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Licensed Unlicensed Requires Authentication Published by De Gruyter July 2, 2014

Protein domain hierarchy Gibbs sampling strategies

Andrew F. Neuwald

Abstract

Hierarchically-arranged multiple sequence alignment profiles are useful for modeling protein domains that have functionally diverged into evolutionarily-related subgroups. Currently such alignment hierarchies are largely constructed through manual curation, as for the NCBI Conserved Domain Database (CDD). Recently, however, I developed a Gibbs sampler that uses an approach termed statistical evolutionary dynamics analysis to accomplish this task automatically while, at the same time, identifying sequence determinants of protein function. Here I describe the statistical model and sampling strategies underlying this sampler. When implemented and applied to simulated protein sequences (which conform to the underlying statistical model precisely), these sampling strategies efficiently converge on the hierarchy used to generate the sequences. However, for real protein sequences the sampler finds alternative, nearly-optimal hierarchies for many domains, indicating a significant degree of ambiguity. I illustrate how both the nature of such ambiguities and the most robust (“consensus”) features of a hierarchy may be determined from an ensemble of independently generated hierarchies for the same domain. Such consensus hierarchies can provide reliably stable models of protein domain functional divergence.


Corresponding author: Andrew F. Neuwald, Institute for Genome Sciences and Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, BioPark II, Room 617, 801 West Baltimore St., Baltimore, MD 21201, USA, e-mail:

Acknowledgments

This work was supported by the School of Medicine at the University of Maryland, Baltimore and by a contract from the NIH (HHSN263000099957I). I thank John L. Spouge for critical reading of the manuscript.

Disclosure statement:

No competing financial interests exist.

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Supplemental Material

The online version of this article (DOI: 10.1515/sagmb-2014-0008) offers supplementary material, available to authorized users.


Published Online: 2014-7-2
Published in Print: 2014-8-1

© 2014 by De Gruyter

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