexandre G de Brevern
Journal:  Proteins       Date:  2009-08-15

7.  ModLink+: improving fold recognition by using protein-protein interactions.

Authors:  Oriol Fornes; Ramon Aragues; Jordi Espadaler; Marc A Marti-Renom; Andrej Sali; Baldo Oliva
Journal:  Bioinformatics       Date:  2009-04-08       Impact factor: 6.937

8.  Protein structure prediction by pro-Sp3-TASSER.

Authors:  Hongyi Zhou; Jeffrey Skolnick
Journal:  Biophys J       Date:  2009-03-18       Impact factor: 4.033

9.  LIBRUS: combined machine learning and homology information for sequence-based ligand-binding residue prediction.

Authors:  Chris Kauffman; George Karypis
Journal:  Bioinformatics       Date:  2009-09-28       Impact factor: 6.937

10.  A model for protein sequence evolution based on selective pressure for protein stability: application to hemoglobins.

Authors:  Lorraine Marsh
Journal:  Evol Bioinform Online       Date:  2009-08-27       Impact factor: 1.625

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