IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v13y2014i4p21n7.html
   My bibliography  Save this article

Protein domain hierarchy Gibbs sampling strategies

Author

Listed:
  • 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 1-21, August.
    • Handle: RePEc:bpj:sagmbi:v:13:y:2014:i:4:p:21:n:7
    DOI: 10.1515/sagmb-2014-0008
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/sagmb-2014-0008
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/sagmb-2014-0008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Peter D. Grünwald, 2007. "The Minimum Description Length Principle," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262072815, December.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Das Ujjwal & Ebrahimi Nader, 2018. "A New Method For Covariate Selection In Cox Model," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 297-314, June.
    2. Zelaya Mendizábal, Valentina & Boullé, Marc & Rossi, Fabrice, 2023. "Fast and fully-automated histograms for large-scale data sets," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    3. Ujjwal Das & Nader Ebrahimi, 2018. "A New Method For Covariate Selection In Cox Model," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 297-314, June.
    4. Andrew F Neuwald & Stephen F Altschul, 2016. "Inference of Functionally-Relevant N-acetyltransferase Residues Based on Statistical Correlations," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-30, December.
    5. Yurij L. Katchanov & Natalia A. Shmatko, 2014. "Complexity-Based Modeling of Scientific Capital: An Outline of Mathematical Theory," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2014, pages 1-10, October.
    6. Kris V Parag & Christl A Donnelly, 2020. "Using information theory to optimise epidemic models for real-time prediction and estimation," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-20, July.
    7. Mullins, Joshua & Mahadevan, Sankaran, 2014. "Variable-fidelity model selection for stochastic simulation," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 40-52.
    8. K. Vela Velupillai, 2010. "The Algorithmic Revolution in the Social Sciences: Mathematical Economics, Game Theory and Statistical Inference," ASSRU Discussion Papers 1005, ASSRU - Algorithmic Social Science Research Unit.
    9. Andrew F Neuwald & Stephen F Altschul, 2016. "Bayesian Top-Down Protein Sequence Alignment with Inferred Position-Specific Gap Penalties," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-21, May.
    10. Alperen Bektas & Valentino Piana & René Schumann, 2021. "A meso-level empirical validation approach for agent-based computational economic models drawing on micro-data: a use case with a mobility mode-choice model," SN Business & Economics, Springer, vol. 1(6), pages 1-25, June.
    11. 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.
    12. Löcherbach, Eva & Orlandi, Enza, 2011. "Neighborhood radius estimation for variable-neighborhood random fields," Stochastic Processes and their Applications, Elsevier, vol. 121(9), pages 2151-2185, September.
    13. Vittoria Bruni & Michela Tartaglione & Domenico Vitulano, 2020. "A Signal Complexity-Based Approach for AM–FM Signal Modes Counting," Mathematics, MDPI, vol. 8(12), pages 1-33, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:sagmbi:v:13:y:2014:i:4:p:21:n:7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.