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Protein domain hierarchy Gibbs sampling strategies

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  • Neuwald Andrew F.

    (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)

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.

Suggested Citation

  • Neuwald Andrew F., 2014. "Protein domain hierarchy Gibbs sampling strategies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(4), pages 497-517, August.
    • Handle: RePEc:bpj:sagmbi:v:13:y:2014:i:4:p:21:n:7
    DOI: 10.1515/sagmb-2014-0008
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    References listed on IDEAS

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    1. Peter D. Grünwald, 2007. "The Minimum Description Length Principle," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262072815, April.
    2. Neuwald Andrew F., 2011. "Surveying the Manifold Divergence of an Entire Protein Class for Statistical Clues to Underlying Biochemical Mechanisms," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-30, August.
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