Gapped alignment of protein sequence motifs through Monte Carlo optimization of a hidden Markov model

Neuwald, A. F., Liu, J. S. (October 2004) Gapped alignment of protein sequence motifs through Monte Carlo optimization of a hidden Markov model. BMC Bioinformatics, 5. p. 157. ISSN 1471-2105 (Electronic)

[img]
Preview
PDF (Paper)
Neuwald BMC Bioinformatics 2005.pdf - Published Version

Download (6Mb) | Preview
URL: http://www.pubmedcentral.nih.gov/articlerender.fcg...
DOI: 10.1186/1471-2105-5-157

Abstract

BACKGROUND: Certain protein families are highly conserved across distantly related organisms and belong to large and functionally diverse superfamilies. The patterns of conservation present in these protein sequences presumably are due to selective constraints maintaining important but unknown structural mechanisms with some constraints specific to each family and others shared by a larger subset or by the entire superfamily. To exploit these patterns as a source of functional information, we recently devised a statistically based approach called contrast hierarchical alignment and interaction network (CHAIN) analysis, which infers the strengths of various categories of selective constraints from co-conserved patterns in a multiple alignment. The power of this approach strongly depends on the quality of the multiple alignments, which thus motivated development of theoretical concepts and strategies to improve alignment of conserved motifs within large sets of distantly related sequences. RESULTS: Here we describe a hidden Markov model (HMM), an algebraic system, and Markov chain Monte Carlo (MCMC) sampling strategies for alignment of multiple sequence motifs. The MCMC sampling strategies are useful both for alignment optimization and for adjusting position specific background amino acid frequencies for alignment uncertainties. Associated statistical formulations provide an objective measure of alignment quality as well as automatic gap penalty optimization. Improved alignments obtained in this way are compared with PSI-BLAST based alignments within the context of CHAIN analysis of three protein families: Gialpha subunits, prolyl oligopeptidases, and transitional endoplasmic reticulum (p97) AAA+ ATPases. CONCLUSION: While not entirely replacing PSI-BLAST based alignments, which likewise may be optimized for CHAIN analysis using this approach, these motif-based methods often more accurately align very distantly related sequences and thus can provide a better measure of selective constraints. In some instances, these new approaches also provide a better understanding of family-specific constraints, as we illustrate for p97 ATPases. Programs implementing these procedures and supplementary information are available from the authors.

Item Type: Paper
Uncontrolled Keywords: Amino Acid Motifs Amino Acid Sequence Animals Archaeal Proteins chemistry Bacterial Proteins chemistry Conserved Sequence GTP Phosphohydrolases chemistry Helminth Proteins chemistry Hydrolases chemistry Markov Chains Molecular Sequence Data Monte Carlo Method Protein Folding Protein Structure Tertiary Sequence Alignment methods statistics & numerical data Sequence Analysis Protein methods statistics & numerical data Serine Endopeptidases chemistry Software statistics & numerical data
Subjects: bioinformatics > genomics and proteomics > genetics & nucleic acid processing > protein structure, function, modification
bioinformatics > quantitative biology
bioinformatics > genomics and proteomics > annotation > sequence annotation
bioinformatics > genomics and proteomics > analysis and processing > Sequence Data Processing
bioinformatics > genomics and proteomics > Mapping and Rendering > Sequence Rendering
CSHL Authors:
Depositing User: CSHL Librarian
Date: 25 October 2004
Date Deposited: 31 Jan 2012 16:43
Last Modified: 03 Nov 2017 16:22
PMCID: PMC538276
URI: http://repository.cshl.edu/id/eprint/22449

Actions (login required)

Administrator's edit/view item Administrator's edit/view item
CSHL HomeAbout CSHLResearchEducationNews & FeaturesCampus & Public EventsCareersGiving